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Generalized Graph Matching for Data Mining and Information Retrieval
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Visual query and exploration system for temporal relational database
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Coupled behavior analysis for capturing coupling relationships in group-based market manipulations
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Data Mining and Knowledge Discovery
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qGraph is a visual query language for knowledge discovery in relational data. Using qGraph, a user can query and update relational data in ways that support data exploration, data transformation, and sampling. When combined with modeling algorithms, such as those developed in inductive logic programming and relational learning, the language assists analysis of relational data, such as data drawn from the Web, chemical structure-activity relationships, and social networks. Several features distinguish qGraph from other query languages such as SQL and Datalog. It is a visual language, so its queries are annotated graphs that reflect potential structures within a database. qGraph treats objects, links, and attributes as first-class entities, so its queries can dynamically alter a data schema by adding and deleting those entities. Finally, the language provides grouping and counting constructs that facilitate calculation of attributes that can capture features of local graph structure. We describe the language in detail, discuss key aspects of the underlying data model and implementation, and discuss several uses of qGraph for knowledge discovery.