U.S. Department of Energy

Pacific Northwest National Laboratory

Toward Usable Interactive Analytics: Coupling Cognition and Computation

Publish Date: 
Monday, July 21, 2014
Interactive analytics provide users a myriad of computational means to aid in extracting meaningful information from large and complex datasets. Much prior work focuses either on advancing the capabilities of machine-centric approaches by the data mining and machine learning communities, or human-driven methods by the visualization and CHI communities. However, these methods do not yet support a true human-machine symbiotic relationship where users and machines work together collaboratively and adapt to each other to advance an interactive analytic process. In this paper we discuss some of the inherent issues, outlining what we believe are the steps toward usable interactive analytics that will ultimately increase the effectiveness for both humans and computers to produce insights.
Endert A, C North, R Chang, and M Zhou. 2014. "Toward Usable Interactive Analytics: Coupling Cognition and Computation." In ACM KDD 2014 Workshop on Interactive Data Exploration and Analytics (IDEA).
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