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Context-Enhanced Information Fusion pp 231–267Cite as

Modeling User Behaviors to Enable Context-Aware Proactive Decision Support

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Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

The problem of automatically recognizing a user’s operational context, the implications of its shifting properties, and reacting in a dynamic manner are at the core of mission intelligence and decision making. Environments such as the OZONE Widget Framework (http://www.owfgoss.org) (OWF) provide the foundation for capturing the objectives, actions, and activities of both the mission analyst and the decision maker. By utilizing a “context container” that envelops an OZONE Application, we hypothesize that both user action and intent can be used to characterize user context with respect to operational modality (strategic, tactical, opportunistic, or random). As the analyst moves from one operational modality to another, we propose that information visualization techniques should adapt and present data and analysis pertinent to the new modality and to the trend of the shift. As a system captures the analyst’s actions and decisions in response to the new visualizations, the context container has the opportunity to assess the analyst’s perception of the information value, risk, uncertainty, prioritization, projection, and insight with respect to the current context stage. This paper will describe a conceptual architecture for an adaptive work environment for inferring user behavior and interaction within the OZONE framework, in order to provide the decision maker with context relevant information. We then bridge from our more conceptual OWF discussion to specific examples describing the role of context in decision making. Our first concrete example demonstrates how the Web analytics of a user’s browsing behavior can be used to authenticate users. The second example briefly examines the role of context in cyber security. Our third example illustrates how to capture the behavior of expert analysts in exploratory data analysis, which coupled with a recommender system, advises domain experts of “standard” analytical operations in order to suggest operations novel to the domain, but consistent with analytical goals. Finally, our fourth example discusses the role of context in a supervisory control problem when managing multiple autonomous systems.

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Notes

  1. 1.

    https://noscript.net/.

  2. 2.

    http://www.diffbot.com.

  3. 3.

    https://www.csie.ntu.edu.tw/~cjlin/libsvm/.

  4. 4.

    https://www.djangoprojct.com.

  5. 5.

    https://www.python.org.

  6. 6.

    http://www.mongodb.org.

  7. 7.

    http://www.r-project.org.

  8. 8.

    https://code.djangoproject.com/wiki/AJAX.

  9. 9.

    http://www.mozilla.org/en-US/firefox/.

  10. 10.

    https://developers.google.com/chart/.

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Acknowledgment

We appreciate the efforts of Ms. Linda McGibbon in the preparation of this chapter. Her efforts are always beyond the call of duty.

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Authors and Affiliations

  1. Next Century Corporation, 7075 Samuel Morse Drive, Columbia, MD, 21046, USA

    Benjamin Newsom

  2. Naval Research Laboratory, 4555 Overlook Avenue, Washington, DC, 20375, USA

    Ranjeev Mittu, Mark A. Livingston, Jonathan W. Decker, Eric Leadbetter, Ira S. Moskowitz, Antonio Gilliam, Ciara Sibley, Joseph Coyne & Myriam Abramson

  3. Army Research Laboratory, 2800 Powder Mill Road, Adelphi, MD, 20783, USA

    Stephen Russell

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Correspondence to Benjamin Newsom .

Editor information

Editors and Affiliations

  1. Department Mathematics and Computer Science, University of Udine, Udine, Italy

    Lauro Snidaro

  2. University Carlos III, Department of Computer Science and Engg. University Carlos III, Colmenarejo, Spain

    Jesús García

  3. Center Multisource Info. Fusion & Dept, Univ. at Buffalo, of Industrial and Engg Center Multisource Info. Fusion & Dept, Buffalo, New York, USA

    James Llinas

  4. Information Directorate, Air Force Research Laboratory Information Directorate, Rome, New York, USA

    Erik Blasch

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Newsom, B. et al. (2016). Modeling User Behaviors to Enable Context-Aware Proactive Decision Support. In: Snidaro, L., García, J., Llinas, J., Blasch, E. (eds) Context-Enhanced Information Fusion. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-28971-7_10

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  • DOI: https://doi.org/10.1007/978-3-319-28971-7_10

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