iExplore: Accelerating Exploratory Data Analysis by Predicting User Intention

• Published in: Database Systems for Advanced Applications Vol. 10828; pp. 149 - 165
• Main Authors: Yang, Zhihui, Gong, Jiyang, Liu, Chaoying, Jing, Yinan, He, Zhenying, Zhang, Kai, Wang, X. Sean
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Data exploration; Query log; User intention
• ISBN: 331991457X; 9783319914572
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-91458-9_9

Exploratory data analysis over large datasets has become an increasingly prevalent use case. However, users are easily overwhelmed by the data and might take a long time to find interesting facts. In this paper, we design a system called iExplore to assist users in doing this time-consuming data exploration task through predicting user intention. Moreover, we propose an intention model to help the iExplore system have a comprehensive understanding of user’s intention. Thus, the exploratory process can be accelerated by the intention-driven recommendation and prefetching mechanisms. Extensive experiments demonstrate that the intention-driven iExplore system can significantly lighten the burden of users and facilitate the exploratory process.