Querying Communities in Relational Databases
Refereed conference paper presented and published in conference proceedings

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AbstractKeyword search on relational databases provides users with insights that they can not easily observe using the traditional RDBMS techniques. Here, an l-keyword query is specified by a set of l keywords, {k(1), k(2), ... , k(l)}. It finds how the tuples that contain the keywords are connected in a relational database via the possible foreign key references. Conceptually, it is to find some structural information in a database graph, where nodes are tuples and edges are foreign key references. The existing work studied how to find connected trees for an l-keyword query. However, a tree may only show partial information about how those tuples that contain the keywords are connected. In this paper, we focus on finding communities for an l-keyword query. A community is an induced subgraph that contains all the l-keywords within a given distance. We propose new efficient algorithms to find all/top-k communities which consume small memory, for an l-keyword query. For top-k l-keyword queries, our algorithm allows users to interactively enlarge k at run time. We conducted extensive performance studies using two large real datasets to confirm the efficiency of our algorithms.
All Author(s) ListQin L, Yu JX, Chang LJ, Tao YF
Name of ConferenceIEEE 25th International Conference on Data Engineering
Start Date of Conference29/03/2009
End Date of Conference02/04/2009
Place of ConferenceShanghai
Country/Region of ConferenceChina
Detailed descriptionIEEE
Pages724 - 735
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering; Engineering, Electrical & Electronic

Last updated on 2020-31-05 at 01:45