REQUEST: a scalable framework for interactive construction of exploratory queries

Ge, Xiaoyu, Xue, Yanbing, Luo, Zhipeng, Sharaf, Mohamed A. and Chrysanthis, Panos K. (2017). REQUEST: a scalable framework for interactive construction of exploratory queries. In: Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. 4th IEEE International Conference on Big Data, Big Data 2016, Washington, DC, United States, (646-655). 5 - 8 December 2016. doi:10.1109/BigData.2016.7840657


Author Ge, Xiaoyu
Xue, Yanbing
Luo, Zhipeng
Sharaf, Mohamed A.
Chrysanthis, Panos K.
Title of paper REQUEST: a scalable framework for interactive construction of exploratory queries
Conference name 4th IEEE International Conference on Big Data, Big Data 2016
Conference location Washington, DC, United States
Conference dates 5 - 8 December 2016
Convener IEEE
Proceedings title Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
Journal name 2016 Ieee International Conference On Big Data (Big Data)
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2017
Sub-type Fully published paper
DOI 10.1109/BigData.2016.7840657
Open Access Status Not yet assessed
ISBN 9781467390057
9781467390040
Start page 646
End page 655
Total pages 10
Language eng
Abstract/Summary Exploration over large datasets is a key first step in data analysis, as users may be unfamiliar with the underlying database schema and unable to construct precise queries that represent their interests. Such data exploration task usually involves executing numerous ad-hoc queries, which requires a considerable amount of time and human effort. In this paper, we present REQUEST, a novel framework that is designed to minimize the human effort and enable both effective and efficient data exploration. REQUEST supports the query-from-examples style of data exploration by integrating two key components: 1) Data Reduction, and 2) Query Selection. As instances of the REQUEST framework, we propose several highly scalable schemes, which employ active learning techniques and provide different levels of efficiency and effectiveness as guided by the user's preferences. Our results, on real-world datasets from Sloan Digital Sky Survey, show that our schemes on average require 1-2 orders of magnitude fewer feedback questions than the random baseline, and 3-16× fewer questions than the state-of-the-art, while maintaining interactive response time. Moreover, our schemes are able to construct, with high accuracy, queries that are often undetectable by current techniques.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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