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Permanent link (DOI): https://doi.org/10.7939/R3HW5C

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Theses and Dissertations

Heavyweight Pattern Mining in Attributed Flow Graphs Open Access

Descriptions

Other title
Subject/Keyword
software analysis
pattern mining
program analysis
flow graph
program profiling
sub-graph mining
data mining
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Simoes Gomes, Carolina
Supervisor and department
Amaral, Jose Nelson (Computing Science)
Examining committee member and department
Kurgan, Lukasz (Electrical and Computer Engineering)
Zaiane, Osmar (Computing Science)
Hindle, Abram (Computing Science)
Sutton, Richard (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2012-09-26T12:59:46Z
Graduation date
2012-09
Degree
Master of Science
Degree level
Master's
Abstract
Flow graphs are an abstraction used to represent elements travelling through a network of nodes. The paths between nodes are directed edges in the graph, and the amount or transmission frequency of elements that go through the paths are edge weights. If additional data is associated with the nodes, we have attributed flow graphs (AFGs). This thesis defines heavyweight patterns, which are sub-sets of attributes connected by edges found in a dataset of AFGs, and have a computed weight higher than an user-defined threshold. The thesis also defines Heavyweight Pattern Mining, the problem of finding heavyweight patterns in AFGs. It presents a new algorithm called AFGMiner, which solves Heavyweight Pattern Mining and associates patterns with their occurrences in the dataset. In addition, it describes HEPMiner and SCPMiner, two new program performance analysis tools that apply AFGMiner and have as target users compiler and application developers respectively.
Language
English
DOI
doi:10.7939/R3HW5C
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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