A framework for SQL-based mining of large graphs on relational databases

Sriganesh Srihari, Shruti Chandrashekar, Srinivasan Parthasarathy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

We design and develop an SQL-based approach for querying and mining large graphs within a relational database management system (RDBMS). We propose a simple lightweight framework to integrate graph applications with the RDBMS through a tightly-coupled network layer, thereby leveraging efficient features of modern databases. Comparisons with straight-up main memory implementations of two kernels - readth-first search and quasi clique detection - reveal that SQL implementations offer an attractive option in terms of productivity and performance.

LanguageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings
Pages160-167
Number of pages8
EditionPART 2
DOIs
Publication statusPublished - 1 Dec 2010
Event14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010 - Hyderabad, India
Duration: 21 Jun 201024 Jun 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
No.PART 2
Volume6119 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010
CountryIndia
CityHyderabad
Period21/06/1024/06/10

Keywords

  • Graph mining
  • Relational databases
  • SQL-based approach

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Srihari, S., Chandrashekar, S., & Parthasarathy, S. (2010). A framework for SQL-based mining of large graphs on relational databases. In Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings (PART 2 ed., pp. 160-167). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6119 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-13672-6_16
Srihari, Sriganesh ; Chandrashekar, Shruti ; Parthasarathy, Srinivasan. / A framework for SQL-based mining of large graphs on relational databases. Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings. PART 2. ed. 2010. pp. 160-167 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
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Srihari, S, Chandrashekar, S & Parthasarathy, S 2010, A framework for SQL-based mining of large graphs on relational databases. in Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 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. 6119 LNAI, pp. 160-167, 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010, Hyderabad, India, 21/06/10. https://doi.org/10.1007/978-3-642-13672-6_16

A framework for SQL-based mining of large graphs on relational databases. / Srihari, Sriganesh; Chandrashekar, Shruti; Parthasarathy, Srinivasan.

Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings. PART 2. ed. 2010. p. 160-167 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6119 LNAI, No. PART 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Srihari S, Chandrashekar S, Parthasarathy S. A framework for SQL-based mining of large graphs on relational databases. In Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings. PART 2 ed. 2010. p. 160-167. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-13672-6_16