Can We Ever Escape from Data Overload? A Cognitive Systems Diagnosis

Abstract

Data overload is a generic and tremendously difficult problem. We examine three characterizations of the data overload problem and how they lead to different proposed solutions. The first characterization is a clutter problem where there is “too much stuff,” which leads to proposals to reduce the number of data units that are displayed. The second characterization is a workload bottleneck where there is too much data to process in the time available. Data overload as a workload bottleneck shifts the view to practitioner activities rather than elemental data and leads to proposals to use automation to perform activities for the practitioner. The third characterization is a problem in finding the significance of data when it not known a priori what data will be informative. This characterization leads to model-based abstractions and representation design techniques as potential solutions. Many of the existing approaches to coping with data overload avoid directly confronting the problem that what is significant depends on context. We advocate an alternative approach that depends on model-based organization of the data in a conceptual space that depicts the relationships, events, and contrasts that are informative in a field of practice and uses active machine intelligence in circumscribed, cooperative roles to aid human observers in organizing, selecting, managing, and interpreting data.