Capturing and Using Patterns for Evidence Detection:
Papers from the AAAI Fall Symposium
Ken Murray and Ian Harrison, Cochairs
Pattern-based analysis of data plays an increasing role in several important applications. In crime prevention (including securities trading, tax fraud, and homeland security) it is being used both to detect evidence of criminal events and to predict threatening activities before they completely mature. In marketing it is being used to assess trends in the aggregate sentiments of populations as well as the preferences of individuals. In epidemiology it is used to assess health trends in populations and provide early warning of epidemics. In these applications the data is typically incomplete and becomes available incrementally over time, and it can often support alternative interpretations, so assessing the quality of the evolving evidence among a set of competing hypotheses is critical. This symposium brought together researchers from diverse backgrounds, including machine learning, data management, graph theory, link analysis, information retrieval, privacy, automated reasoning, and knowledge representation, to promote advances in acquiring and using patterns for detecting and managing evidence in data.