RNRank: Network-based ranking on relational tuples

Li, Peng, Chen, Ling, Li, Xue and Wen, Junhao (2013). RNRank: Network-based ranking on relational tuples. In: Longbing Cao, Hiroshi Motoda, Jaideep Srivastava, Ee-Peng Lim, Irwin King, Philip S. Yu, Wolfgang Nejdl, Guandong Xu, Gang Li and Ya Zhang, Behavior and Social Computing International Workshop on Behavior and Social Informatics, BSI 2013 and International Workshop on Behavior and Social Informatics and Computing, BSIC 2013. 2013 International Workshop on Behavior and Social Informatics and Computing, BSIC 2013, Beijing, Peoples R China, (139-150). 3 - 9 August 2013. doi:10.1007/978-3-319-04048-6_13


Author Li, Peng
Chen, Ling
Li, Xue
Wen, Junhao
Title of paper RNRank: Network-based ranking on relational tuples
Conference name 2013 International Workshop on Behavior and Social Informatics and Computing, BSIC 2013
Conference location Beijing, Peoples R China
Conference dates 3 - 9 August 2013
Proceedings title Behavior and Social Computing International Workshop on Behavior and Social Informatics, BSI 2013 and International Workshop on Behavior and Social Informatics and Computing, BSIC 2013   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2013
Sub-type Fully published paper
DOI 10.1007/978-3-319-04048-6_13
ISBN 9783319040479
9783319040486
ISSN 0302-9743
1611-3349
Editor Longbing Cao
Hiroshi Motoda
Jaideep Srivastava
Ee-Peng Lim
Irwin King
Philip S. Yu
Wolfgang Nejdl
Guandong Xu
Gang Li
Ya Zhang
Volume 8178
Issue PART 2
Start page 139
End page 150
Total pages 12
Collection year 2014
Language eng
Abstract/Summary Conventional relational top-k queries ignore the inherent referential relationships existing between tuples that can effectively link all tuples of a database together. A relational database can be viewed as a network of tuples connected via foreign keys. With respect to the semantics defined over the foreign keys, the most referenced tuples, therefore, can be regarded as either the most influential, relevant, popular, or authoritative objects stored in a relational database according to its domain semantics. In this paper we propose a novel network-based ranking approach to discover those tuples that are mostly referenced in a relational database as top-k query results. Compared with the conventional relational top-k query processing, our approach can provide information about network structured relational tuples and expand top-k query results as recommendations to users using linkage information in databases. Our experiments on sample relational databases demonstrate the effectiveness and efficiency of our proposed RNRank (Relational Network-based Rank) approach.
Subjects 1700 Computer Science
2614 Theoretical Computer Science
Keyword Information network
Network-based ranking
Relational tuples
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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