Querying communities in relational databases

Qin, L; Yu, JX; Chang, L; Tao, Y

Querying communities in relational databases

Qin, L

Yu, JX

Chang, L Tao, Y

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Data Engineering, 2009, pp. 724 - 735
Issue Date:: 2009-07-08

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Field	Value	Language
dc.contributor.author	Qin, L https://orcid.org/0000-0001-6068-5062	en_US
dc.contributor.author	Yu, JX https://orcid.org/0000-0002-9738-827X	en_US
dc.contributor.author	Chang, L	en_US
dc.contributor.author	Tao, Y	en_US
dc.date.issued	2009-07-08	en_US
dc.identifier.citation	Proceedings - International Conference on Data Engineering, 2009, pp. 724 - 735	en_US
dc.identifier.isbn	9780769535456	en_US
dc.identifier.issn	1084-4627	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28937
dc.description.abstract	Keyword 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, {k1, k 2, ⋯ , kl}. 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 topk 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. © 2009 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Data Engineering	en_US
dc.relation.isbasedon	10.1109/ICDE.2009.67	en_US
dc.title	Querying communities in relational databases	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
dc.location.activity	Shanghai, China	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Keyword 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, {k1, k 2, ⋯ , kl}. 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 topk 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. © 2009 IEEE.

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http://hdl.handle.net/10453/28937