Ranking objects based on attribute value correlation

Jaehui Park; Sang Goo Lee

doi:10.1007/978-3-642-15251-1_28

Ranking objects based on attribute value correlation

Jaehui Park, Sang Goo Lee

Department of Statistics

Seoul National University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

There has been a great deal of interest in recent years on ranking query results in relational databases. This paper presents a novel method to rank objects (e.g., tuples) by exploiting the correlations among their attribute values. Given a query, each attribute value is assigned a score according to mutual occurrences with the query and its distribution status in the columns of the attribute. These attribute value scores are aggregated to get a final score for an object. Furthermore, a concept vector is proposed to provide a synopsis of the attribute value in a given database. A concept vector is utilized to get the similar objects. Experimental results demonstrate the performance of our ranking method, RAVC (Ranking with Attribute Value Correlation), in terms of search quality and efficiency.

Original language	English
Title of host publication	Database and Expert Systems Applications - 21st International Conference, DEXA 2010, Proceedings
Pages	346-359
Number of pages	14
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-15251-1_28
State	Published - 2010
Event	21st International Conference on Database and Expert Systems Applications, DEXA 2010 - Bilbao, Spain Duration: 30 Aug 2010 → 3 Sep 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6262 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Database and Expert Systems Applications, DEXA 2010
Country/Territory	Spain
City	Bilbao
Period	30/08/10 → 3/09/10

Keywords

Ranking function for structured data
attribute importance
attribute value correlation

Access to Document

10.1007/978-3-642-15251-1_28

Cite this

Park, J., & Lee, S. G. (2010). Ranking objects based on attribute value correlation. In Database and Expert Systems Applications - 21st International Conference, DEXA 2010, Proceedings (PART 2 ed., pp. 346-359). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6262 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-15251-1_28

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Park, J & Lee, SG 2010, Ranking objects based on attribute value correlation. in Database and Expert Systems Applications - 21st International Conference, DEXA 2010, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6262 LNCS, pp. 346-359, 21st International Conference on Database and Expert Systems Applications, DEXA 2010, Bilbao, Spain, 30/08/10. https://doi.org/10.1007/978-3-642-15251-1_28

Ranking objects based on attribute value correlation. / Park, Jaehui; Lee, Sang Goo.
Database and Expert Systems Applications - 21st International Conference, DEXA 2010, Proceedings. PART 2. ed. 2010. p. 346-359 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6262 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - There has been a great deal of interest in recent years on ranking query results in relational databases. This paper presents a novel method to rank objects (e.g., tuples) by exploiting the correlations among their attribute values. Given a query, each attribute value is assigned a score according to mutual occurrences with the query and its distribution status in the columns of the attribute. These attribute value scores are aggregated to get a final score for an object. Furthermore, a concept vector is proposed to provide a synopsis of the attribute value in a given database. A concept vector is utilized to get the similar objects. Experimental results demonstrate the performance of our ranking method, RAVC (Ranking with Attribute Value Correlation), in terms of search quality and efficiency.

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Park J, Lee SG. Ranking objects based on attribute value correlation. In Database and Expert Systems Applications - 21st International Conference, DEXA 2010, Proceedings. PART 2 ed. 2010. p. 346-359. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-15251-1_28

Ranking objects based on attribute value correlation

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