Optimization for active learning-based interactive database exploration

Enhui Huang, Liping Peng, Luciano Di Palma, Ahmed Abdelkafi, Anna Liu, Yanlei Diao
2018 Proceedings of the VLDB Endowment  
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"
more » ... framework, and bring the properties of important classes of database queries to bear on the design of new algorithms and optimizations for active learningbased 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.
doi:10.14778/3275536.3275542 fatcat:thkyknp22rg2vbgva7hl7g3c6q