Can We Ever Escape from Data Overload? A Cognitive Systems Diagnosis
Abstract
Data overload is a generic and tremendously difficult problem. In this report, we diagnose why this is the case and how intelligence analysis presents a particularly difficult version of data overload. We examine three different characterizations that have been offered to capture the nature of the data overload problem and how they lead to different proposed solutions. The first characterization is the clutter problem where there is too much stuff, which leads to proposals to reduce the number of data bits that are displayed. The second characterization is a workload bottleneck where there is too much data to analyze in the time available. Data overload as a workload bottleneck shifts the view to particular activities rather than elemental data and leads to proposals to use automation to perform activities for the practitioner or cooperating automation to assist the practitioner. The third characterization is a problem in finding the significance of data when it is not known a priori what data will be informative. This characterization leads to model based abstractions and representation design techniques as potential solutions By focusing attention on the root issues that make data overload a difficult problem, we have identified a set of challenges that all potential solutions must meet. Most notably, all techniques must deal with the importance of context sensitivity in interpreting data. In order to place data in context, designers need to display data in a conceptual space that depicts the relationships, events, and contrasts that are informative in a field of practice. The diagnosis also reveals how data overload is manifested in intelligence analysis like situations.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 1998
- Accession Number
- ADA371142
Entities
People
- David D. Woods
- Emilie M. Roth
- Emily S. Patterson
Organizations
- Ohio State University