Efficient and Effective Aggregate Keyword Search on Relational Databases

Authors:
Luping Li;Stephen Petschulat;Guanting Tang;Jian Pei;Wo-Shun Luk
Affiliations:
Baidu, Inc., Beijing, China;SAP Business Objects, Coquitlam, BC, Canada;School of Computing Science, Simon Fraser University, Burnaby, BC, Canada;School of Computing Science, Simon Fraser University, Burnaby, BC, Canada;School of Computing Science, Simon Fraser University, Burnaby, BC, Canada
Venue:
International Journal of Data Warehousing and Mining
Year:
2012

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Abstract

Keyword search on relational databases is useful and popular for many users without technical background. Recently, aggregate keyword search on relational databases was proposed and has attracted interest. However, two important problems still remain. First, aggregate keyword search can be very costly on large relational databases, partly due to the lack of efficient indexes. Second, finding the top-k answers to an aggregate keyword query has not been addressed systematically, including both the ranking model and the efficient evaluation methods. In this paper, the authors tackle these two problems to improve the efficiency and effectiveness of aggregate keyword search on large relational databases. They designed indexes efficient in both size and construction time. The authors propose a general ranking model and an efficient ranking algorithm. They also report a systematic performance evaluation using real data sets.