Bibliographic Citation
| Document |  340 K |
|---|---|
| DOI | 10.2172/891627 |
| Title | Interactive Analysis of Large Network Data Collections UsingQuery-Driven Visualization |
| Creator/Author | Bethel, E. Wes ; Campbell, Scott ; Dart, Eli ; Lee, Jason ; Smith,Steven A. ; Stockinger, Kurt ; Tierney, Brian ; Wu, Kesheng |
| Publication Date | 2005 Dec 01 |
| OSTI Identifier | OSTI ID: 891627 |
| Report Number(s) | LBNL--59166 |
| DOE Contract Number | DE-AC02-05CH11231 |
| Other Number(s) | R&D Project: K11107; Other: BnR: KJ0101030; TRN: US200622%%269 |
| Resource Type | Technical Report |
| Research Org | Ernest Orlando Lawrence Berkeley NationalLaboratory, Berkeley, CA (US) |
| Sponsoring Org | USDOE Director. Office of Science. Advanced ScientificComputing Research; Department of Homeland Security NationalVisualization and Analytics Center, National Nuclear SecurityAdministration |
| Subject | 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; DATA ANALYSIS; HYPOTHESIS; MANAGEMENT; PERFORMANCE; TESTING |
| Related Subject | interactive data exploration and discovery multivariatevisualization security traffic analysis query-drivenvisualization |
| Description/Abstract | Realizing operational analytics solutions where large and complex data must be analyzed in a time-critical fashion entails integrating many different types of technology. Considering the extreme scale of contemporary datasets, one significant challenge is to reduce the duty cycle in the analytics discourse process. This paper focuses on an interdisciplinary combination of scientific data management and visualization/analysis technologies targeted at reducing the duty cyclein hypothesis testing and knowledge discovery. We present an application of such a combination in the problem domain of network traffic data analysis. Our performance experiment results, including both serial and parallel scalability tests, show that the combination can dramatically decrease the analytics duty cycle for this particular application. The combination is effectively applied to the analysis of network traffic data to detect slow and distributed scans, which is a difficult-to-detect form of cyber attack. Our approach is sufficiently general to be applied to a diverse set of data understanding problems as well as used in conjunction with a diverse set of analysis and visualization tools. |
| Country of Publication | United States |
| Language | English |
| Format | Medium: ED |
| System Entry Date | 2008 Feb 05 |
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Last Updated: 11/19/2009