Interactive data-driven search and discovery of temporal behavior patterns from media streams

Abstract

The presented thesis work addresses how social scientists may derive patterns of human behavior captured in media streams. Currently, media streams are being segmented into sequences of events describing the actions captured in the streams, such as the interactions among humans. This segmentation creates a challenging data space to search characterized by non-numerical, temporal, descriptive data, e.g., Person A walks up to Person B at time T. We present an approach that allows one to interactively search and discover temporal behavior patterns within such a data space.