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Title: Data Management Architectures

Abstract

Data management is the organization of information to support efficient access and analysis. For data intensive computing applications, the speed at which relevant data can be accessed is a limiting factor in terms of the size and complexity of computation that can be performed. Data access speed is impacted by the size of the relevant subset of the data, the complexity of the query used to define it, and the layout of the data relative to the query. As the underlying data sets become increasingly complex, the questions asked of it become more involved as well. For example, geospatial data associated with a city is no longer limited to the map data representing its streets, but now also includes layers identifying utility lines, key points, locations and types of businesses within the city limits, tax information for each land parcel, satellite imagery, and possibly even street-level views. As a result, queries have gone from simple questions, such as "how long is Main Street?", to much more complex questions such as "taking all other factors into consideration, are the property values of houses near parks higher than those under power lines, and if so, by what percentage". Answering these questions requiresmore » a coherent infrastructure, integrating the relevant data into a format optimized for the questions being asked.« less

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1054848
Report Number(s):
PNNL-SA-84640
KJ0403000
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Book
Resource Relation:
Related Information: Data Intensive Computing: Architectures, Algorithms, and Applications, 48-84
Country of Publication:
United States
Language:
English

Citation Formats

Critchlow, Terence J., Abdulla, Ghaleb, Becla, Jacek, Kleese van Dam, Kerstin, Lang, Sam, and McGuinness, Deborah L. Data Management Architectures. United States: N. p., 2012. Web. doi:10.1017/CBO9780511844409.004.
Critchlow, Terence J., Abdulla, Ghaleb, Becla, Jacek, Kleese van Dam, Kerstin, Lang, Sam, & McGuinness, Deborah L. Data Management Architectures. United States. doi:10.1017/CBO9780511844409.004.
Critchlow, Terence J., Abdulla, Ghaleb, Becla, Jacek, Kleese van Dam, Kerstin, Lang, Sam, and McGuinness, Deborah L. Wed . "Data Management Architectures". United States. doi:10.1017/CBO9780511844409.004.
@article{osti_1054848,
title = {Data Management Architectures},
author = {Critchlow, Terence J. and Abdulla, Ghaleb and Becla, Jacek and Kleese van Dam, Kerstin and Lang, Sam and McGuinness, Deborah L.},
abstractNote = {Data management is the organization of information to support efficient access and analysis. For data intensive computing applications, the speed at which relevant data can be accessed is a limiting factor in terms of the size and complexity of computation that can be performed. Data access speed is impacted by the size of the relevant subset of the data, the complexity of the query used to define it, and the layout of the data relative to the query. As the underlying data sets become increasingly complex, the questions asked of it become more involved as well. For example, geospatial data associated with a city is no longer limited to the map data representing its streets, but now also includes layers identifying utility lines, key points, locations and types of businesses within the city limits, tax information for each land parcel, satellite imagery, and possibly even street-level views. As a result, queries have gone from simple questions, such as "how long is Main Street?", to much more complex questions such as "taking all other factors into consideration, are the property values of houses near parks higher than those under power lines, and if so, by what percentage". Answering these questions requires a coherent infrastructure, integrating the relevant data into a format optimized for the questions being asked.},
doi = {10.1017/CBO9780511844409.004},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2012},
month = {10}
}

Book:
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