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Journal Articles Proceedings of the VLDB Endowment (PVLDB) Year : 2018

Optimization for active learning-based interactive database exploration

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Abstract

There is an increasing gap between fast growth of data and limited human ability to comprehend data. Consequently, there has been a growing demand of data management tools that can bridge this gap and help the user retrieve high-value content from data more e↵ectively. In this work, we aim to build interactive data exploration as a new database service, using an approach called "explore-by-example". In particular, we cast the explore-by-example problem in a principled "active learning" framework, and bring the properties of important classes of database queries to bear on the design of new algorithms and optimizations for active learning-based database exploration. These new techniques allow the database system to overcome a fundamental limitation of traditional active learning, i.e., the slow convergence problem. Evaluation results using real-world datasets and user interest patterns show that our new system significantly outperforms state-of-the-art active learning techniques and data exploration systems in accuracy while achieving desired eciency for interactive performance.
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Dates and versions

hal-01969886 , version 1 (07-01-2019)

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Enhui Huang, Liping Peng, Luciano Di Palma, Ahmed Abdelkafi, Anna Liu, et al.. Optimization for active learning-based interactive database exploration. Proceedings of the VLDB Endowment (PVLDB), 2018, 12 (1), pp.71-84. ⟨10.14778/3275536.3275542⟩. ⟨hal-01969886⟩
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