PROJECT TITLE :

BiSet: Semantic Edge Bundling with Biclusters for Sensemaking

ABSTRACT:

Identifying coordinated relationships is a crucial task in knowledge analytics. For example, an intelligence analyst might wish to find 3 suspicious folks who all visited the identical four cities. Existing techniques that show individual relationships, like between lists of entities, need repetitious manual choice and vital mental aggregation in cluttered visualizations to search out coordinated relationships. In this paper, we have a tendency to present BiSet, a visible analytics technique to support interactive exploration of coordinated relationships. In BiSet, we model coordinated relationships as biclusters and algorithmically mine them from a dataset. Then, we tend to visualize the biclusters in context as bundled edges between sets of connected entities. Thus, bundles enable analysts to infer task-oriented semantic insights regarding probably coordinated activities. We tend to make bundles as first category objects and add a new layer, “in-between”, to contain these bundle objects. Based mostly on this, bundles serve to prepare entities represented in lists and visually reveal their membership. Users will interact with edge bundles to organize connected entities, and vice versa, for sensemaking purposes. With a usage situation, we demonstrate how BiSet supports the exploration of coordinated relationships in Text Analytics.