Best Poster Award for "Provenance-Based Visualization Retrieval"

Description

Storing interaction provenance generates a knowledge base with a large potential for recalling previous results and guiding the user in future analyses. However, search and retrieval of analysis states can become tedious without extensive creation of meta-information by the user. In this work we present an approach for an efficient retrieval of analysis states which are structured as provenance graphs of automatically recorded user interactions and visualizations. As a core component, we describe a visual interface for querying and exploring analysis states based on their similarity to a partial defi- nition of the requested analysis state. Depending on the use case, this definition may be provided explicitly by the user or inferred from a reference state. We explain the definition by means of a Gapminder-inspired prototype and discuss our implementation for an effective retrieval of previous states.

Period	02 Oct 2017