Scalable motif search in graphs using distributed computing : a thesis presented in partial fulfilment of the requirements for the degree of a Masters in Computer Science, Massey University, Turitea, New Zealand
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2012
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Massey University
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Abstract
Motif detection allows software engineers to detect antipatterns in software.
By decreasing the number of antipattern instances in a piece of software, the
overall quality of the software is improved. Current methods to nd these
antipatterns are slow and return results only when all antipatterns have been
found. The GUERY framework is able to perform motif detection using
multiple cores and deliver results as they are generated. By scaling GUERY
to run on multiple machines, it was hoped that research requiring many
queries on a graph could be performed signi cantly faster than is currently
possible.
The objective of this thesis was to research and prototype mechanisms
whereby GUERY could be run using a cluster of computers and results
delivered as a stream to interested systems. A system capable of running on
a cluster of machines and delivering a stream of results as they are computed
was developed.
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Research Subject Categories::TECHNOLOGY::Information technology::Computer science::Software engineering, Antipatterns (Software engineering), Electronic data processing, Distributed processing