Efficient and Effective Aggregate Keyword Search on Relational Databases

Luping Li, Stephen Petschulat, Guanting Tang, Jian Pei and Wo-Shun Luk
Additional contact information
Luping Li: Baidu, Inc., Beijing, China
Stephen Petschulat: SAP Business Objects, Coquitlam, BC, Canada
Guanting Tang: School of Computing Science, Simon Fraser University, Burnaby, BC, Canada
Jian Pei: School of Computing Science, Simon Fraser University, Burnaby, BC, Canada
Wo-Shun Luk: School of Computing Science, Simon Fraser University, Burnaby, BC, Canada

International Journal of Data Warehousing and Mining (IJDWM), 2012, vol. 8, issue 4, 41-81

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.

Date: 2012
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 4018/jdwm.2012100103 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jdwm00:v:8:y:2012:i:4:p:41-81

Access Statistics for this article

International Journal of Data Warehousing and Mining (IJDWM) is currently edited by Eric Pardede

More articles in International Journal of Data Warehousing and Mining (IJDWM) from IGI Global
Bibliographic data for series maintained by Journal Editor ().