%0 Conference Paper %1 lahmar2017 %A Ben Lahmar, Houssem %A Herschel, Melanie %B Proceedings of the USENIX Conference on Theory and Practice of Provenance (TAPP) %D 2017 %K 2017 D03 from:leonkokkoliadis sfbtrr161 visus visus:herschme %P 1-7 %T Provenance-based Recommendations for Visual Data Exploration %X 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.

@inproceedings{lahmar2017, 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.}, added-at = {2020-03-06T15:49:52.000+0100}, author = {Ben Lahmar, Houssem and Herschel, Melanie}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2287913aae571a6eb50f913d8c46f5814/sfbtrr161}, booktitle = {Proceedings of the USENIX Conference on Theory and Practice of Provenance (TAPP)}, interhash = {868eb8c0c45b2947192f1f0b6f6ac1b5}, intrahash = {287913aae571a6eb50f913d8c46f5814}, keywords = {2017 D03 from:leonkokkoliadis sfbtrr161 visus visus:herschme}, pages = {1-7}, timestamp = {2020-10-05T11:32:24.000+0200}, title = {Provenance-based Recommendations for Visual Data Exploration}, year = 2017 }