National Library of Energy BETA

Sample records for xls files graph

  1. Archived Weekly Files, Revised, 1984 Forward EIA revises its...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    XLS XLS 1990 XLS XLS 1989 XLS XLS 1988 XLS XLS 1987 XLS XLS 1986 XLS XLS 1985 XLS XLS 1984 XLS XLS Original estimates* year weekly monthly 2015 XLS XLS 2014 XLS XLS 2013 XLS XLS...

  2. OMBDOEFAIR2005.xls | Department of Energy

    Energy Savers [EERE]

    OMBDOEFAIR2005.xls&0; OMBDOEFAIR2005.xls&0; More Documents & Publications 2003 DOE IGCA Inventory Data for web.xls&0; 3REV2004DOEFAIR.xls&0; N:My Documentsporfin.pdf...

  3. hud_doe_supplemental_list_of_eligible_properties_list_1.xls ...

    Broader source: Energy.gov (indexed) [DOE]

    xls More Documents & Publications huddoesupplementallistofeligiblepropertieslist1.xls rdmfhlowandverylow...

  4. hud_doe_supplemental_list_of_eligible_properties_list_2.xls ...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    2.xls huddoesupplementallistofeligiblepropertieslist2.xls huddoesupplementallistofeligiblepropertieslist2.xls More Documents & Publications huddoesupplementallis...

  5. Grantsdown.xls | Department of Energy

    Office of Environmental Management (EM)

    Grantsdown.xls Grantsdown.xls Grantsdown.xls More Documents & Publications Class Patent Waiver W(C)2012-001 Amendment No. 1 (August 5, 2010) FOA 148 Amendment...

  6. Recursive Feature Extraction in Graphs

    Energy Science and Technology Software Center (OSTI)

    2014-08-14

    ReFeX extracts recursive topological features from graph data. The input is a graph as a csv file and the output is a csv file containing feature values for each node in the graph. The features are based on topological counts in the neighborhoods of each nodes, as well as recursive summaries of neighbors' features.

  7. Annual Electric Generator data - EIA-860 data file

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    ... date, energy source, heat content, nameplate capacity, summer and winter capability, etc. 860-A (Utility) Data are compressed into a zip file that expands into xls data files and a ...

  8. rd_mfh_low_and_very_low.xls | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    mfhlowandverylow.xls More Documents & Publications list2eligiblemultifamilybuildings10-cfr-440-22b4ii.xls hudlist-107-01-11.xls hudlist-107-01-11.xls...

  9. Pressure Data Within BOP- XLS

    Broader source: Energy.gov [DOE]

    This file describes the components within the BOP and the pressure readings taken during diagnostic operations on May 25.

  10. 2003 DOE IGCA Inventory Data for web.xls | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    3 DOE IGCA Inventory Data for web.xls&0; 2003 DOE IGCA Inventory Data for web.xls&0; 2003 DOE IGCA Inventory Data for web.xls&0; (570.91 KB) More Documents & Publications ...

  11. hud_list-1_07-01-11.xls | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    list-107-01-11.xls More Documents & Publications hudlist-107-01-11.xls list2eligiblemultifamilybuildings10-cfr-440-22b4ii.xls rdmfhlowandverylow...

  12. FILE

    Office of Legacy Management (LM)

    c&o--L>+/ co.o-09 ~~Epq-J+~~jDU" 1 . [o( / 5-/-- 1, "I ' -i, [ . - -. j s: FILE :3r.jNER is} -------.- pas',: Current: -----__---_---__-------- ----____-_________________ Cwner ccntacted 0 yes 0 nag i+ yet,? date czntacted ----___--I__- Ty-;Pfz ,' F iTiC~CC2~j 1 iljbj AA-r-ti--=' ="L---- /8;' ; z.eseaf-ch & sevei apment EJ F' raducticx scale tssting rJ Pilot si-jle 0 ' jench Scale Fracess 0 Theoretical Studies 0 Samp:! e & haivsis 0 Fz-citity Tvpe 0 Manui artur i ng

  13. Graph Theory

    SciTech Connect (OSTI)

    Sanfilippo, Antonio P.

    2005-12-27

    Graph theory is a branch of discrete combinatorial mathematics that studies the properties of graphs. The theory was pioneered by the Swiss mathematician Leonhard Euler in the 18th century, commenced its formal development during the second half of the 19th century, and has witnessed substantial growth during the last seventy years, with applications in areas as diverse as engineering, computer science, physics, sociology, chemistry and biology. Graph theory has also had a strong impact in computational linguistics by providing the foundations for the theory of features structures that has emerged as one of the most widely used frameworks for the representation of grammar formalisms.

  14. Utilization Graphs

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    that use data from the PDSF batch scheduler (SGE) to show the utilization of the cluster over the past 24 hours. The graphs were generated with RRDTool and are updated...

  15. Cell Total Activity Final Estimate.xls

    Office of Legacy Management (LM)

    WSSRAP Cell Total Activity Final Estimate (calculated September 2002, Fleming) (Waste streams & occupied cell volumes from spreadsheet titled "cell waste volumes-8.23.02 with macros.xls") Waste Stream a Volume (cy) Mass (g) 2 Radiological Profile 3 Nuclide Activity (Ci) 4 Total % of Total U-238 U-234 U-235 Th-228 Th-230 Th-232 Ra-226 Ra-228 Rn-222 5 Activity if > 1% Raffinate Pits Work Zone (Ci) Raffinate processed through CSS Plant 1 159990 1.49E+11 Raffinate 6.12E+01 6.12E+01

  16. 3REV2004DOEFAIR.xls | Department of Energy

    Office of Environmental Management (EM)

    More Documents & Publications N:My Documentsporfin.pdf&0; 2003 DOE IGCA Inventory Data for web.xls&0; 2002 DOE Final Inherently Governmental and Commercial Activities Inventory

  17. list2_eligible_multifamily_buildings_10-cfr-440-22b4ii.xls |...

    Energy Savers [EERE]

    list2eligiblemultifamilybuildings10-cfr-440-22b4ii.xls list2eligiblemultifamilybuildings10-cfr-440-22b4ii.xls Office spreadsheet icon list2eligiblemultifamilybuildings1...

  18. FY 2007 Operating Plan for DOE--March 16, 2007.xls | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    FY 2007 Operating Plan for DOE--March 16, 2007.xls U.S Department of Energy 2007 operating plan by appropriation. PDF icon FY 2007 Operating Plan for DOE--March 16, 2007.xls More ...

  19. FINAL Combined SGIG Selections - By State for Press -5.xls | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Energy FINAL Combined SGIG Selections - By State for Press -5.xls FINAL Combined SGIG Selections - By State for Press -5.xls FINAL Combined SGIG Selections - By State for Press -5.xls (161.6 KB) More Documents & Publications Recovery Act Selections for Smart Grid Invesment Grant Awards- By Category Updated July 2010 FINAL Combined SGIG Selections - By Category for Press -AOv10.xls Recovery Act Selections for Smart Grid Investment Grant Awards - By State - Updated November 2011

  20. The MultiThreaded Graph Library (MTGL)

    Energy Science and Technology Software Center (OSTI)

    2008-07-17

    The MultiThreaded Graph Library (MTGL) is a set of header files that implement graph algorithm in such a way that they can run on massively multithreaded architectures. It is based upon the Boost Graph Library, but doesn’t use Boost since the latter doesn’t run well on these architectures.

  1. Final FY 2009 NEUP RD Awards (2).xls | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Final FY 2009 NEUP RD Awards (2).xls Final FY 2009 NEUP RD Awards (2).xls Final FY 2009 NEUP RD Awards (2).xls (32.32 KB) More Documents & Publications NEET Awards for FY2012 Meeting Materials: June 9, 2009 EA-1775: Final Environmental Assessment

  2. Attachment 5 Volume II Pricing Matrix.xls | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    Attachment 5 Volume II Pricing Matrix.xls&0; More Documents & Publications Microsoft Word - FY07AnnualReport.doc CX-005455: Categorical Exclusion Determination Microsoft Word -...

  3. Copy of FINAL SG Demo Project List 11 13 09-External.xls | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    of FINAL SG Demo Project List 11 13 09-External.xls More Documents & Publications Smart Grid Regional and Energy Storage Demonstration Projects: Awards Energy Storage Activities...

  4. Methods of visualizing graphs

    DOE Patents [OSTI]

    Wong, Pak C.; Mackey, Patrick S.; Perrine, Kenneth A.; Foote, Harlan P.; Thomas, James J.

    2008-12-23

    Methods for visualizing a graph by automatically drawing elements of the graph as labels are disclosed. In one embodiment, the method comprises receiving node information and edge information from an input device and/or communication interface, constructing a graph layout based at least in part on that information, wherein the edges are automatically drawn as labels, and displaying the graph on a display device according to the graph layout. In some embodiments, the nodes are automatically drawn as labels instead of, or in addition to, the label-edges.

  5. supplemental_lists_1d-2d-3c_06-24-2011.xls | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    lists1d-2d-3c06-24-2011.xls supplementallists1d-2d-3c06-24-2011.xls Office spreadsheet icon supplementallists1d-2d-3c06-24-2011.xls More Documents & Publications...

  6. mpiGraph

    Energy Science and Technology Software Center (OSTI)

    2007-05-22

    MpiGraph consists of an MPI application called mpiGraph written in C to measure message bandwidth and an associated crunch_mpiGraph script written in Perl to process the application output into an HTMO report. The mpiGraph application is designed to inspect the health and scalability of a high-performance interconnect while under heavy load. This is useful to detect hardware and software problems in a system, such as slow nodes, links, switches, or contention in switch routing. Itmore » is also useful to characterize how interconnect performance changes with different settings or how one interconnect type compares to another.« less

  7. Graph Generator Survey

    SciTech Connect (OSTI)

    Lothian, Josh; Powers, Sarah S; Sullivan, Blair D; Baker, Matthew B; Schrock, Jonathan; Poole, Stephen W

    2013-12-01

    The benchmarking effort within the Extreme Scale Systems Center at Oak Ridge National Laboratory seeks to provide High Performance Computing benchmarks and test suites of interest to the DoD sponsor. The work described in this report is a part of the effort focusing on graph generation. A previously developed benchmark, SystemBurn, allowed the emulation of dierent application behavior profiles within a single framework. To complement this effort, similar capabilities are desired for graph-centric problems. This report examines existing synthetic graph generator implementations in preparation for further study on the properties of their generated synthetic graphs.

  8. Graphs, matrices, and the GraphBLAS: Seven good reasons

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Kepner, Jeremy; Bader, David; Buluç, Aydın; Gilbert, John; Mattson, Timothy; Meyerhenke, Henning

    2015-01-01

    The analysis of graphs has become increasingly important to a wide range of applications. Graph analysis presents a number of unique challenges in the areas of (1) software complexity, (2) data complexity, (3) security, (4) mathematical complexity, (5) theoretical analysis, (6) serial performance, and (7) parallel performance. Implementing graph algorithms using matrix-based approaches provides a number of promising solutions to these challenges. The GraphBLAS standard (istcbigdata.org/GraphBlas) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. The GraphBLAS mathematically defines a core set of matrix-based graph operations that can be used to implementmore » a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the GraphBLAS and describes how the GraphBLAS can be used to address many of the challenges associated with analysis of graphs.« less

  9. Graphs, matrices, and the GraphBLAS: Seven good reasons

    SciTech Connect (OSTI)

    Kepner, Jeremy; Bader, David; Buluç, Aydın; Gilbert, John; Mattson, Timothy; Meyerhenke, Henning

    2015-01-01

    The analysis of graphs has become increasingly important to a wide range of applications. Graph analysis presents a number of unique challenges in the areas of (1) software complexity, (2) data complexity, (3) security, (4) mathematical complexity, (5) theoretical analysis, (6) serial performance, and (7) parallel performance. Implementing graph algorithms using matrix-based approaches provides a number of promising solutions to these challenges. The GraphBLAS standard (istcbigdata.org/GraphBlas) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. The GraphBLAS mathematically defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the GraphBLAS and describes how the GraphBLAS can be used to address many of the challenges associated with analysis of graphs.

  10. 2011 Cost Symposium Agenda 4-28-11 web draft.xls | Department...

    Broader source: Energy.gov (indexed) [DOE]

    Cost Symposium Agenda 4-28-11 web draft.xls (17.46 KB) More Documents & Publications 2011 Cost Symposium Agenda for web (2)-OPAM 2011 Workshop AgendaVer9

  11. Subdominant pseudoultrametric on graphs

    SciTech Connect (OSTI)

    Dovgoshei, A A; Petrov, E A

    2013-08-31

    Let (G,w) be a weighted graph. We find necessary and sufficient conditions under which the weight w:E(G)?R{sup +} can be extended to a pseudoultrametric on V(G), and establish a criterion for the uniqueness of such an extension. We demonstrate that (G,w) is a complete k-partite graph, for k?2, if and only if for any weight that can be extended to a pseudoultrametric, among all such extensions one can find the least pseudoultrametric consistent with w. We give a structural characterization of graphs for which the subdominant pseudoultrametric is an ultrametric for any strictly positive weight that can be extended to a pseudoultrametric. Bibliography: 14 titles.

  12. Original Signatures on File

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Signatures on File

  13. Original Signatures on File

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Original Signatures on File

  14. Role Discovery in Graphs

    Energy Science and Technology Software Center (OSTI)

    2014-08-14

    RolX takes the features from Re-FeX or any other feature matrix as input and outputs role assignments (clusters). The output of RolX is a csv file containing the node-role memberships and a csv file containing the role-feature definitions.

  15. File Storage

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    File Storage File Storage Disk Quota Change Request Form Carver File Systems Carver has 3 kinds of file systems available to users: home directories, scratch directories and project directories, all provided by the NERSC Global File system. Each file system serves a different purpose. File System Home Scratch Project Environment Variable Definition $HOME $SCRATCH or $GSCRATCH No environment variable /project/projectdirs/ Description Global homes file system shared by all NERSC systems except

  16. File storage

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    File storage File storage Disk Quota Change Request Form Euclid File Systems Euclid has 3 kinds of file systems available to users: home directories, scratch directories and project directories, all provided by the NERSC Global File system. Each file system serves a different purpose. File System Home Scratch Project Environment Variable Definition $HOME $SCRATCH or $GSCRATCH No environment variable /project/projectdirs/ Description Global homes file system shared by all NERSC systems except

  17. GraphLib

    Energy Science and Technology Software Center (OSTI)

    2013-02-19

    This library is used in several LLNL projects, including STAT (the Stack Trace Analysis Tool for scalable debugging) and some modules in P^nMPI (a tool MPI tool infrastructure). It can also be used standalone for creating and manipulationg graphs, but its API is primarily tuned to support these other projects

  18. Simple and Flexible Scene Graph

    Energy Science and Technology Software Center (OSTI)

    2007-10-01

    The system implements a flexible and extensible scene graph for the visualization and analysis of scientific information.

  19. Temporal Representation in Semantic Graphs

    SciTech Connect (OSTI)

    Levandoski, J J; Abdulla, G M

    2007-08-07

    A wide range of knowledge discovery and analysis applications, ranging from business to biological, make use of semantic graphs when modeling relationships and concepts. Most of the semantic graphs used in these applications are assumed to be static pieces of information, meaning temporal evolution of concepts and relationships are not taken into account. Guided by the need for more advanced semantic graph queries involving temporal concepts, this paper surveys the existing work involving temporal representations in semantic graphs.

  20. A Clustering Graph Generator

    SciTech Connect (OSTI)

    Winlaw, Manda; De Sterck, Hans; Sanders, Geoffrey

    2015-10-26

    In very simple terms a network can be de ned as a collection of points joined together by lines. Thus, networks can be used to represent connections between entities in a wide variety of elds including engi- neering, science, medicine, and sociology. Many large real-world networks share a surprising number of properties, leading to a strong interest in model development research and techniques for building synthetic networks have been developed, that capture these similarities and replicate real-world graphs. Modeling these real-world networks serves two purposes. First, building models that mimic the patterns and prop- erties of real networks helps to understand the implications of these patterns and helps determine which patterns are important. If we develop a generative process to synthesize real networks we can also examine which growth processes are plausible and which are not. Secondly, high-quality, large-scale network data is often not available, because of economic, legal, technological, or other obstacles [7]. Thus, there are many instances where the systems of interest cannot be represented by a single exemplar network. As one example, consider the eld of cybersecurity, where systems require testing across diverse threat scenarios and validation across diverse network structures. In these cases, where there is no single exemplar network, the systems must instead be modeled as a collection of networks in which the variation among them may be just as important as their common features. By developing processes to build synthetic models, so-called graph generators, we can build synthetic networks that capture both the essential features of a system and realistic variability. Then we can use such synthetic graphs to perform tasks such as simulations, analysis, and decision making. We can also use synthetic graphs to performance test graph analysis algorithms, including clustering algorithms and anomaly detection algorithms.

  1. Petroleum Supply Monthly September 2004

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Ranges in Inventory Graphs XLS HTML Entire . The entire report as a single file. PDF 1.2MB . . Front Matter . Petroleum Supply Monthly Cover Page, Preface, and Table of...

  2. Graph Coarsening for Path Finding in Cybersecurity Graphs

    SciTech Connect (OSTI)

    Hogan, Emilie A.; Johnson, John R.; Halappanavar, Mahantesh

    2013-01-01

    n the pass-the-hash attack, hackers repeatedly steal password hashes and move through a computer network with the goal of reaching a computer with high level administrative privileges. In this paper we apply graph coarsening in network graphs for the purpose of detecting hackers using this attack or assessing the risk level of the network's current state. We repeatedly take graph minors, which preserve the existence of paths in the graph, and take powers of the adjacency matrix to count the paths. This allows us to detect the existence of paths as well as find paths that have high risk of being used by adversaries.

  3. File Systems

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    File Systems File Systems For a general description of the different file systems available on PDSF please see Eliza File Systems and Other File Systems. Below is a summary of how ATLAS uses the various systems: /common In the past ATLAS used /common primarily for their software installations but with cvmfs (see below) this is no longer necessary. ATLAS users also have made personal directories under /common/atlas. However, this is not the intended use of /common, as described on Other File

  4. Signature on File

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Signature on File Signature on File 5/29/08

  5. Quantum Graph Analysis

    SciTech Connect (OSTI)

    Maunz, Peter Lukas Wilhelm; Sterk, Jonathan David; Lobser, Daniel; Parekh, Ojas D.; Ryan-Anderson, Ciaran

    2016-01-01

    In recent years, advanced network analytics have become increasingly important to na- tional security with applications ranging from cyber security to detection and disruption of ter- rorist networks. While classical computing solutions have received considerable investment, the development of quantum algorithms to address problems, such as data mining of attributed relational graphs, is a largely unexplored space. Recent theoretical work has shown that quan- tum algorithms for graph analysis can be more efficient than their classical counterparts. Here, we have implemented a trapped-ion-based two-qubit quantum information proces- sor to address these goals. Building on Sandia's microfabricated silicon surface ion traps, we have designed, realized and characterized a quantum information processor using the hyperfine qubits encoded in two 171 Yb + ions. We have implemented single qubit gates using resonant microwave radiation and have employed Gate set tomography (GST) to characterize the quan- tum process. For the first time, we were able to prove that the quantum process surpasses the fault tolerance thresholds of some quantum codes by demonstrating a diamond norm distance of less than 1 . 9 x 10 [?] 4 . We used Raman transitions in order to manipulate the trapped ions' motion and realize two-qubit gates. We characterized the implemented motion sensitive and insensitive single qubit processes and achieved a maximal process infidelity of 6 . 5 x 10 [?] 5 . We implemented the two-qubit gate proposed by Molmer and Sorensen and achieved a fidelity of more than 97 . 7%.

  6. TOTAL ARRA Homes Weatherized thru Q2 2010 8.19.10.xls | Department of

    Broader source: Energy.gov (indexed) [DOE]

    Energy TOTAL ARRA Homes Weatherized thru Q2 2010 8.19.10.xls (14.26 KB) More Documents & Publications U.S. Department of Energy Weatherization Assistance Program Homes Weatherized By State through 06/30/2010 (Calendar Year) Homes Weatherized by State March 2010 ARRA Homes Weatherized by Grantee

  7. A Collection of Features for Semantic Graphs

    SciTech Connect (OSTI)

    Eliassi-Rad, T; Fodor, I K; Gallagher, B

    2007-05-02

    Semantic graphs are commonly used to represent data from one or more data sources. Such graphs extend traditional graphs by imposing types on both nodes and links. This type information defines permissible links among specified nodes and can be represented as a graph commonly referred to as an ontology or schema graph. Figure 1 depicts an ontology graph for data from National Association of Securities Dealers. Each node type and link type may also have a list of attributes. To capture the increased complexity of semantic graphs, concepts derived for standard graphs have to be extended. This document explains briefly features commonly used to characterize graphs, and their extensions to semantic graphs. This document is divided into two sections. Section 2 contains the feature descriptions for static graphs. Section 3 extends the features for semantic graphs that vary over time.

  8. Graph Partitioning and Sequencing Software

    Energy Science and Technology Software Center (OSTI)

    1995-09-19

    Graph partitioning is a fundemental problem in many scientific contexts. CHACO2.0 is a software package designed to partition and sequence graphs. CHACO2.0 allows for recursive application of several methods for finding small edge separators in weighted graphs. These methods include inertial, spectral, Kernighan Lin and multilevel methods in addition to several simpler strategies. Each of these approaches can be used to partition the graph into two, four, or eight pieces at each level of recursion.more » In addition, the Kernighan Lin method can be used to improve partitions generated by any of the other algorithms. CHACO2.0 can also be used to address various graph sequencing problems, with applications to scientific computing, database design, gene sequencing and other problems.« less

  9. A Metadata-Rich File System

    SciTech Connect (OSTI)

    Ames, S; Gokhale, M B; Maltzahn, C

    2009-01-07

    Despite continual improvements in the performance and reliability of large scale file systems, the management of file system metadata has changed little in the past decade. The mismatch between the size and complexity of large scale data stores and their ability to organize and query their metadata has led to a de facto standard in which raw data is stored in traditional file systems, while related, application-specific metadata is stored in relational databases. This separation of data and metadata requires considerable effort to maintain consistency and can result in complex, slow, and inflexible system operation. To address these problems, we have developed the Quasar File System (QFS), a metadata-rich file system in which files, metadata, and file relationships are all first class objects. In contrast to hierarchical file systems and relational databases, QFS defines a graph data model composed of files and their relationships. QFS includes Quasar, an XPATH-extended query language for searching the file system. Results from our QFS prototype show the effectiveness of this approach. Compared to the defacto standard, the QFS prototype shows superior ingest performance and comparable query performance on user metadata-intensive operations and superior performance on normal file metadata operations.

  10. Khovanov homology of graph-links

    SciTech Connect (OSTI)

    Nikonov, Igor M

    2012-08-31

    Graph-links arise as the intersection graphs of turning chord diagrams of links. Speaking informally, graph-links provide a combinatorial description of links up to mutations. Many link invariants can be reformulated in the language of graph-links. Khovanov homology, a well-known and useful knot invariant, is defined for graph-links in this paper (in the case of the ground field of characteristic two). Bibliography: 14 titles.

  11. File Systems

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    File Systems File Systems For a general description of the different file systems available on PDSF please see Eliza File Systems and Other File Systems. Below is a summary of how ALICE uses the various systems: /common ALICE uses /common to build the software that supports its grid-based automated production work. This software includes AliRoot, Geant, AliEn, and XRootD. /eliza6, /eliza8, /eliza17 ALICE has space on 3 elizas: 16TB on /eliza6, 6TB on /eliza8 and 11TB on /eliza17. The space on

  12. File Systems

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    File Systems File Systems For a general description of the different file systems available on PDSF please see Eliza File Systems and Other File Systems. Below is a summary of how STAR uses the various systems: /common The STAR software is installed on /common. For 32sl44 it is under /common/star/star44 and for sl53 it is under /common/star/star53. In both cases the software consists primarily of a STAR-specific ROOT installation on which releases of the STAR libraries are built as shown on the

  13. Nevada Bureau of Mines and Geology Open-File Report 12-3: Data...

    Open Energy Info (EERE)

    2012 Jump to: navigation, search OpenEI Reference LibraryAdd to library Report: Nevada Bureau of Mines and Geology Open-File Report 12-3: Data Tables and graphs of geothermal power...

  14. FileSys.pptx

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Navigating NERSC File Systems May 3, 2011 David Turner NERSC User Services Group * Focus on user-writable file systems * Global file systems * Local file systems * Policies * ...

  15. New Developments in MadGraph/MadEvent

    SciTech Connect (OSTI)

    Alwall, Johan; Artoisenet, Pierre; de Visscher, Simon; Duhr, Claude; Frederix, Rikkert; Herquet, Michel; Mattelaer, Olivier; /IBA, Louvain-la-Neuve

    2011-11-08

    We here present some recent developments of MadGraph/MadEvent since the latest published version, 4.0. These developments include: Jet matching with Pythia parton showers for both Standard Model and Beyond the Standard Model processes, decay chain functionality, decay width calculation and decay simulation, process generation for the Grid, a package for calculation of quarkonium amplitudes, calculation of Matrix Element weights for experimental events, automatic dipole subtraction for next-to-leading order calculations, and an interface to FeynRules, a package for automatic calculation of Feynman rules and model files from the Lagrangian of any New Physics model.

  16. Graph Analytics for Signature Discovery

    SciTech Connect (OSTI)

    Hogan, Emilie A.; Johnson, John R.; Halappanavar, Mahantesh; Lo, Chaomei

    2013-06-01

    Within large amounts of seemingly unstructured data it can be diffcult to find signatures of events. In our work we transform unstructured data into a graph representation. By doing this we expose underlying structure in the data and can take advantage of existing graph analytics capabilities, as well as develop new capabilities. Currently we focus on applications in cybersecurity and communication domains. Within cybersecurity we aim to find signatures for perpetrators using the pass-the-hash attack, and in communications we look for emails or phone calls going up or down a chain of command. In both of these areas, and in many others, the signature we look for is a path with certain temporal properties. In this paper we discuss our methodology for finding these temporal paths within large graphs.

  17. Graph modeling systems and methods

    SciTech Connect (OSTI)

    Neergaard, Mike

    2015-10-13

    An apparatus and a method for vulnerability and reliability modeling are provided. The method generally includes constructing a graph model of a physical network using a computer, the graph model including a plurality of terminating vertices to represent nodes in the physical network, a plurality of edges to represent transmission paths in the physical network, and a non-terminating vertex to represent a non-nodal vulnerability along a transmission path in the physical network. The method additionally includes evaluating the vulnerability and reliability of the physical network using the constructed graph model, wherein the vulnerability and reliability evaluation includes a determination of whether each terminating and non-terminating vertex represents a critical point of failure. The method can be utilized to evaluate wide variety of networks, including power grid infrastructures, communication network topologies, and fluid distribution systems.

  18. To: File

    Office of Legacy Management (LM)

    In 1931 and 19Siz the Atomic Energy Commission He; and Safety Division participated in ... Information obtained from EPFI files indicates that the facilit continued operations ...

  19. Dr.L: Distributed Recursive (Graph) Layout

    Energy Science and Technology Software Center (OSTI)

    2007-11-19

    Dr. L provides two-dimensional visualizations of very large abstract graph structures. it can be used for data mining applications including biology, scientific literature, and social network analysis. Dr. L is a graph layout program that uses a multilevel force-directed algorithm. A graph is input and drawn using a force-directed algorithm based on simulated annealing. The resulting layout is clustered using a single link algorithm. This clustering is used to produce a coarsened graph (fewer nodes)more » which is then re-drawn. this process is repeated until a sufficiently small graph is produced. The smallest graph is drawn and then used as a basis for drawing the original graph by refining the series of coarsened graphs that were produced. The layout engine can be run in serial or in parallel.« less

  20. 1987 RECS Public Use Microdata Files

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    cvs file File 3: Type of Energy and Equipment text file cvs file File 4: Household Demographics text file cvs file File 5: Presence of Appliances text file cvs file File 6: Energy...

  1. TO: FILE

    Office of Legacy Management (LM)

    Homer Watson, Wright Paterson Air Force Base, as a starting point. AWmb ' I. .' ..x; . .:yx . . . . : File a.%-I. (d : AIR FORCE PLANT.36. EvENGALE, OilIO 'w .*:3 '. - ...

  2. File storage

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    This file system is not configured for high IO performance. Home directories should ... Furthermore, SSH will not allow you to login if HOME.ssh is writeable by anyone other ...

  3. TO: FILE

    Office of Legacy Management (LM)

    ... C . Young ,.+-' E. Mitchal file FUS,RAP WI.4,, PA.40 L.-J Iany in :ee, anda Area ' radioactive *hap 6 as a d2 EC stop for 100 employee, and )mme :nd both these Ind Milwaukee

  4. Fast generation of sparse random kernel graphs

    SciTech Connect (OSTI)

    Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

    2015-09-10

    The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.

  5. Fast generation of sparse random kernel graphs

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

    2015-09-10

    The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less

  6. API Requirements for Dynamic Graph Prediction

    SciTech Connect (OSTI)

    Gallagher, B; Eliassi-Rad, T

    2006-10-13

    Given a large-scale time-evolving multi-modal and multi-relational complex network (a.k.a., a large-scale dynamic semantic graph), we want to implement algorithms that discover patterns of activities on the graph and learn predictive models of those discovered patterns. This document outlines the application programming interface (API) requirements for fast prototyping of feature extraction, learning, and prediction algorithms on large dynamic semantic graphs. Since our algorithms must operate on large-scale dynamic semantic graphs, we have chosen to use the graph API developed in the CASC Complex Networks Project. This API is supported on the back end by a semantic graph database (developed by Scott Kohn and his team). The advantages of using this API are (i) we have full-control of its development and (ii) the current API meets almost all of the requirements outlined in this document.

  7. Graph algorithms in the titan toolkit.

    SciTech Connect (OSTI)

    McLendon, William Clarence, III; Wylie, Brian Neil

    2009-10-01

    Graph algorithms are a key component in a wide variety of intelligence analysis activities. The Graph-Based Informatics for Non-Proliferation and Counter-Terrorism project addresses the critical need of making these graph algorithms accessible to Sandia analysts in a manner that is both intuitive and effective. Specifically we describe the design and implementation of an open source toolkit for doing graph analysis, informatics, and visualization that provides Sandia with novel analysis capability for non-proliferation and counter-terrorism.

  8. Enabling Graph Appliance for Genome Assembly

    SciTech Connect (OSTI)

    Singh, Rina; Graves, Jeffrey A; Lee, Sangkeun; Sukumar, Sreenivas R; Shankar, Mallikarjun

    2015-01-01

    In recent years, there has been a huge growth in the amount of genomic data available as reads generated from various genome sequencers. The number of reads generated can be huge, ranging from hundreds to billions of nucleotide, each varying in size. Assembling such large amounts of data is one of the challenging computational problems for both biomedical and data scientists. Most of the genome assemblers developed have used de Bruijn graph techniques. A de Bruijn graph represents a collection of read sequences by billions of vertices and edges, which require large amounts of memory and computational power to store and process. This is the major drawback to de Bruijn graph assembly. Massively parallel, multi-threaded, shared memory systems can be leveraged to overcome some of these issues. The objective of our research is to investigate the feasibility and scalability issues of de Bruijn graph assembly on Cray s Urika-GD system; Urika-GD is a high performance graph appliance with a large shared memory and massively multithreaded custom processor designed for executing SPARQL queries over large-scale RDF data sets. However, to the best of our knowledge, there is no research on representing a de Bruijn graph as an RDF graph or finding Eulerian paths in RDF graphs using SPARQL for potential genome discovery. In this paper, we address the issues involved in representing a de Bruin graphs as RDF graphs and propose an iterative querying approach for finding Eulerian paths in large RDF graphs. We evaluate the performance of our implementation on real world ebola genome datasets and illustrate how genome assembly can be accomplished with Urika-GD using iterative SPARQL queries.

  9. Useful Graphs and Charts - Ion Beams - Radiation Effects Facility...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Times 15 MeVu LET vs Range Graph 25 MeVu LET vs Range Graph 40 Mevu LET vs Range Graph Radiation Effects Facility Cyclotron Institute Texas A&M University MS 3366 ...

  10. Graph Mining Meets the Semantic Web

    SciTech Connect (OSTI)

    Lee, Sangkeun; Sukumar, Sreenivas R; Lim, Seung-Hwan

    2015-01-01

    The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today, data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. We address that need through implementation of three popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, and PageRank). We implement these algorithms as SPARQL queries, wrapped within Python scripts. We evaluate the performance of our implementation on 6 real world data sets and show graph mining algorithms (that have a linear-algebra formulation) can indeed be unleashed on data represented as RDF graphs using the SPARQL query interface.

  11. GraphReduce: Processing Large-Scale Graphs on Accelerator-Based Systems

    SciTech Connect (OSTI)

    Sengupta, Dipanjan; Song, Shuaiwen; Agarwal, Kapil; Schwan, Karsten

    2015-11-15

    Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device’s internal memory capacity. GraphReduce adopts a combination of edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the host and device.

  12. GraphReduce: Large-Scale Graph Analytics on Accelerator-Based HPC Systems

    SciTech Connect (OSTI)

    Sengupta, Dipanjan; Agarwal, Kapil; Song, Shuaiwen; Schwan, Karsten

    2015-09-30

    Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device’s internal memory capacity. GraphReduce adopts a combination of both edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the host and the device.

  13. Enabling Graph Mining in RDF Triplestores using SPARQL for Holistic In-situ Graph Analysis

    SciTech Connect (OSTI)

    Lee, Sangkeun; Sukumar, Sreenivas R; Hong, Seokyong; Lim, Seung-Hwan

    2016-01-01

    The graph analysis is now considered as a promising technique to discover useful knowledge in data with a new perspective. We envi- sion that there are two dimensions of graph analysis: OnLine Graph Analytic Processing (OLGAP) and Graph Mining (GM) where each respectively focuses on subgraph pattern matching and automatic knowledge discovery in graph. Moreover, as these two dimensions aim to complementarily solve complex problems, holistic in-situ graph analysis which covers both OLGAP and GM in a single system is critical for minimizing the burdens of operating multiple graph systems and transferring intermediate result-sets between those systems. Nevertheless, most existing graph analysis systems are only capable of one dimension of graph analysis. In this work, we take an approach to enabling GM capabilities (e.g., PageRank, connected-component analysis, node eccentricity, etc.) in RDF triplestores, which are originally developed to store RDF datasets and provide OLGAP capability. More specifically, to achieve our goal, we implemented six representative graph mining algorithms using SPARQL. The approach allows a wide range of available RDF data sets directly applicable for holistic graph analysis within a system. For validation of our approach, we evaluate performance of our implementations with nine real-world datasets and three different computing environments - a laptop computer, an Amazon EC2 instance, and a shared-memory Cray XMT2 URIKA-GD graph-processing appliance. The experimen- tal results show that our implementation can provide promising and scalable performance for real world graph analysis in all tested environments. The developed software is publicly available in an open-source project that we initiated.

  14. Enabling Graph Mining in RDF Triplestores using SPARQL for Holistic In-situ Graph Analysis

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Lee, Sangkeun; Sukumar, Sreenivas R; Hong, Seokyong; Lim, Seung-Hwan

    2016-01-01

    The graph analysis is now considered as a promising technique to discover useful knowledge in data with a new perspective. We envi- sion that there are two dimensions of graph analysis: OnLine Graph Analytic Processing (OLGAP) and Graph Mining (GM) where each respectively focuses on subgraph pattern matching and automatic knowledge discovery in graph. Moreover, as these two dimensions aim to complementarily solve complex problems, holistic in-situ graph analysis which covers both OLGAP and GM in a single system is critical for minimizing the burdens of operating multiple graph systems and transferring intermediate result-sets between those systems. Nevertheless, most existingmore » graph analysis systems are only capable of one dimension of graph analysis. In this work, we take an approach to enabling GM capabilities (e.g., PageRank, connected-component analysis, node eccentricity, etc.) in RDF triplestores, which are originally developed to store RDF datasets and provide OLGAP capability. More specifically, to achieve our goal, we implemented six representative graph mining algorithms using SPARQL. The approach allows a wide range of available RDF data sets directly applicable for holistic graph analysis within a system. For validation of our approach, we evaluate performance of our implementations with nine real-world datasets and three different computing environments - a laptop computer, an Amazon EC2 instance, and a shared-memory Cray XMT2 URIKA-GD graph-processing appliance. The experimen- tal results show that our implementation can provide promising and scalable performance for real world graph analysis in all tested environments. The developed software is publicly available in an open-source project that we initiated.« less

  15. CEKRM. FILES

    Office of Legacy Management (LM)

    *:-I c-Y,- -.>A - L z.23 ' ,gQ+ CEKRM. FILES : -- i" ry .' ;,:;;:i ;- ' _ :;, I' -. .-,- p.." * .i' ' --yr:,? ,5 .Y' :?!, .I I >L L qgy~T.5 ,-:,,. \>,' yt r, .,-:,7 ,A : _ . . T_ 31' :;:: . .' : z ^., - -; &.' -" ' \-,-y . ..L' .:"A .:1i2*;,1 1- .,: _.,-.- 1 ,... _ . , , 2; J..i"!w , . . . .' _ \ ' d>; : r. . _ " ' . ;I 2: a : ..i. ,. 7, I . :ri cij?TL-i; 12, ;;,2;-,: ;. ." * 1. 1 .:1 : :: .' I .-=-. I. Iv-.. . . . . - i. ; i -2. ./ ..l,..- '

  16. Bipartite graph partitioning and data clustering

    SciTech Connect (OSTI)

    Zha, Hongyuan; He, Xiaofeng; Ding, Chris; Gu, Ming; Simon, Horst D.

    2001-05-07

    Many data types arising from data mining applications can be modeled as bipartite graphs, examples include terms and documents in a text corpus, customers and purchasing items in market basket analysis and reviewers and movies in a movie recommender system. In this paper, the authors propose a new data clustering method based on partitioning the underlying biopartite graph. The partition is constructed by minimizing a normalized sum of edge weights between unmatched pairs of vertices of the bipartite graph. They show that an approximate solution to the minimization problem can be obtained by computing a partial singular value decomposition (SVD) of the associated edge weight matrix of the bipartite graph. They point out the connection of their clustering algorithm to correspondence analysis used in multivariate analysis. They also briefly discuss the issue of assigning data objects to multiple clusters. In the experimental results, they apply their clustering algorithm to the problem of document clustering to illustrate its effectiveness and efficiency.

  17. Visualization Graph | OpenEI Community

    Open Energy Info (EERE)

    8 August, 2012 - 12:37 New Gapminder Visualizations Added EIA Energy data Gapminder OECD OpenEI SEDS Visualization Graph OpenEI now features some cool new Gapminder...

  18. Fault-tolerant dynamic task graph scheduling

    SciTech Connect (OSTI)

    Kurt, Mehmet C.; Krishnamoorthy, Sriram; Agrawal, Kunal; Agrawal, Gagan

    2014-11-16

    In this paper, we present an approach to fault tolerant execution of dynamic task graphs scheduled using work stealing. In particular, we focus on selective and localized recovery of tasks in the presence of soft faults. We elicit from the user the basic task graph structure in terms of successor and predecessor relationships. The work stealing-based algorithm to schedule such a task graph is augmented to enable recovery when the data and meta-data associated with a task get corrupted. We use this redundancy, and the knowledge of the task graph structure, to selectively recover from faults with low space and time overheads. We show that the fault tolerant design retains the essential properties of the underlying work stealing-based task scheduling algorithm, and that the fault tolerant execution is asymptotically optimal when task re-execution is taken into account. Experimental evaluation demonstrates the low cost of recovery under various fault scenarios.

  19. Bayati Kim Saberi random graph sampler

    Energy Science and Technology Software Center (OSTI)

    2012-06-05

    This software package implements the algorithm from a paper by Bayati, Kim, and Saberi (first reference below) to generate a uniformly random sample of a graph with a prescribed degree distribution.

  20. Accelerating semantic graph databases on commodity clusters

    SciTech Connect (OSTI)

    Morari, Alessandro; Castellana, Vito G.; Haglin, David J.; Feo, John T.; Weaver, Jesse R.; Tumeo, Antonino; Villa, Oreste

    2013-10-06

    We are developing a full software system for accelerating semantic graph databases on commodity cluster that scales to hundreds of nodes while maintaining constant query throughput. Our framework comprises a SPARQL to C++ compiler, a library of parallel graph methods and a custom multithreaded runtime layer, which provides a Partitioned Global Address Space (PGAS) programming model with fork/join parallelism and automatic load balancing over a commodity clusters. We present preliminary results for the compiler and for the runtime.

  1. Graph representation of protein free energy landscape

    SciTech Connect (OSTI)

    Li, Minghai; Duan, Mojie; Fan, Jue; Huo, Shuanghong; Han, Li

    2013-11-14

    The thermodynamics and kinetics of protein folding and protein conformational changes are governed by the underlying free energy landscape. However, the multidimensional nature of the free energy landscape makes it difficult to describe. We propose to use a weighted-graph approach to depict the free energy landscape with the nodes on the graph representing the conformational states and the edge weights reflecting the free energy barriers between the states. Our graph is constructed from a molecular dynamics trajectory and does not involve projecting the multi-dimensional free energy landscape onto a low-dimensional space defined by a few order parameters. The calculation of free energy barriers was based on transition-path theory using the MSMBuilder2 package. We compare our graph with the widely used transition disconnectivity graph (TRDG) which is constructed from the same trajectory and show that our approach gives more accurate description of the free energy landscape than the TRDG approach even though the latter can be organized into a simple tree representation. The weighted-graph is a general approach and can be used on any complex system.

  2. NERSC File Systems

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    sharing between platforms. File systems are configured for different purposes. On each machine you have access to at least three different file system Home: Permanent, relatively...

  3. Continuous-time quantum walks on star graphs

    SciTech Connect (OSTI)

    Salimi, S.

    2009-06-15

    In this paper, we investigate continuous-time quantum walk on star graphs. It is shown that quantum central limit theorem for a continuous-time quantum walk on star graphs for N-fold star power graph, which are invariant under the quantum component of adjacency matrix, converges to continuous-time quantum walk on K{sub 2} graphs (complete graph with two vertices) and the probability of observing walk tends to the uniform distribution.

  4. NX Configuration File

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Configuration File NX Configuration File This is the NX configuration file that you will need to connect to NERSC NX Server: NX5Confgure.nxs.zip To use this file, please follow the Online Tutorial. Last edited: 2016-04-29 11:34:57

  5. Dynamic graph system for a semantic database

    DOE Patents [OSTI]

    Mizell, David

    2015-01-27

    A method and system in a computer system for dynamically providing a graphical representation of a data store of entries via a matrix interface is disclosed. A dynamic graph system provides a matrix interface that exposes to an application program a graphical representation of data stored in a data store such as a semantic database storing triples. To the application program, the matrix interface represents the graph as a sparse adjacency matrix that is stored in compressed form. Each entry of the data store is considered to represent a link between nodes of the graph. Each entry has a first field and a second field identifying the nodes connected by the link and a third field with a value for the link that connects the identified nodes. The first, second, and third fields represent the rows, column, and elements of the adjacency matrix.

  6. Dynamic graph system for a semantic database

    DOE Patents [OSTI]

    Mizell, David

    2016-04-12

    A method and system in a computer system for dynamically providing a graphical representation of a data store of entries via a matrix interface is disclosed. A dynamic graph system provides a matrix interface that exposes to an application program a graphical representation of data stored in a data store such as a semantic database storing triples. To the application program, the matrix interface represents the graph as a sparse adjacency matrix that is stored in compressed form. Each entry of the data store is considered to represent a link between nodes of the graph. Each entry has a first field and a second field identifying the nodes connected by the link and a third field with a value for the link that connects the identified nodes. The first, second, and third fields represent the rows, column, and elements of the adjacency matrix.

  7. Communication Graph Generator for Parallel Programs

    Energy Science and Technology Software Center (OSTI)

    2014-04-08

    Graphator is a collection of relatively simple sequential programs that generate communication graphs/matrices for commonly occurring patterns in parallel programs. Currently, there is support for five communication patterns: two-dimensional 4-point stencil, four-dimensional 8-point stencil, all-to-alls over sub-communicators, random near-neighbor communication, and near-neighbor communication.

  8. GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection

    SciTech Connect (OSTI)

    Harshaw, Chris R; Bridges, Robert A; Iannacone, Michael D; Reed, Joel W; Goodall, John R

    2016-01-01

    This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called \\textit{GraphPrints}. Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets\\textemdash small induced subgraphs that describe local topology. By performing outlier detection on the sequence of graphlet counts, anomalous intervals of traffic are identified, and furthermore, individual IPs experiencing abnormal behavior are singled-out. Initial testing of GraphPrints is performed on real network data with an implanted anomaly. Evaluation shows false positive rates bounded by 2.84\\% at the time-interval level, and 0.05\\% at the IP-level with 100\\% true positive rates at both.

  9. Navigating NERSC File Systems

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Navigating NERSC File Systems May 3, 2011 David-Turner.jpg NERSC Training Event 10:00 - ... Navigating NERSC File Systems David Turner, NERSC User Services Group NERSC hosts a number ...

  10. Fast Global File Status

    Energy Science and Technology Software Center (OSTI)

    2013-01-01

    Fast Global File Status (FGFS) is a system software package that implimints a scalable mechanism to retrieve file information, such as its degree of distribution or replication and consistency.

  11. Standard interface file handbook

    SciTech Connect (OSTI)

    Shapiro, A.; Huria, H.C. )

    1992-10-01

    This handbook documents many of the standard interface file formats that have been adopted by the US Department of Energy to facilitate communications between and portability of, various large reactor physics and radiation transport software packages. The emphasis is on those files needed for use of the VENTURE/PC diffusion-depletion code system. File structures, contents and some practical advice on use of the various files are provided.

  12. StreamWorks - A system for Dynamic Graph Search

    SciTech Connect (OSTI)

    Choudhury, Sutanay; Holder, Larry; Chin, George; Ray, Abhik; Beus, Sherman J.; Feo, John T.

    2013-06-11

    Acting on time-critical events by processing ever growing social media, news or cyber data streams is a major technical challenge. Many of these data sources can be modeled as multi-relational graphs. Mining and searching for subgraph patterns in a continuous setting requires an efficient approach to incremental graph search. The goal of our work is to enable real-time search capabilities for graph databases. This demonstration will present a dynamic graph query system that leverages the structural and semantic characteristics of the underlying multi-relational graph.

  13. Modular Environment for Graph Research and Analysis with a Persistent

    Energy Science and Technology Software Center (OSTI)

    2009-11-18

    The MEGRAPHS software package provides a front-end to graphs and vectors residing on special-purpose computing resources. It allows these data objects to be instantiated, destroyed, and manipulated. A variety of primitives needed for typical graph analyses are provided. An example program illustrating how MEGRAPHS can be used to implement a PageRank computation is included in the distribution.The MEGRAPHS software package is targeted towards developers of graph algorithms. Programmers using MEGRAPHS would write graph analysis programsmore » in terms of high-level graph and vector operations. These computations are transparently executed on the Cray XMT compute nodes.« less

  14. Knowledge Representation Issues in Semantic Graphs for Relationship Detection

    SciTech Connect (OSTI)

    Barthelemy, M; Chow, E; Eliassi-Rad, T

    2005-02-02

    An important task for Homeland Security is the prediction of threat vulnerabilities, such as through the detection of relationships between seemingly disjoint entities. A structure used for this task is a ''semantic graph'', also known as a ''relational data graph'' or an ''attributed relational graph''. These graphs encode relationships as typed links between a pair of typed nodes. Indeed, semantic graphs are very similar to semantic networks used in AI. The node and link types are related through an ontology graph (also known as a schema). Furthermore, each node has a set of attributes associated with it (e.g., ''age'' may be an attribute of a node of type ''person''). Unfortunately, the selection of types and attributes for both nodes and links depends on human expertise and is somewhat subjective and even arbitrary. This subjectiveness introduces biases into any algorithm that operates on semantic graphs. Here, we raise some knowledge representation issues for semantic graphs and provide some possible solutions using recently developed ideas in the field of complex networks. In particular, we use the concept of transitivity to evaluate the relevance of individual links in the semantic graph for detecting relationships. We also propose new statistical measures for semantic graphs and illustrate these semantic measures on graphs constructed from movies and terrorism data.

  15. Graph processing platforms at scale: practices and experiences

    SciTech Connect (OSTI)

    Lim, Seung-Hwan; Lee, Sangkeun; Brown, Tyler C; Sukumar, Sreenivas R; Ganesh, Gautam

    2015-01-01

    Graph analysis unveils hidden associations of data in many phenomena and artifacts, such as road network, social networks, genomic information, and scientific collaboration. Unfortunately, a wide diversity in the characteristics of graphs and graph operations make it challenging to find a right combination of tools and implementation of algorithms to discover desired knowledge from the target data set. This study presents an extensive empirical study of three representative graph processing platforms: Pegasus, GraphX, and Urika. Each system represents a combination of options in data model, processing paradigm, and infrastructure. We benchmarked each platform using three popular graph operations, degree distribution, connected components, and PageRank over a variety of real-world graphs. Our experiments show that each graph processing platform shows different strength, depending the type of graph operations. While Urika performs the best in non-iterative operations like degree distribution, GraphX outputforms iterative operations like connected components and PageRank. In addition, we discuss challenges to optimize the performance of each platform over large scale real world graphs.

  16. Frequent Subgraph Discovery in Large Attributed Streaming Graphs

    SciTech Connect (OSTI)

    Ray, Abhik; Holder, Larry; Choudhury, Sutanay

    2014-08-13

    The problem of finding frequent subgraphs in large dynamic graphs has so far only consid- ered a dynamic graph as being represented by a series of static snapshots taken at various points in time. This representation of a dynamic graph does not lend itself well to real time processing of real world graphs like social networks or internet traffic which consist of a stream of nodes and edges. In this paper we propose an algorithm that discovers the frequent subgraphs present in a graph represented by a stream of labeled nodes and edges. Our algorithm is efficient and consists of tunable parameters that can be tuned by the user to get interesting patterns from various kinds of graph data. In our model updates to the graph arrive in the form of batches which contain new nodes and edges. Our algorithm con- tinuously reports the frequent subgraphs that are estimated to be found in the entire graph as each batch arrives. We evaluate our system using 5 large dynamic graph datasets: the Hetrec 2011 challenge data, Twitter, DBLP and two synthetic. We evaluate our approach against two popular large graph miners, i.e., SUBDUE and GERM. Our experimental re- sults show that we can find the same frequent subgraphs as a non-incremental approach applied to snapshot graphs, and in less time.

  17. Eliza File Systems

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Eliza File Systems Eliza File Systems Storage at PDSF is organized by group and available to all the nodes in PDSF. Please note that files on these file systems are not backed up. It is the responsibility of users to back up their files themselves as necessary. Over the years at PDSF there have been incidents which resulted in a loss of data from disk, often related to aging hardware. You can display the UGE io units with the command "qconf -se global". Disk Vault Experiments SGE io

  18. Jargon and Graph Modularity on Twitter

    SciTech Connect (OSTI)

    Dowling, Chase P.; Corley, Courtney D.; Farber, Robert M.; Reynolds, William

    2013-09-01

    The language of conversation is just as dependent upon word choice as it is on who is taking part. Twitter provides an excellent test-bed in which to conduct experiments not only on language usage but on who is using what language with whom. To this end, we combine large scale graph analytical techniques with known socio-linguistic methods. In this article we leverage both expert curated vocabularies and naive mathematical graph analyses to determine if network behavior on Twitter corroborates with the current understanding of language usage. The results reported indicate that, based on networks constructed from user to user communication and communities identified using the Clauset- Newman greedy modularity algorithm we find that more prolific users of these curated vocabularies are concentrated in distinct network communities.

  19. File:Bureauofreclamationfactsheet.pdf | Open Energy Information

    Open Energy Info (EERE)

    Bureauofreclamationfactsheet.pdf Jump to: navigation, search File File history File usage Metadata File:Bureauofreclamationfactsheet.pdf Size of this preview: 463 599 pixels....

  20. File:20121127144703735.pdf | Open Energy Information

    Open Energy Info (EERE)

    20121127144703735.pdf Jump to: navigation, search File File history File usage File:20121127144703735.pdf Size of this preview: 463 599 pixels. Other resolution: 464 600...

  1. File:Windyclassroom.pdf | Open Energy Information

    Open Energy Info (EERE)

    Windyclassroom.pdf Jump to: navigation, search File File history File usage Metadata File:Windyclassroom.pdf Size of this preview: 463 599 pixels. Other resolution: 464 600...

  2. File:Npsfactsheet.pdf | Open Energy Information

    Open Energy Info (EERE)

    Npsfactsheet.pdf Jump to: navigation, search File File history File usage Metadata File:Npsfactsheet.pdf Size of this preview: 463 599 pixels. Other resolution: 464 600...

  3. File:Oregonshpodocumentationstandards.pdf | Open Energy Information

    Open Energy Info (EERE)

    Oregonshpodocumentationstandards.pdf Jump to: navigation, search File File history File usage File:Oregonshpodocumentationstandards.pdf Size of this preview: 463 599 pixels....

  4. File:Noaafactsheet.pdf | Open Energy Information

    Open Energy Info (EERE)

    Noaafactsheet.pdf Jump to: navigation, search File File history File usage Metadata File:Noaafactsheet.pdf Size of this preview: 463 599 pixels. Other resolution: 464 600...

  5. File:Drillinghandbook.pdf | Open Energy Information

    Open Energy Info (EERE)

    Drillinghandbook.pdf Jump to: navigation, search File File history File usage File:Drillinghandbook.pdf Size of this preview: 463 599 pixels. Other resolution: 464 600...

  6. File:Energydatabusfacthseet.pdf | Open Energy Information

    Open Energy Info (EERE)

    Energydatabusfacthseet.pdf Jump to: navigation, search File File history File usage Metadata File:Energydatabusfacthseet.pdf Size of this preview: 463 599 pixels. Other...

  7. File:Bonnevillepowerfactsheet.pdf | Open Energy Information

    Open Energy Info (EERE)

    Bonnevillepowerfactsheet.pdf Jump to: navigation, search File File history File usage Metadata File:Bonnevillepowerfactsheet.pdf Size of this preview: 463 599 pixels. Other...

  8. Eia.gov BETA - Analysis & Projections - U.S. Energy Information...

    Gasoline and Diesel Fuel Update (EIA)

    power plants, 1 MW or more XLS Shapefile Data definitions for all above data files XLS Web services and other map resources North American Cooperation on Energy Information, ...

  9. Original Signature on File

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Original Signature on File Page8 of 8 M. EMERGENCY PROCEDURES 1. The owneroperator must maintain an adequately trained onsite RCRA emergency coordinator to direct emergency...

  10. Other File Systems

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    User Home Directories Your home directory is located at homelogin-name. Home directories ... Home directories are backed up as insurance against catastrophic file system failure. ...

  11. EIA-411 Data File

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Form EIA-411 Database Form EIA-411 - Coordinated Bulk Power Supply Program Report Data ... except No. 7 (Transmission Outages) which will continue to be a voluntary filing. ...

  12. A Graph Search Heuristic for Shortest Distance Paths

    SciTech Connect (OSTI)

    Chow, E

    2005-03-24

    This paper presents a heuristic for guiding A* search for finding the shortest distance path between two vertices in a connected, undirected, and explicitly stored graph. The heuristic requires a small amount of data to be stored at each vertex. The heuristic has application to quickly detecting relationships between two vertices in a large information or knowledge network. We compare the performance of this heuristic with breadth-first search on graphs with various topological properties. The results show that one or more orders of magnitude improvement in the number of vertices expanded is possible for large graphs, including Poisson random graphs.

  13. Sequoia supercomputer tops Graph 500 | National Nuclear Security...

    National Nuclear Security Administration (NNSA)

    Lawrence Livermore National Laboratory scientists' search for new ways to solve large complex national security problems led to the top ranking on Graph 500 and new techniques for ...

  14. Mining Graphs for Understanding Time-Varying Volumetric Data...

    Office of Scientific and Technical Information (OSTI)

    SciTech Connect Search Results Journal Article: Mining Graphs for Understanding ... DOE Contract Number: AC02-06CH11357 Resource Type: Journal Article Resource Relation: ...

  15. Two linear time, low overhead algorithms for graph layout

    Energy Science and Technology Software Center (OSTI)

    2008-01-10

    The software comprises two algorithms designed to perform a 2D layout of a graph structure in time linear with respect to the vertices and edges in the graph, whereas most other layout algorithms have a running time that is quadratic with respect to the number of vertices or greater. Although these layout algorithms run in a fraction of the time as their competitors, they provide competitive results when applied to most real-world graphs. These algorithmsmore » also have a low constant running time and small memory footprint, making them useful for small to large graphs.« less

  16. International energy indicators. [Statistical tables and graphs

    SciTech Connect (OSTI)

    Bauer, E.K.

    1980-05-01

    International statistical tables and graphs are given for the following: (1) Iran - Crude Oil Capacity, Production and Shut-in, June 1974-April 1980; (2) Saudi Arabia - Crude Oil Capacity, Production, and Shut-in, March 1974-Apr 1980; (3) OPEC (Ex-Iran and Saudi Arabia) - Capacity, Production and Shut-in, June 1974-March 1980; (4) Non-OPEC Free World and US Production of Crude Oil, January 1973-February 1980; (5) Oil Stocks - Free World, US, Japan, and Europe (Landed, 1973-1st Quarter, 1980); (6) Petroleum Consumption by Industrial Countries, January 1973-December 1979; (7) USSR Crude Oil Production and Exports, January 1974-April 1980; and (8) Free World and US Nuclear Generation Capacity, January 1973-March 1980. Similar statistical tables and graphs included for the United States include: (1) Imports of Crude Oil and Products, January 1973-April 1980; (2) Landed Cost of Saudi Oil in Current and 1974 Dollars, April 1974-January 1980; (3) US Trade in Coal, January 1973-March 1980; (4) Summary of US Merchandise Trade, 1976-March 1980; and (5) US Energy/GNP Ratio, 1947 to 1979.

  17. Register file soft error recovery

    DOE Patents [OSTI]

    Fleischer, Bruce M.; Fox, Thomas W.; Wait, Charles D.; Muff, Adam J.; Watson, III, Alfred T.

    2013-10-15

    Register file soft error recovery including a system that includes a first register file and a second register file that mirrors the first register file. The system also includes an arithmetic pipeline for receiving data read from the first register file, and error detection circuitry to detect whether the data read from the first register file includes corrupted data. The system further includes error recovery circuitry to insert an error recovery instruction into the arithmetic pipeline in response to detecting the corrupted data. The inserted error recovery instruction replaces the corrupted data in the first register file with a copy of the data from the second register file.

  18. Scaling Semantic Graph Databases in Size and Performance

    SciTech Connect (OSTI)

    Morari, Alessandro; Castellana, Vito G.; Villa, Oreste; Tumeo, Antonino; Weaver, Jesse R.; Haglin, David J.; Choudhury, Sutanay; Feo, John T.

    2014-08-06

    In this paper we present SGEM, a full software system for accelerating large-scale semantic graph databases on commodity clusters. Unlike current approaches, SGEM addresses semantic graph databases by only employing graph methods at all the levels of the stack. On one hand, this allows exploiting the space efficiency of graph data structures and the inherent parallelism of graph algorithms. These features adapt well to the increasing system memory and core counts of modern commodity clusters. On the other hand, however, these systems are optimized for regular computation and batched data transfers, while graph methods usually are irregular and generate fine-grained data accesses with poor spatial and temporal locality. Our framework comprises a SPARQL to data parallel C compiler, a library of parallel graph methods and a custom, multithreaded runtime system. We introduce our stack, motivate its advantages with respect to other solutions and show how we solved the challenges posed by irregular behaviors. We present the result of our software stack on the Berlin SPARQL benchmarks with datasets up to 10 billion triples (a triple corresponds to a graph edge), demonstrating scaling in dataset size and in performance as more nodes are added to the cluster.

  19. File:DIAsample.pdf | Open Energy Information

    Open Energy Info (EERE)

    DIAsample.pdf Jump to: navigation, search File File history File usage File:DIAsample.pdf Size of this preview: 776 600 pixels. Full resolution (1,650 1,275 pixels, file...

  20. EAGLE: 'EAGLE'Is an' Algorithmic Graph Library for Exploration

    SciTech Connect (OSTI)

    2015-01-16

    The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. Today there is no tools to conduct "graph mining" on RDF standard data sets. We address that need through implementation of popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, degree distribution, diversity degree, PageRank, etc.). We implement these algorithms as SPARQL queries, wrapped within Python scripts and call our software tool as EAGLE. In RDF style, EAGLE stands for "EAGLE 'Is an' algorithmic graph library for exploration. EAGLE is like 'MATLAB' for 'Linked Data.'

  1. An Experiment on Graph Analysis Methodologies for Scenarios

    SciTech Connect (OSTI)

    Brothers, Alan J.; Whitney, Paul D.; Wolf, Katherine E.; Kuchar, Olga A.; Chin, George

    2005-09-30

    Visual graph representations are increasingly used to represent, display, and explore scenarios and the structure of organizations. The graph representations of scenarios are readily understood, and commercial software is available to create and manage these representations. The purpose of the research presented in this paper is to explore whether these graph representations support quantitative assessments of the underlying scenarios. The underlying structure of the scenarios is the information that is being targeted in the experiment and the extent to which the scenarios are similar in content. An experiment was designed that incorporated both the contents of the scenarios and analysts’ graph representations of the scenarios. The scenarios’ content was represented graphically by analysts, and both the structure and the semantics of the graph representation were attempted to be used to understand the content. The structure information was not found to be discriminating for the content of the scenarios in this experiment; but, the semantic information was discriminating.

  2. Query optimization for graph analytics on linked data using SPARQL

    SciTech Connect (OSTI)

    Hong, Seokyong; Lee, Sangkeun; Lim, Seung -Hwan; Sukumar, Sreenivas R.; Vatsavai, Ranga Raju

    2015-07-01

    Triplestores that support query languages such as SPARQL are emerging as the preferred and scalable solution to represent data and meta-data as massive heterogeneous graphs using Semantic Web standards. With increasing adoption, the desire to conduct graph-theoretic mining and exploratory analysis has also increased. Addressing that desire, this paper presents a solution that is the marriage of Graph Theory and the Semantic Web. We present software that can analyze Linked Data using graph operations such as counting triangles, finding eccentricity, testing connectedness, and computing PageRank directly on triple stores via the SPARQL interface. We describe the process of optimizing performance of the SPARQL-based implementation of such popular graph algorithms by reducing the space-overhead, simplifying iterative complexity and removing redundant computations by understanding query plans. Our optimized approach shows significant performance gains on triplestores hosted on stand-alone workstations as well as hardware-optimized scalable supercomputers such as the Cray XMT.

  3. Graph facilitates tracking water and gas influx

    SciTech Connect (OSTI)

    Gruy, H.J. )

    1990-03-26

    Graphing the vertical distribution of reservoir volume is an easy method for estimating the acre-ft remaining to be exploited in reservoirs with water or gas encroachment. To evaluate reservoir performance and estimate oil and gas reserves in water-drive reservoirs or oil reservoirs with a gas cap, it is necessary to determine the magnitude of the movement of oil-water and gas-oil contact surfaces. In reviewing reserve estimates and reservoir studies done by others, the authors have found that very few reservoir engineers or geologists have an easy method for tracking the movement of these surfaces and estimating the volumes of oil displaced water encroachment, gas cap expansion, or the volumes of oil lost by wetting the gas cap. The following method evolved from the author's studies of the East Texas field starting in 1942, and it took this form in the early 1950s.

  4. Construction of file database management

    SciTech Connect (OSTI)

    MERRILL,KYLE J.

    2000-03-01

    This work created a database for tracking data analysis files from multiple lab techniques and equipment stored on a central file server. Experimental details appropriate for each file type are pulled from the file header and stored in a searchable database. The database also stores specific location and self-directory structure for each data file. Queries can be run on the database according to file type, sample type or other experimental parameters. The database was constructed in Microsoft Access and Visual Basic was used for extraction of information from the file header.

  5. Filing Information | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Filing Information Filing Information The Office of Hearings and Appeals (OHA) encourages electronic filing of submissions, by e-mail or by telefax. OHA's e-mail address for filing submissions is OHA.filings@hq.doe.gov; the OHA telefax number is (202) 287-1415. Note, however, that because of signature issues, we may ask you to file a signed original of a document. We will send an acknowledgment (by letter or e-mail) upon our receipt of all principal pleadings. If you choose to file by regular

  6. MEMORANDUM TO: FILE FROM:

    Office of Legacy Management (LM)

    p' : , .; ' ' < 3.518 MEMORANDUM TO: FILE FROM: -Ye L&a ---...e---e--- DATE 6j88 7 v---s -- ---... SUBJECT: ;&l a+-b IA Tcornqm Q afib4 SITE NAME: CITY: & &&at leg co ...

  7. Unix File Permissions

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    also sometimes known as "world" permissions, and applies to all users who can login to the system. The command "ls -l" displays the permissions and associated group for any file. ...

  8. Composing Data Parallel Code for a SPARQL Graph Engine

    SciTech Connect (OSTI)

    Castellana, Vito G.; Tumeo, Antonino; Villa, Oreste; Haglin, David J.; Feo, John

    2013-09-08

    Big data analytics process large amount of data to extract knowledge from them. Semantic databases are big data applications that adopt the Resource Description Framework (RDF) to structure metadata through a graph-based representation. The graph based representation provides several benefits, such as the possibility to perform in memory processing with large amounts of parallelism. SPARQL is a language used to perform queries on RDF-structured data through graph matching. In this paper we present a tool that automatically translates SPARQL queries to parallel graph crawling and graph matching operations. The tool also supports complex SPARQL constructs, which requires more than basic graph matching for their implementation. The tool generates parallel code annotated with OpenMP pragmas for x86 Shared-memory Multiprocessors (SMPs). With respect to commercial database systems such as Virtuoso, our approach reduces memory occupation due to join operations and provides higher performance. We show the scaling of the automatically generated graph-matching code on a 48-core SMP.

  9. File:NREL-banglmetst-221.pdf | Open Energy Information

    Open Energy Info (EERE)

    File Edit with form History File:NREL-banglmetst-221.pdf Jump to: navigation, search File File history File usage Selected Meteorological Stations and Elevation Size of this...

  10. Parallel Algorithms for Graph Optimization using Tree Decompositions

    SciTech Connect (OSTI)

    Sullivan, Blair D; Weerapurage, Dinesh P; Groer, Christopher S

    2012-06-01

    Although many $\\cal{NP}$-hard graph optimization problems can be solved in polynomial time on graphs of bounded tree-width, the adoption of these techniques into mainstream scientific computation has been limited due to the high memory requirements of the necessary dynamic programming tables and excessive runtimes of sequential implementations. This work addresses both challenges by proposing a set of new parallel algorithms for all steps of a tree decomposition-based approach to solve the maximum weighted independent set problem. A hybrid OpenMP/MPI implementation includes a highly scalable parallel dynamic programming algorithm leveraging the MADNESS task-based runtime, and computational results demonstrate scaling. This work enables a significant expansion of the scale of graphs on which exact solutions to maximum weighted independent set can be obtained, and forms a framework for solving additional graph optimization problems with similar techniques.

  11. Must all charting and graphing code be written in javascript...

    Open Energy Info (EERE)

    Must all charting and graphing code be written in javascript? Home > Groups > Databus In the documentation chapter entitled Developing charts using 3rd party api, we are told that...

  12. EAGLE: 'EAGLE'Is an' Algorithmic Graph Library for Exploration

    Energy Science and Technology Software Center (OSTI)

    2015-01-16

    The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. Today there is no tools to conduct "graphmore » mining" on RDF standard data sets. We address that need through implementation of popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, degree distribution, diversity degree, PageRank, etc.). We implement these algorithms as SPARQL queries, wrapped within Python scripts and call our software tool as EAGLE. In RDF style, EAGLE stands for "EAGLE 'Is an' algorithmic graph library for exploration. EAGLE is like 'MATLAB' for 'Linked Data.'« less

  13. Data Sources For Emerging Technologies Program MYPP Target Graphs

    Broader source: Energy.gov [DOE]

    The BTO Emerging Technologies Accomplishments and Outcomes – 2015 page contains graphs on Multi-Year Program Plan R&D targets for certain technologies. This page contains information on data...

  14. TIFF Image Writer patch for OpenSceneGraph

    Energy Science and Technology Software Center (OSTI)

    2012-01-05

    This software consists of code modifications to the open-source OpenSceneGraph software package to enable the creation of TlFF images containing 16 bit unsigned data. They also allow the user to disable compression and set the DPI tags in the resulting TIFF Images. Some image analysis programs require uncompressed, 16 bit unsigned input data. These code modifications allow programs based on OpenSceneGraph to write out such images, improving connectivity between applications.

  15. Highly Asynchronous VisitOr Queue Graph Toolkit

    Energy Science and Technology Software Center (OSTI)

    2012-10-01

    HAVOQGT is a C++ framework that can be used to create highly parallel graph traversal algorithms. The framework stores the graph and algorithmic data structures on external memory that is typically mapped to high performance locally attached NAND FLASH arrays. The framework supports a vertex-centered visitor programming model. The frameworkd has been used to implement breadth first search, connected components, and single source shortest path.

  16. File:S-38-Well-Construction-and-Modification-Permit.pdf | Open...

    Open Energy Info (EERE)

    S-38-Well-Construction-and-Modification-Permit.pdf Jump to: navigation, search File File history File usage File:S-38-Well-Construction-and-Modification-Permit.pdf Size of this...

  17. The peculiar phase structure of random graph bisection

    SciTech Connect (OSTI)

    Percus, Allon G; Istrate, Gabriel; Goncalves, Bruno T; Sumi, Robert Z

    2008-01-01

    The mincut graph bisection problem involves partitioning the n vertices of a graph into disjoint subsets, each containing exactly n/2 vertices, while minimizing the number of 'cut' edges with an endpoint in each subset. When considered over sparse random graphs, the phase structure of the graph bisection problem displays certain familiar properties, but also some surprises. It is known that when the mean degree is below the critical value of 2 log 2, the cutsize is zero with high probability. We study how the minimum cutsize increases with mean degree above this critical threshold, finding a new analytical upper bound that improves considerably upon previous bounds. Combined with recent results on expander graphs, our bound suggests the unusual scenario that random graph bisection is replica symmetric up to and beyond the critical threshold, with a replica symmetry breaking transition possibly taking place above the threshold. An intriguing algorithmic consequence is that although the problem is NP-hard, we can find near-optimal cutsizes (whose ratio to the optimal value approaches 1 asymptotically) in polynomial time for typical instances near the phase transition.

  18. File:Mmpa.pdf | Open Energy Information

    Open Energy Info (EERE)

    Mmpa.pdf Jump to: navigation, search File File history File usage File:Mmpa.pdf Size of this preview: 463 599 pixels. Other resolution: 464 600 pixels. Go to page 1 2 3 4 5 6...

  19. File:Keystone.pdf | Open Energy Information

    Open Energy Info (EERE)

    Keystone.pdf Jump to: navigation, search File File history File usage File:Keystone.pdf Size of this preview: 463 599 pixels. Other resolution: 464 600 pixels. Go to page 1 2...

  20. File:Handbook.pdf | Open Energy Information

    Open Energy Info (EERE)

    Handbook.pdf Jump to: navigation, search File File history File usage File:Handbook.pdf Size of this preview: 463 599 pixels. Other resolution: 464 600 pixels. Go to page 1 2...

  1. File:Install.pdf | Open Energy Information

    Open Energy Info (EERE)

    Install.pdf Jump to: navigation, search File File history File usage File:Install.pdf Size of this preview: 463 599 pixels. Other resolution: 464 600 pixels. Full resolution...

  2. File:Hydrofracking.pdf | Open Energy Information

    Open Energy Info (EERE)

    Hydrofracking.pdf Jump to: navigation, search File File history File usage File:Hydrofracking.pdf Size of this preview: 463 599 pixels. Other resolution: 464 600 pixels. Go...

  3. File:Consultants.pdf | Open Energy Information

    Open Energy Info (EERE)

    Consultants.pdf Jump to: navigation, search File File history File usage File:Consultants.pdf Size of this preview: 463 599 pixels. Other resolution: 464 600 pixels. Go to...

  4. File:Installnot.pdf | Open Energy Information

    Open Energy Info (EERE)

    Installnot.pdf Jump to: navigation, search File File history File usage File:Installnot.pdf Size of this preview: 463 599 pixels. Other resolution: 464 600 pixels. Full...

  5. File:RSC.pdf | Open Energy Information

    Open Energy Info (EERE)

    RSC.pdf Jump to: navigation, search File File history File usage File:RSC.pdf Size of this preview: 463 599 pixels. Other resolution: 464 600 pixels. Go to page 1 2 3 4 5 6 7...

  6. Category:Map Files | Open Energy Information

    Open Energy Info (EERE)

    has the following 4 subcategories, out of 4 total. M Map Image Files Map PDF Files N NREL Map Files 1 pages S SWERA Map Files Media in category...

  7. File:Primer.pdf | Open Energy Information

    Open Energy Info (EERE)

    Primer.pdf Jump to: navigation, search File File history File usage File:Primer.pdf Size of this preview: 463 599 pixels. Other resolution: 464 600 pixels. Go to page 1 2 3 4...

  8. File:600.pdf | Open Energy Information

    Open Energy Info (EERE)

    600.pdf Jump to: navigation, search File File history File usage File:600.pdf Size of this preview: 463 599 pixels. Other resolution: 464 600 pixels. Go to page 1 2 3 4 5 6 7...

  9. File:SGI.pdf | Open Energy Information

    Open Energy Info (EERE)

    SGI.pdf Jump to: navigation, search File File history File usage Metadata File:SGI.pdf Size of this preview: 463 599 pixels. Other resolution: 464 600 pixels. Go to page 1 2...

  10. File:038392007).pdf | Open Energy Information

    Open Energy Info (EERE)

    8392007).pdf Jump to: navigation, search File File history File usage File:038392007).pdf Size of this preview: 463 599 pixels. Other resolution: 463 600 pixels. Go to page 1...

  11. File:Methane.pdf | Open Energy Information

    Open Energy Info (EERE)

    Methane.pdf Jump to: navigation, search File File history File usage File:Methane.pdf Size of this preview: 448 600 pixels. Go to page 1 2 3 4 5 Go next page next page ...

  12. Hopper File Storage and I/O

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    File Storage and IO File Storage and IO Disk Quota Change Request Form Hopper File Systems Hopper has 5 user file systems which provide different degrees of storage, performance...

  13. Setting up File Permissions

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Setting up File Permissions Setting up File Permissions Recommendations for Setting up "Shared" Directories This section shows the recommended commands for setting up shared directories. Please read the full page to get an understanding of what these commands are doing. These recommendations are based on the common use-case at the JGI for user/group read/write and global read access. Creating a New Shared Directory dmj@genepool04:~$ umask 002 # or have this set in .bashrc.ext

  14. Look At (Search) Large Files

    Energy Science and Technology Software Center (OSTI)

    1992-07-13

    Scanning large files for information can be time consuming and expensive when using edit utilities on large mainframe computers. The reason is that editors must usually load the file into a buffer.

  15. OHA Misc Cases Archive File

    Office of Energy Efficiency and Renewable Energy (EERE)

    This is a archive file of our Misc decisions, Please download this file to your local computer and use the build in adobe search feature. Individual cases are listed in the bookmark section of the...

  16. OHA Whistleblower Cases Archive File

    Office of Energy Efficiency and Renewable Energy (EERE)

    This is a archive file of our Whistleblower decisions, Please download this file to your local computer and use the build in adobe search feature. Individual cases are listed in the bookmark...

  17. OHA Security Cases Archive File

    Office of Energy Efficiency and Renewable Energy (EERE)

    This is a archive file of our Security decisions, Please download this file to your local computer and use the build in adobe search feature. Individual cases are listed in the bookmark section of...

  18. OHA EIA CASES ARCHIVE FILE

    Office of Energy Efficiency and Renewable Energy (EERE)

    This is a archive file of our EIA decisions, Please download this file to your local computer and use the build in adobe search feature. Individual cases are listed in the bookmark section of the...

  19. OHA FOIA Cases Archive File

    Office of Energy Efficiency and Renewable Energy (EERE)

    This is a archive file of our FOIA decisions, Please download this file to your local computer and use the build in adobe search feature. Individual cases are listed in the bookmark section of the...

  20. File storage and I/O

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    File storage and I/O File storage and I/O Disk Quota Change Request Form Franklin File Systems The Franklin system has 4 different file systems mounted which provide different levels of disk storage, I/O performance and file permanence. The table below describes the various Franklin file systems File System Home Local Scratch Project Environment Variable Definition $HOME $SCRATCH $SCRATCH2 No environment variable /project/projectdirs/ Description Global homes file system shared with other NERSC

  1. Algorithms and architectures for high performance analysis of semantic graphs.

    SciTech Connect (OSTI)

    Hendrickson, Bruce Alan

    2005-09-01

    Semantic graphs offer one promising avenue for intelligence analysis in homeland security. They provide a mechanism for describing a wide variety of relationships between entities of potential interest. The vertices are nouns of various types, e.g. people, organizations, events, etc. Edges in the graph represent different types of relationships between entities, e.g. 'is friends with', 'belongs-to', etc. Semantic graphs offer a number of potential advantages as a knowledge representation system. They allow information of different kinds, and collected in differing ways, to be combined in a seamless manner. A semantic graph is a very compressed representation of some of relationship information. It has been reported that the semantic graph can be two orders of magnitude smaller than the processed intelligence data. This allows for much larger portions of the data universe to be resident in computer memory. Many intelligence queries that are relevant to the terrorist threat are naturally expressed in the language of semantic graphs. One example is the search for 'interesting' relationships between two individuals or between an individual and an event, which can be phrased as a search for short paths in the graph. Another example is the search for an analyst-specified threat pattern, which can be cast as an instance of subgraph isomorphism. It is important to note than many kinds of analysis are not relationship based, so these are not good candidates for semantic graphs. Thus, a semantic graph should always be used in conjunction with traditional knowledge representation and interface methods. Operations that involve looking for chains of relationships (e.g. friend of a friend) are not efficiently executable in a traditional relational database. However, the semantic graph can be thought of as a pre-join of the database, and it is ideally suited for these kinds of operations. Researchers at Sandia National Laboratories are working to facilitate semantic graph

  2. MPI File Tree Walk

    Energy Science and Technology Software Center (OSTI)

    2007-04-30

    MPI-FTW is a scalable MPI based software application that navigates a directory tree by dynamically allocating processes to navigate sub-directories found. Upon completion, MPI-FTW provides statistics on the number of directories found, files found, and time to complete. Inaddition, commands can be executed at each directory level.

  3. MEMORANDUfl J: FILE DATE

    Office of Legacy Management (LM)

    J: FILE DATE r so ---...w------m FROM: 9. 34oyc -w---...v----- SUBJECT: D3 Bo;s CL&;C J mL-;+J; - Rcc cap 049 'A :j: &336;s L-.fh w-f L-1 ALE"nirTE ---...

  4. Fast Search for Dynamic Multi-Relational Graphs

    SciTech Connect (OSTI)

    Choudhury, Sutanay; Holder, Larry; Chin, George; Feo, John T.

    2013-06-23

    Acting on time-critical events by processing ever growing social media or news streams is a major technical challenge. Many of these data sources can be modeled as multi-relational graphs. Continuous queries or techniques to search for rare events that typically arise in monitoring applications have been studied extensively for relational databases. This work is dedicated to answer the question that emerges naturally: how can we efficiently execute a continuous query on a dynamic graph? This paper presents an exact subgraph search algorithm that exploits the temporal characteristics of representative queries for online news or social media monitoring. The algorithm is based on a novel data structure called the that leverages the structural and semantic characteristics of the underlying multi-relational graph. The paper concludes with extensive experimentation on several real-world datasets that demonstrates the validity of this approach.

  5. Mining Large Heterogeneous Graphs using Cray s Urika

    SciTech Connect (OSTI)

    Sukumar, Sreenivas R; Bond, Nathaniel A

    2013-01-01

    Pattern discovery and predictive modeling from seemingly related Big Data represented as massive, ad-hoc, heterogeneous networks (e.g., extremely large graphs with complex, possibly unknown structure) is an outstanding problem in many application domains. To address this problem, we are designing graph-mining algorithms capable of discovering relationship-patterns from such data and using those discovered patterns as features for classification and predictive modeling. Specifically, we are: (i) exploring statistical properties, mechanics and generative models of behavior patterns in heterogeneous information networks, (ii) developing novel, automated and scalable graph-pattern discovery algorithms and (iii) applying our relationship-analytics (data science + network science) expertise to domains spanning healthcare to homeland security.

  6. On the mixing time of geographical threshold graphs

    SciTech Connect (OSTI)

    Bradonjic, Milan

    2009-01-01

    In this paper, we study the mixing time of random graphs generated by the geographical threshold graph (GTG) model, a generalization of random geometric graphs (RGG). In a GTG, nodes are distributed in a Euclidean space, and edges are assigned according to a threshold function involving the distance between nodes as well as randomly chosen node weights. The motivation for analyzing this model is that many real networks (e.g., wireless networks, the Internet, etc.) need to be studied by using a 'richer' stochastic model (which in this case includes both a distance between nodes and weights on the nodes). We specifically study the mixing times of random walks on 2-dimensional GTGs near the connectivity threshold. We provide a set of criteria on the distribution of vertex weights that guarantees that the mixing time is {Theta}(n log n).

  7. File:01UTALandUsePlanning.pdf | Open Energy Information

    Open Energy Info (EERE)

    Datasets Community Login | Sign Up Search File Edit History File:01UTALandUsePlanning.pdf Jump to: navigation, search File File history File usage Metadata File:01UTALandUsePlan...

  8. File:01CAALandUsePlanning.pdf | Open Energy Information

    Open Energy Info (EERE)

    File Edit History File:01CAALandUsePlanning.pdf Jump to: navigation, search File File history File usage Metadata File:01CAALandUsePlanning.pdf Size of this preview: 463 599...

  9. File:01IDALandUseConsiderations.pdf | Open Energy Information

    Open Energy Info (EERE)

    File Edit History File:01IDALandUseConsiderations.pdf Jump to: navigation, search File File history File usage Metadata File:01IDALandUseConsiderations.pdf Size of this preview:...

  10. File:08COCStateTransmissionProcess.pdf | Open Energy Information

    Open Energy Info (EERE)

    Community Login | Sign Up Search File Edit History File:08COCStateTransmissionProcess.pdf Jump to: navigation, search File File history File usage Metadata File:08COCStateTransm...

  11. File:01MTALandUseConsiderations.pdf | Open Energy Information

    Open Energy Info (EERE)

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  12. File:03COCEncroachmentOverview.pdf | Open Energy Information

    Open Energy Info (EERE)

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  13. File:0 - Overall Flow (Solar).pdf | Open Energy Information

    Open Energy Info (EERE)

    File Edit History File:0 - Overall Flow (Solar).pdf Jump to: navigation, search File File history File usage Metadata File:0 - Overall Flow (Solar).pdf Size of this preview: 463 ...

  14. DOE - Fossil Energy: Introduction Page to E-Filing System

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    E-Filing Natural Gas Import & Export Regulation - E-Filing DOE's Natural Gas E-Filing System More Info E-Filing Instructions Go directly to e-Filing Form View Sample e-File...

  15. File:WSR flowchart-introduction.pdf | Open Energy Information

    Open Energy Info (EERE)

    Apps Datasets Community Login | Sign Up Search File Edit History File:WSR flowchart-introduction.pdf Jump to: navigation, search File File history File usage File:WSR...

  16. Detailed Drawings of NERSC File Systems

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    File System Drawing Detailed Drawings of NERSC File Systems Cori Global Scratch cori scratch Global Project project Abbreviations CMP Chip Multicore Processor OSS Object Storage Server: a component of a Lustre File System OST Object Storage Target: a component of a Lustre File System LNET Lustre Network router MDS Metadata Server, manage file operation, e.g., create new file, write to shared file DVS Data Virtualization Server, Running a service to mount external storage to Cray systems HPSS

  17. File Storage and I/O

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    File Storage and I/O File Storage and I/O Disk Quota Change Request Form Hopper File Systems Hopper has 5 user file systems which provide different degrees of storage, performance and permanence. The table below summarizes these file systems: File System Home Local Scratch Global Scratch Project Environment Variable Definition $HOME $SCRATCH $SCRATCH2 $GSCRATCH None. Must use /project/projectdirs/ Description Global home file system shared with other NERSC systems. All NERSC machines mount the

  18. Original Signature On File

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Signature On File Department of Energy Carl sbad Field Office P. O. Box 3090 Carlsbad , New Mexico 8822 1 June 10, 2009 Mr. Steve Zappe, Project Leader Hazardous Waste Bureau New Mexico Environment Department 2905 Rodeo Park Drive East, Building 1 Santa Fe, New Mexico 87505-6303 Subject: Transmittal of CBFO Final Audit Report A-09-12 , Los Alamos National Laboratory Central Characterization Project TRU Waste Characterization and Certification Dear Mr. Zappe: Enclosed is the Carlsbad Field Office

  19. File:08MTATransmission (3).pdf | Open Energy Information

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  20. File:07FDCPURPAQualifyingFacilityCertificationProcess.pdf | Open...

    Open Energy Info (EERE)

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  1. File:07TXBRECGeneratorCertification.pdf | Open Energy Information

    Open Energy Info (EERE)

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  2. File:04AKBGeophysicalExplorationPermit.pdf | Open Energy Information

    Open Energy Info (EERE)

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  3. File:07FDBPlantCommissioning.pdf | Open Energy Information

    Open Energy Info (EERE)

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  4. File:07HIAGeothermalAndCableSystemDevelopmentPermitting.pdf ...

    Open Energy Info (EERE)

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  5. File:ApplicationtoAppropriate.pdf | Open Energy Information

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  6. File:08CABCaliforniaTransmissionCPUCProcess.pdf | Open Energy...

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  7. File:AlaskaTitleVApplicationSubmittalInstructions.pdf | Open...

    Open Energy Info (EERE)

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  8. File:03ORENoncompetitiveGeothermalLease.pdf | Open Energy Information

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  9. File:07CAAPlantCommissioningProcessApplicationForCertification...

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  10. File:08IDAStateTransmission.pdf | Open Energy Information

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  11. File:03AKCEncroachmentOverview.pdf | Open Energy Information

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  12. File:06ORATransportationPermit.pdf | Open Energy Information

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  13. File:06NVATransportationPermit.pdf | Open Energy Information

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  14. File:08CAACaliforniaTransmission.pdf | Open Energy Information

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  15. File:06AKATransportationOversizeOverweight.pdf | Open Energy...

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  16. File:07CACCaliforniaEnergyFacilityCPUCProcess.pdf | Open Energy...

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  17. File:03CACEncroachmentPermit.pdf | Open Energy Information

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  18. File:06FDAEPAConstructionGeneralPermitConstructionStormwater...

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  19. File:07ORDExpeditedPlantCommissioningProcess.pdf | Open Energy...

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  20. File:03IDCEncroachmentPermit.pdf | Open Energy Information

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  1. File:0 - Overall Flow - Transmission.pdf | Open Energy Information

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  2. File:Federal Hydropower - Southwestern Power Administration.pdf...

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  3. File:Federal Hydropower - Western Area Power Administration.pdf...

    Open Energy Info (EERE)

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  4. File:Texas Construction General Permit (TXR150000).pdf | Open...

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  5. File:03NVCEncroachment (1).pdf | Open Energy Information

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  6. Data Storage & File Systems | Argonne Leadership Computing Facility

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    continue to build documentation for our new computing resource. Feedback Form Data Storage & File Systems BGQ File Systems BGQ File Systems: An overview of the BGQ file...

  7. File:(PECC) Special Program on Climate Change SUMMARY (english...

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  8. File:CDPHE Industrial Individual Wastewater Discharge Permit...

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  9. File:06COBConstructionStormWaterPermit.pdf | Open Energy Information

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    Login | Sign Up Search File Edit History File:06COBConstructionStormWaterPermit.pdf Jump to: navigation, search File File history File usage Metadata File:06COBConstructio...

  10. Integrated Network Decompositions and Dynamic Programming for Graph Optimization (INDDGO)

    Energy Science and Technology Software Center (OSTI)

    2012-05-31

    The INDDGO software package offers a set of tools for finding exact solutions to graph optimization problems via tree decompositions and dynamic programming algorithms. Currently the framework offers serial and parallel (distributed memory) algorithms for finding tree decompositions and solving the maximum weighted independent set problem. The parallel dynamic programming algorithm is implemented on top of the MADNESS task-based runtime.

  11. In-Memory Graph Databases for Web-Scale Data

    SciTech Connect (OSTI)

    Castellana, Vito G.; Morari, Alessandro; Weaver, Jesse R.; Tumeo, Antonino; Haglin, David J.; Villa, Oreste; Feo, John

    2015-03-01

    RDF databases have emerged as one of the most relevant way for organizing, integrating, and managing expo- nentially growing, often heterogeneous, and not rigidly structured data for a variety of scientific and commercial fields. In this paper we discuss the solutions integrated in GEMS (Graph database Engine for Multithreaded Systems), a software framework for implementing RDF databases on commodity, distributed-memory high-performance clusters. Unlike the majority of current RDF databases, GEMS has been designed from the ground up to primarily employ graph-based methods. This is reflected in all the layers of its stack. The GEMS framework is composed of: a SPARQL-to-C++ compiler, a library of data structures and related methods to access and modify them, and a custom runtime providing lightweight software multithreading, network messages aggregation and a partitioned global address space. We provide an overview of the framework, detailing its component and how they have been closely designed and customized to address issues of graph methods applied to large-scale datasets on clusters. We discuss in details the principles that enable automatic translation of the queries (expressed in SPARQL, the query language of choice for RDF databases) to graph methods, and identify differences with respect to other RDF databases.

  12. STRUCTURAL ANNOTATION OF EM IMAGES BY GRAPH CUT

    SciTech Connect (OSTI)

    Chang, Hang; Auer, Manfred; Parvin, Bahram

    2009-05-08

    Biological images have the potential to reveal complex signatures that may not be amenable to morphological modeling in terms of shape, location, texture, and color. An effective analytical method is to characterize the composition of a specimen based on user-defined patterns of texture and contrast formation. However, such a simple requirement demands an improved model for stability and robustness. Here, an interactive computational model is introduced for learning patterns of interest by example. The learned patterns bound an active contour model in which the traditional gradient descent optimization is replaced by the more efficient optimization of the graph cut methods. First, the energy function is defined according to the curve evolution. Next, a graph is constructed with weighted edges on the energy function and is optimized with the graph cut algorithm. As a result, the method combines the advantages of the level set method and graph cut algorithm, i.e.,"topological" invariance and computational efficiency. The technique is extended to the multi-phase segmentation problem; the method is validated on synthetic images and then applied to specimens imaged by transmission electron microscopy(TEM).

  13. Policy Flash Archive Search File

    Office of Environmental Management (EM)

    ... Policy Flash Archive Search File 4 Flash2002-10 ... Principles; 2) Contract Terms and Conditions Required to ... Transaction Authority to enter into Technology Investment ...

  14. 1999 CBECS Public Use Files

    U.S. Energy Information Administration (EIA) Indexed Site

    only. Geographic identifiers and National Oceanic and Atmospheric Administration Weather Division identifiers are not included on any data files delivered to EIA. Geographic...

  15. J319.xls

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    MISO Project Number J319 Point of Interconnection Entergy AR ANO-Pleasant Hill 500 kV line ... Page 1 of 3 February 10, 2014 MISO Project Number J319 Point of Interconnection Holland ...

  16. a8.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    ......... 6,922 5,613 1,028 Q Q N 223 5,001 to 10,000 ...... 7,033 5,304 1,383 Q N Q Q 10,001 to 25,000 ......

  17. eia-910.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Indicate unit of measure by placing an "X" in the appropriate box. Commercial Residential ... Address 1: OOG.SURVEYS@eia.gov Contact Name: Fax: (202) 586-1076 Ext: Fax No.: enter an "X...

  18. nstec_home.xls

    National Nuclear Security Administration (NNSA)

    1 11767 1 11772 1 11778 1 11787 1 12144 1 12170 1 12189 1 12569 1 14625 1 NY Total 20 OK 73044 1 OK Total 1 PA 17302 1 PA Total 1 SC 29715 1 29909 1 SC Total 2 TN 37604 1 37722...

  19. c30.xls

    Gasoline and Diesel Fuel Update (EIA)

    27.3 Building Floorspace (Square Feet) 1,001 to 5,000 ... 56 81 35 55 16 660 979 421 789 234 85.0 82.9 82.5 69.8 66.6 5,001 to 10,000...

  20. c26.xls

    Gasoline and Diesel Fuel Update (EIA)

    3,553 4,844 3,866 2,261 8.56 7.09 8.40 7.28 0.39 0.37 0.29 0.29 Building Floorspace (Square Feet) 1,001 to 5,000 ... 456 782 599 317 9.84 8.57 9.21...

  1. c21.xls

    Gasoline and Diesel Fuel Update (EIA)

    Q 14.5 18.7 Buildings without Cooling ... 11 8 Q 2,142 2,757 Q 5.2 2.8 7.7 Water-Heating Energy Sources Electricity ... 88 163...

  2. c15.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    without Cooling ... 7 Q 3 6 1,855 2,232 1,214 1,080 3.6 6.4 2.6 5.8 Water-Heating Energy Sources Electricity ... 57 86...

  3. c14.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    0.069 Buildings without Cooling ... 39 4.8 11.8 1.1 2.4 5.1 3.2 0.39 0.082 Water-Heating Energy Sources Electricity ... 211...

  4. c20.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    without Cooling ... 7 Q 1 5 Q 1,843 2,567 430 1,195 Q 4.0 6.3 3.0 4.1 Q Water-Heating Energy Sources Electricity ... 43 88 77...

  5. c22.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    19.1 Buildings without Cooling ... Q 8 4 3,308 1,832 1,241 5.7 4.4 2.9 Water-Heating Energy Sources Electricity ... 51 216...

  6. c16.xls

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    without Cooling ... 741 Q 279 708 0.11 0.05 0.09 0.11 0.40 0.33 0.23 0.66 Water-Heating Energy Sources Electricity ... 5,313...

  7. OMBDOEFAIR2005.xls

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    NV Las Vegas US 1 Y515 C B 2005 7049 019 05 NV NNSA NV Las Vegas US 1 Y550 C B 1999 7050 019 05 NV NNSA NV NTS Area 6 US 1 Y999 C B 1999 7051 019 20 OE DC Washington US 1 R110...

  8. natgas1980.xls

    Gasoline and Diesel Fuel Update (EIA)

    Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total U.S. Households 51.6 39.7 88.5 125 56 96.2 34 497 0.22 383 137 Census Region and Division Northeast 10.9 6.5 18.8 144 50 86.6 31 771 0.27 463 168 New England 1.9 0.9 3.1 162 47 78.9 28 971 0.28 472 169 Middle Atlantic 9.0 5.6 15.7 141 51 88.1 32 739 0.27 461 168 Midwest 15.5 12.4 29.4 164 70 131.6 46 586 0.25 470 165

  9. oil1980.xls

    Gasoline and Diesel Fuel Update (EIA)

    5.4 11.6 29.7 131 51 99.0 36 1,053 0.41 795 287 Census Region and Division Northeast 9.2 6.0 18.2 176 59 116.2 42 1,419 0.47 934 335 New England 2.7 2.0 6.0 161 53 118.3 42 1,297 0.43 954 336 Middle Atlantic 6.5 4.1 12.2 184 61 115.3 42 1,478 0.49 926 335 Midwest 2.0 1.9 4.4 92 39 84.5 28 728 0.31 669 220 East North Central 1.5 1.4 3.3 92 39 84.4 28 731 0.31 673 220 West North Central 0.5 0.5 1.1 93 40 85.0 29 720 0.31 657 220 South 3.6 3.2 6.0 79 42 68.8 26 637 0.34 558 214 South Atlantic 3.5

  10. b1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    1 Number of Buildings (thousand) Total Floorspace (million square feet) Total Workers in All Buildings (thousand) Mean Square Feet per Building (thousand) Mean Square Feet per Worker Mean Hours per Week All Buildings*................................... 4,645 64,783 72,807 13.9 890 61 Table B1. Summary Table: Total and Means of Floorspace, Number of Workers, and Hours of Operation for Non-Mall Buildings, 2003 Climate Zone: 30-Year Average Under 2,000 CDD and -- More than 7,000 HDD

  11. b1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Revised June 2006 15 Released: Dec 2006 Next CBECS will be conducted in 2007 Number of Buildings (thousand) Total Floorspace (million square feet) Total Workers in All Buildings (thousand) Mean Square Feet per Building (thousand) Mean Square Feet per Worker Mean Hours per Week All Buildings*................................... 4,645 64,783 72,807 13.9 890 61 Table B1. Summary Table: Total and Means of Floorspace, Number of Workers, and Hours of Operation for Non-Mall Buildings, 2003 Climate Zone:

  12. b1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    All Buildings RSEs for Total Floorspace RSEs for Total Workers in All Buildings RSEs for Mean Square Feet per Building RSEs for Mean Square Feet per Worker RSEs for Mean Hours per Week All Buildings*................................... 3.9 3.1 5.6 4.1 5.4 2.0 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 5.7 5.6 6.0 1.3 4.5 3.3 5,001 to 10,000 ................................. 5.8 5.6 8.8 0.9 8.0 4.1 10,001 to 25,000 ............................... 5.0 5.0

  13. b1.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Released: Dec 2006 Next CBECS will be conducted in 2007 Number of Buildings (thousand) Total Floorspace (million square feet) Total Workers in All Buildings (thousand) Mean Square Feet per Building (thousand) Mean Square Feet per Worker Mean Hours per Week All Buildings*................................... 4,645 64,783 72,807 13.9 890 61 Table B1. Summary Table: Total and Means of Floorspace, Number of Workers, and Hours of Operation for Non-Mall Buildings, 2003 Climate Zone: 30-Year Average

  14. b10.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    One Floor Two Floors Three Floors Four to Nine Floors Ten or More Floors All Build- ings* One Floor Two Floors Three Floors Four to Nine Floors Ten or More Floors All Buildings* .................................. 4,645 3,136 1,031 339 128 12 64,783 25,981 16,270 7,501 10,085 4,947 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 2,014 411 115 Q N 6,789 5,192 1,217 343 Q N 5,001 to 10,000 ................................. 889 564 239 70 Q N 6,585 4,150

  15. b11.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Table B11. Selected Principal Building Activity: Part 1, Number of Buildings for Non- Mall Buildings, 2003 Principal Building Activity Number of Buildings (thousand) Health Care All Buildings* Education Food Sales Food Service Lodging Retail (Other Than Mall) Energy Information Administration 2003 Commercial Buildings Energy Consumption Survey: Building Characteristics Tables Revised June 2006 81 Released: June 2006 Next CBECS will be conducted in 2007 Inpatient Outpatient All Buildings*

  16. b11.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Lodging Retail (Other Than Mall) Table B11. Selected Principal Building Activity: Part 1, Number of Buildings for Non- Mall Buildings, 2003 Principal Building Activity Number of Buildings (thousand) Health Care All Buildings* Education Food Sales Food Service Energy Information Administration 2003 Commercial Buildings Energy Consumption Survey: Building Characteristics Tables Released: June 2006 Next CBECS will be conducted in 2007 Inpatient Outpatient All Buildings*

  17. b12.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 9,874 1,255 1,654 1,905 1,258 5,096 4,317 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 409 409 544 N 165 99 638 5,001 to 10,000 ................................. 6,585 399 356 442 N 280 160 725 10,001 to 25,000 ............................... 11,535 931 Q 345 Q 312 631 1,284 25,001 to 50,000 ............................... 8,668 1,756 Q Q Q Q 803 578 50,001 to 100,000 ............................. 9,057 2,690 Q Q Q 206 841 Q 100,001 to 200,000

  18. b13.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    4,645 824 277 71 370 622 597 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 503 119 37 152 434 294 5,001 to 10,000 ................................. 889 127 67 Q 104 100 110 10,001 to 25,000 ............................... 738 116 69 Q 83 66 130 25,001 to 50,000 ............................... 241 43 9 Q 27 17 27 50,001 to 100,000 ............................. 129 17 7 Q Q Q 21 100,001 to 200,000 ........................... 65 11 6 Q Q Q 8 200,001 to

  19. b14.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 12,208 3,939 1,090 3,754 4,050 10,078 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 1,382 336 122 416 1,034 895 5,001 to 10,000 ................................. 6,585 938 518 Q 744 722 868 10,001 to 25,000 ............................... 11,535 1,887 1,077 Q 1,235 1,021 2,064 25,001 to 50,000 ............................... 8,668 1,506 301 Q 930 560 1,043 50,001 to 100,000 ............................. 9,057 1,209 474 Q Q Q 1,494 100,001 to

  20. b15.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Revised June 2006 105 Released: Dec 2006 Next CBECS will be conducted in 2007 Fewer than 5 Workers 5 to 9 Workers 10 to 19 Workers 20 to 49 Workers 50 to 99 Workers 100 to 249 Workers 250 or More Workers All Buildings* .................................. 4,645 2,653 778 563 398 147 77 30 Table B15. Employment Size Category, Number of Buildings for Non-Mall Buildings, 2003 All Buildings* Number of Workers Number of Buildings (thousand) Number of Floors One

  1. b15.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Fewer than 5 Workers 5 to 9 Workers 10 to 19 Workers 20 to 49 Workers 50 to 99 Workers 100 to 249 Workers 250 or More Workers All Buildings* .................................. 4,645 2,653 778 563 398 147 77 30 Table B15. Employment Size Category, Number of Buildings for Non-Mall Buildings, 2003 All Buildings* Number of Workers Number of Buildings (thousand) Number of Floors One ................................................... 3,136 2,005 515 333 198 69 13 3 Two

  2. b16.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 15,492 6,166 7,803 10,989 7,934 6,871 9,528 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 4,659 1,264 689 155 Q Q N 5,001 to 10,000 ................................. 6,585 3,323 1,373 1,109 689 Q Q N 10,001 to 25,000 ............................... 11,535 4,006 2,075 2,456 2,113 692 Q N 25,001 to 50,000 ............................... 8,668 1,222 836 1,327 2,920 1,648 667 Q 50,001 to 100,000 ............................. 9,057 704 291 1,157

  3. b17.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    4,645 4,011 1,841 2,029 141 635 46 164 425 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 2,272 980 1,205 87 280 Q 77 183 5,001 to 10,000 ................................. 889 783 384 375 Q 106 Q Q 87 10,001 to 25,000 ............................... 738 625 320 293 Q 113 Q 40 64 25,001 to 50,000 ............................... 241 185 91 86 Q 56 Q 16 36 50,001 to 100,000 ............................. 129 82 35 40 Q 47 Q 9 37 100,001 to 200,000

  4. b18.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 49,421 23,591 23,914 1,916 15,363 1,956 3,808 9,599 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 6,043 2,682 3,162 199 746 Q 206 498 5,001 to 10,000 ................................. 6,585 5,827 2,858 2,791 Q 758 Q Q 620 10,001 to 25,000 ............................... 11,535 9,738 5,028 4,530 Q 1,797 Q 604 1,044 25,001 to 50,000 ............................... 8,668 6,659 3,197 3,141 Q 2,009 Q 531 1,327 50,001 to 100,000

  5. b19.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    4,645 3,754 643 55 23 14 157 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 2,131 311 Q Q N 100 5,001 to 10,000 ................................. 889 720 136 Q N Q Q 10,001 to 25,000 ............................... 738 590 104 22 Q Q Q 25,001 to 50,000 ............................... 241 163 50 11 Q Q Q 50,001 to 100,000 ............................. 129 87 25 4 5 Q Q 100,001 to 200,000 ........................... 65 43 11 4 Q Q Q 200,001 to 500,000

  6. b2.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Total Workers in All Buildings (thousand) Median Square Feet per Building (thousand) Median Square Feet per Worker Median Hours per Week Median Age of Buildings (years) All Buildings* .................................. 4,645 64,783 72,807 4.6 1,000 50 30.5 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 6,789 9,936 2.4 750 48 30.5 5,001 to 10,000 ................................. 889 6,585 7,512 7.2 1,300 50 30.5 10,001 to 25,000

  7. b20.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 45,144 10,960 1,958 1,951 2,609 2,161 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 5,613 916 Q Q N 223 5,001 to 10,000 ................................. 6,585 5,304 1,031 Q N Q Q 10,001 to 25,000 ............................... 11,535 9,098 1,732 383 Q Q Q 25,001 to 50,000 ............................... 8,668 5,807 1,837 355 Q Q Q 50,001 to 100,000 ............................. 9,057 6,218 1,739 273 337 Q Q 100,001 to 200,000

  8. b21.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Buildings With Central Physical Plant All Buildings With Central Physical Plant All Buildings* .................................. 4,645 1,477 116 64,783 24,735 6,604 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 2,552 771 Q 6,789 2,009 Q 5,001 to 10,000 ................................. 889 259 Q 6,585 1,912 Q 10,001 to 25,000 ............................... 738 263 33 11,535 4,158 520 25,001 to 50,000 ............................... 241 92 18 8,668 3,277

  9. b22.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    Revised June 2006 144 Released: Dec 2006 Next CBECS will be conducted in 2007 Elec- tricity Natural Gas Fuel Oil District Heat District Chilled Water Propane Other a All Buildings* .................................. 4,645 4,414 4,404 2,391 451 67 33 502 132 Table B22. Energy Sources, Number of Buildings for Non-Mall Buildings, 2003 Number of Buildings (thousand) Energy Sources Used (more than one may apply) All Buildings* Buildings Using Any Energy Source Number of Workers (main shift) Fewer

  10. b23.xls

    U.S. Energy Information Administration (EIA) Indexed Site

    64,783 63,343 63,307 43,468 15,157 5,443 2,853 7,076 1,401 Building Floorspace (Square Feet) 1,001 to 5,000 ................................... 6,789 6,362 6,346 3,084 600 Q Q 806 199 5,001 to 10,000 ................................. 6,585 6,212 6,197 3,692 716 Q Q 725 Q 10,001 to 25,000 ............................... 11,535 11,370 11,370 7,053 966 289 Q 1,014 Q 25,001 to 50,000 ............................... 8,668 8,385 8,385 6,025 825 369 240 638 Q 50,001 to 100,000