Enhancing the Analysis of Large Multimedia Applications Execution Traces with FrameMiner

Abstract : 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.
Type de document :
Communication dans un congrès
Jilles Vreeken et al. ICDM 2012 workshop: PTDM 2012 - Practical Theories for Exploratory Data Mining, Dec 2012, Brussels, Belgium. IEEE Computer Society, pp.595-602, 2012, 〈10.1109/ICDMW.2012.95〉
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https://hal.archives-ouvertes.fr/hal-00922889
Contributeur : Noha Ibrahim <>
Soumis le : jeudi 2 janvier 2014 - 12:02:30
Dernière modification le : mardi 28 octobre 2014 - 18:34:10

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Christiane Kamdem Kengne, Leon Constantin Fopa, Noha Ibrahim, Alexandre Termier, Marie-Christine Rousset, et al.. Enhancing the Analysis of Large Multimedia Applications Execution Traces with FrameMiner. Jilles Vreeken et al. ICDM 2012 workshop: PTDM 2012 - Practical Theories for Exploratory Data Mining, Dec 2012, Brussels, Belgium. IEEE Computer Society, pp.595-602, 2012, 〈10.1109/ICDMW.2012.95〉. 〈hal-00922889〉

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