The role of interactive biclusters in sensemaking

Abstract

Visual exploration of relationships within large, textual datasets is an important aid for human sensemaking. By understanding computed, structural relationships between entities of different types (e.g., people and locations), users can leverage domain expertise and intuition to determine the importance and relevance of these relationships for tasks, such as intelligence analysis. Biclusters are a potentially desirable method to facilitate this, because they reveal coordinated relationships that can represent meaningful relationships. Bixplorer, a visual analytics prototype, supports interactive exploration of textual datasets in a spatial workspace with biclusters. In this paper, we present results of a study that analyzes how users interact with biclusters to solve an intelligence analysis problem using Bixplorer. We found that biclusters played four principal roles in the analytical process: an effective starting point for analysis, a revealer of two levels of connections, an indicator of potentially important entities, and a useful label for clusters of organized information.