Best Poster Award for "Provenance-Based Visualization Retrieval"
Sprache des Titels:
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.