Skip to Main content Skip to Navigation
Conference papers

DashBot: An ML-Guided Dashboard Generation System Authors' Copy

Abstract : Data summarization provides a bird's eye view of data and groupby queries have been the method of choice for data summarization. Such queries provide the ability to group by some attributes and aggregate by others, and their results can be coupled with a visualization to convey insights. The number of possible groupbys that can be computed over a dataset is quite large which naturally calls for developing approaches to aid users in choosing which groupbys best summarize data. We demonstrate DashBot, a system that leverages Machine Learning to guide users in generating data-driven and customized dashboards. A dashboard contains a set of panels, each of which is a groupby query. DashBot iteratively recommends the most relevant panel while ensuring coverage. Relevance is computed based on intrinsic measures of the dataset and coverage aims to provide comprehensive summaries. DashBot relies on a Multi-Armed Bandits (MABs) approach to balance exploitation of relevance and exploration of different regions of the data to achieve coverage. Users can provide feedback and explanations to customize recommended panels. We demonstrate the utility and features of DashBot on different datasets.
Document type :
Conference papers
Complete list of metadata
Contributor : Sihem Amer-Yahia Connect in order to contact the contributor
Submitted on : Friday, October 15, 2021 - 10:22:51 AM
Last modification on : Tuesday, November 9, 2021 - 8:53:40 AM


  • HAL Id : hal-03379720, version 1



Sandrine da Col, Radu Ciucanu, Marta Soare, Nassim Bouarour, Sihem Amer-Yahia. DashBot: An ML-Guided Dashboard Generation System Authors' Copy. 30th ACM International Conference on Information and Knowledge Management, Nov 2021, Queensland (in line), Australia. ⟨hal-03379720⟩



Record views


Files downloads