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2012 IEEE 12th International Conference on Data Mining Workshops
Enhancing the Analysis of Large Multimedia Applications Execution Traces with FrameMiner
Brussels, Belgium Belgium
December 10-December 10
ISBN: 978-1-4673-5164-5
The analysis of multimedia application traces can reveal important information to enhance program comprehension. However traces can be very large, which hinders their effective exploitation. In this paper, we study the problem of finding a \textit{k-golden} set of blocks that best characterize data. Sequential pattern mining can help to automatically discover the blocks, and we called \textit{k-golden set}, a set of $k$ blocks that maximally covers the trace. These kind of blocks can simplify the exploration of large traces by allowing programmers to see an abstraction instead of low-level events. We propose an approach for mining golden blocks and finding coverage of frames. The experiments carried out on video and audio application decoding show very promising results.
Index Terms:
Decoding,Approximation algorithms,Multimedia communication,Approximation methods,Streaming media,Data mining,Greedy algorithms,Software Engineering,Data mining,Trace Analysis,Program Comprehension
Citation:
C. Kamdem Kengne, L.C. Fopa, N. Ibrahim, A. Termier, M.C. Rousset, T. Washio, "Enhancing the Analysis of Large Multimedia Applications Execution Traces with FrameMiner," icdmw, pp.595-602, 2012 IEEE 12th International Conference on Data Mining Workshops, 2012
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