@inproceedings{PB-VRVis-2017-034,
author = {Stitz, Holger and Gratzl, Samuel and Piringer, Harald and Streit, Marc},
title = {Provenance-Based Visualization Retrieval},
year = {2017},
booktitle = {2017 IEEE Conference on Visual Analytics Science and Technology
  (VAST Best Poster Award)},
editor = {N.N.},
url = {https://www.vrvis.at/publications/PB-VRVis-2017-034},
abstract = {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.},
}