Recursive graph pattern matching: (With magic sets and global search plans)

Gergely Varró, Ákos Horváth, Dániel Varró

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

10 Citations (Scopus)

Abstract

We present core data structures and algorithms for matching graph patterns with general recursion. Our approach uses magic sets, a well-known technique from deductive databases, which combines fixpoint-based bottom-up query evaluation with top-down handling of input parameters. Furthermore, this technique is enhanced with the global search plans, thus non-recursive calls are always flattened before elementary pattern matching operations are initiated in order to improve performance. Our approach is exemplified using Viatra2.

Original languageEnglish
Title of host publicationApplications of Graph Transformations with Industrial Relevance - Third International Symposium, AGTIVE 2007, Revised Selected and Invited Papers
Pages456-470
Number of pages15
DOIs
Publication statusPublished - Dec 1 2008
Event3rd International Symposium on Applications of Graph Transformations with Industrial Relevance, AGTIVE 2007 - Kassel, Germany
Duration: Oct 10 2007Oct 12 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5088 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Symposium on Applications of Graph Transformations with Industrial Relevance, AGTIVE 2007
CountryGermany
CityKassel
Period10/10/0710/12/07

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Varró, G., Horváth, Á., & Varró, D. (2008). Recursive graph pattern matching: (With magic sets and global search plans). In Applications of Graph Transformations with Industrial Relevance - Third International Symposium, AGTIVE 2007, Revised Selected and Invited Papers (pp. 456-470). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5088 LNCS). https://doi.org/10.1007/978-3-540-89020-1-31