Visual Querying of Semantically Enriched Movement Data

Visual data exploration is used to reveal unknown patterns that, however, need to be validated, refined, and extracted for a final presentation and reporting. We contribute VESPa, a pattern-based visual query language for event sequences. With VESPa, analysts can formulate hypotheses gained and query the data for matches. In an interative analysis loop the pattern can be altered with further restrictions to narrow down the result set. Our language allows for (1) hypothesis expression and refinement, (2) visual querying, and (3) knowledge externalization. We focus on semantically enrichend movement data, used in law enforcement, consumer, and traffic analysis. To evaluate the applicability we present two case studies as well as a user study consisting of comprehensive and composition tasks.