Provenance-based Recommendations for Visual Data Exploration

Authors: 

Houssem Ben Lahmar and Melanie Herschel, IPVS - University of Stuttgart

Abstract: 

Visual data exploration allows users to analyze datasets based on visualizations of interesting data characteristics, to possibly discover interesting information about the data that users are a priori unaware of. In this context, both recommendations of queries selecting the data to be visualized and recommendations of visualizations that highlight interesting data characteristics support users in visual data exploration. So far, these two types of recommendations have been mostly considered in isolation of one another.

We present a recommendation approach for visual data exploration that unifies query recommendation and visualization recommendation. The recommendations rely on two types of provenance, i.e., data provenance (aka lineage) and evolution provenance that tracks users’ interactions with a data exploration system. This paper presents the provenance data model as well as the overall system architecture. We then provide details on our provenance-based recommendation algorithms. A preliminary experimental evaluation showcases the applicability of our solution in practice.

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BibTeX
@inproceedings {204235,
author = {Houssem Ben Lahmar and Melanie Herschel},
title = {Provenance-based Recommendations for Visual Data Exploration},
booktitle = {9th {USENIX} Workshop on the Theory and Practice of Provenance (TaPP 2017)},
year = {2017},
address = {Seattle, WA},
url = {https://www.usenix.org/conference/tapp17/workshop-program/presentation/lahmar},
publisher = {{USENIX} Association},
month = jun,
}