Predicting “What is interesting” by mining interactive-data-analysis session logs

Tova Milo, Chai Ozeri, Amit Somech

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations


Assessing the interestingness of data analysis actions has been the subject of extensive previous work, and a multitude of interestingness measures have been devised, each capturing a different facet of the broad concept. While such measures are a core component in many analysis platforms (e.g., for ranking association rules, recommending visualizations, and query formulation), choosing the most adequate measure for a specific analysis task or an application domain is known to be a difficult task. In this work we focus on the choice of interestingness measures particularly for Interactive Data Analysis (IDA), where users examine datasets by performing sessions of analysis actions. Our goal is to determine the most suitable interestingness measure that adequately captures the user’s current interest at each step of an interactive analysis session. We propose a novel solution that is based on the mining of IDA session logs. First, we perform an offline analysis of the logs, and identify unique characteristics of interestingness in IDA sessions. We then define a classification problem and build a predictive model that can select the best measure for a given a state of a user session. Our experimental evaluation, performed over real-life session logs, demonstrates the sensibility and adequacy of our approach.

Original languageEnglish
Title of host publicationAdvances in Database Technology - EDBT 2019
Subtitle of host publication22nd International Conference on Extending Database Technology, Proceedings
EditorsHelena Galhardas, Zoi Kaoudi, Berthold Reinwald, Melanie Herschel, Carsten Binnig, Irini Fundulaki
Number of pages12
ISBN (Electronic)9783893180813
StatePublished - 2019
Externally publishedYes
Event22nd International Conference on Extending Database Technology, EDBT 2019 - Lisbon, Portugal
Duration: 26 Mar 201929 Mar 2019

Publication series

NameAdvances in Database Technology - EDBT
ISSN (Electronic)2367-2005


Conference22nd International Conference on Extending Database Technology, EDBT 2019

Bibliographical note

Funding Information:
This work has been partially funded by the Israel Innovation Authority, the Israel Science Foundation, Len Blavatnik and the Blavatnik Family foundation, and Intel®AI DevCloud.

Publisher Copyright:
© 2019 Copyright held by the owner/author(s).


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