DIMACS TR: 2009-12

Supporting Cognitive Models of Sensemaking in Analytics Systems

Authors: Jason Perry, William M. Pottenger, Chinua Umoja and Christopher Janneck


Cognitive science is beginning to provide us with well-supported models of the stages that professional analysts go through in the course of conducting an investigation, be it reactive or proactive in nature. These process models are generally advanced within the field of Sensemaking, because the analyst’s primary task can be viewed as "making sense" of a large body of unorganized information. One of the most well-known long-term investigations into the structure of Sensemaking is that of Pirolli and Card et al. Their resulting model provides an initial basis for our research. In using these models to improve analytics systems, we have at least two distinct problems: (1) how to infer high-level knowledge of the Sensemaking states from a record of user interactions with an interactive analysis system, and (2) how to use this knowledge to provide user guidance that results in better human-machine interaction and a more robust investigative process. The answers to these questions lie at the intersection of research in machine learning, knowledge representation, user interfaces and cognitive science, and addressing them requires an end-to-end system perspective. In this report, we survey these problems and discuss our initial approaches. We describe the description logic we have developed to model the problem domain and define a set of machine learning tasks. Then we present our initial user interface design, and then the design of the initial experiments, including the ground truth which is from an actual solved crime case. We conclude with the insights gained thus far into building interactive systems that support users’ cognitive models.

Paper Available at: http://dimacs.rutgers.edu/TechnicalReports/TechReports/2009/2009-12.pdf
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