A common challenge, shared by managers, analysts, and operators, is to be able to monitor a dynamic, unfolding situation based on disparate data from multiple data sources that might be uncertain, incomplete, inaccurate, or outdated. Extracting meaning from this flow of data requires recognition of events. Consistently, research finds that practitioners reason and interact in terms of event patterns, meaningful changes over time, e.g., loss of signal, battery discharging, and computer re-booting. These events are based on relationships across data, across time, and relative to the background of expectations, planned activities, team structures, and goals.
Events are generally not directly perceivable from monitoring and control displays, unless display systems have been specifically designed to recognize, capture and share events. Sophisticated support for apprehending, understanding, and communicating event patterns and interactions will be a critical path in future combat and intelligence systems. To make event patterns a basic building block of visualization, collaboration, analysis, prediction, decision, and action, research is needed to:
1. Develop representations and information systems where the basic unit is change (differences against background) as opposed to static properties of the environment.
2. Shift data processing and display to center on events structures that are patterns of change –- events extend over time with complex internal structures.
3. Make observer perspective or point of view is a central variable in the definition and extraction of events, i.e., how change is perceived is observer- and context-conditioned.
4. Develop computation and visualization around context sensitive layers of event structures that compose narratives.
Current research project: see
Christoffersen, K., Woods,, D. D. and Blike, G. T. (2001). Extracting Event Patterns From Telemetry Data. Proceedings of the Human Factors and Ergonomics Society 45th annual meeting. 8-12 October, Minneapolis, MN.
Christoffersen, K., Blike, G. T., and Woods, D. D. (2003). Discovering the Events Expert Practitioners Find Meaningful in Dynamic Data Streams. Cognitive Systems Engineering Laboratory, Institute for Ergonomics, The Ohio State University, Columbus OH.
Christoffersen, K., Woods,, D. D. and Blike, G. T. (2002). Making Sense of Change: Extracting Events From Dynamic Process Data. . Institute for Ergonomics/Cognitive Systems Engineering Laboratory Report, ERGO-CSEL 01-TR-02. May 31, 2002.