Name
Abstract | ||
|---|---|---|
Pattern matching for intelligence organizations is a challenging problem. The data sets are large and noisy, and there is a flexible and constantly changing notion of what constitutes a match. We are developing the Link Analysis Workbench (LAW) to assist an expert user in the intelligence community in creating and maintaining patterns, matching those patterns against a large collec- tion of relational data, and manipulating partial results. This paper describes two key facets of the LAW sys- tem: (1) a pattern-matching module based on a graph edit distance metric, and (2) a system architecture that supports the integration and tasking of multiple pattern matching modules based on their capabilities and the specific problem at hand. |
Year | Venue | Keywords |
|---|---|---|
2003 | IAAI | link analysis,system architecture,pattern matching,relational data,edit distance |
Field | DocType | Citations |
Data mining,Data set,Relational database,Link analysis,Computer science,Theoretical computer science,Artificial intelligence,Systems architecture,Law,Workbench,Pattern matching,Machine learning,Graph edit distance | Conference | 16 |
PageRank | References | Authors |
1.63 | 13 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Wolverton | 1 | 264 | 28.16 |
| Pauline M. Berry | 2 | 185 | 14.34 |
| Ian W. Harrison | 3 | 28 | 4.96 |
| John D. Lowrance | 4 | 215 | 185.69 |
| David Morley | 5 | 285 | 97.07 |
| Andres C. Rodriguez | 6 | 29 | 5.69 |
| Enrique H. Ruspini | 7 | 1140 | 372.49 |
| Jérôme Thoméré | 8 | 78 | 8.43 |