LAW: A Workbench for Approximate Pattern Matching in Relational Data

Michael Wolverton, Pauline Berry, Ian Harrison, John Lowrance, David Morley, Andres Rodriguez, Enrique Ruspini, and Jerome Thomere

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 collection of relational data, and manipulating partial results. This paper describes two key facets of the LAW system: (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.


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