Issue No. 12 - December (2011 vol. 23)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.60
Kequi Li , Sch. of Comput. Sci. & Technol., Dalian Univ. of Technol., Dalian, China
Jianmin Wang , Tsinghua Nat. Lab. for Inf. Sci. & Technol., Tsinghua Univ., Beijing, China
Xiaofang Zhou , Sch. of Inf. Technol. & Electr. Eng., Univ. of Queensland, Brisbane, QLD, Australia
Xuemin Lin , Univ. of New South Wales, Sydney, NSW, Australia
Wei Wang , Univ. of New South Wales, Sydney, NSW, Australia
Yi Luo , Lab. Le2i, CNRS Dijon, Dijon, France
With the increasing amount of text data stored in relational databases, there is a demand for RDBMS to support keyword queries over text data. As a search result is often assembled from multiple relational tables, traditional IR-style ranking and query evaluation methods cannot be applied directly. In this paper, we study the effectiveness and the efficiency issues of answering top-k keyword query in relational database systems. We propose a new ranking formula by adapting existing IR techniques based on a natural notion of virtual document. We also propose several efficient query processing methods for the new ranking method. We have conducted extensive experiments on large-scale real databases using two popular RDBMSs. The experimental results demonstrate significant improvement to the alternative approaches in terms of retrieval effectiveness and efficiency.
text analysis, query processing, question answering (information retrieval), relational databases, large-scale real database, SPARK2, top-k keyword query answering, relational database, text data storage, RDBMS, multiple relational table, efficiency issues, effectiveness issues, IR-style ranking, query evaluation method, virtual document, query processing method, Query processing, Keyword search, Relational databases, Information retrieval, Electronic mail, Semantics, information retrieval., Top-k, keyword search, relational database
Kequi Li, Jianmin Wang, Xiaofang Zhou, Xuemin Lin, Wei Wang and Yi Luo, "SPARK2: Top-k Keyword Query in Relational Databases," in IEEE Transactions on Knowledge & Data Engineering, vol. 23, no. , pp. 1763-1780, 2011.