NDLI: Detecting subgraph isomorphism with MapReduce

Content Provider	Springer Nature Link
Author	Fehér, Péter Asztalos, Márk Vajk, Tamás Mészáros, Tamás Lengyel, László
Copyright Year	2016
Abstract	In recent years, the MapReduce framework has become one of the most popular parallel computing platforms for processing big data. MapReduce is used by companies such as Facebook, IBM, and Google to process or analyze massive data sets. Since the approach is frequently used for industrial solutions, the algorithms based on the MapReduce framework gained significant attention within the scientific community. The subgraph isomorphism is a fundamental graph theory problem. Finding small patterns in large graphs is a core challenge in the analysis of applications with big data sets. This paper introduces two novel algorithms, which are capable of finding matching patterns in arbitrary large graphs. The algorithms are designed for utilizing the easy parallelization technique offered by the MapReduce framework. The approaches are evaluated regarding their space and memory requirements. The paper also provides the applied data structure and presents formal analysis of the algorithms.
Starting Page	1810
Ending Page	1851
Page Count	42
File Format	PDF
ISSN	09208542
Journal	The Journal of Supercomputing
Volume Number	73
Issue Number	5
e-ISSN	15730484
Language	English
Publisher	Springer US
Publisher Date	2016-10-06
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Subgraph isomorphism MapReduce Pattern matching Programming Languages, Compilers, Interpreters Processor Architectures Computer Science
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Information Systems Hardware and Architecture Software

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