Optimization for Active Learning-based Interactive Database Exploration

Abstract : There is an increasing gap between the fast growth of data and the 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 effectively. 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 fundamental limitations of traditional active learning, in particular, 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 efficiency for interactive performance.
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Contributor : Enhui Huang <>
Submitted on : Friday, September 7, 2018 - 7:20:57 PM
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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. [Technical Report] Ecole Polytechnique; University of Massachusetts Amherst. 2018. ⟨hal-01870560⟩

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