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Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
ISBN: 978-1-4673-5164-5
pp: 595-602
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.
Decoding, Approximation algorithms, Multimedia communication, Approximation methods, Streaming media, Data mining, Greedy algorithms, Software Engineering, Data mining, Trace Analysis, Program Comprehension
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, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 595-602, doi:10.1109/ICDMW.2012.95
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