Title :
Enhancing the Analysis of Large Multimedia Applications Execution Traces with FrameMiner
Author :
Kengne, Christiane Kamdem ; Fopa, L.C. ; Ibrahim, Niko ; Termier, Alexandre ; Rousset, M.C. ; Washio, Takashi
Author_Institution :
LIG, Univ. of Grenoble, St. Martin d´Heres, France
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 k-golden set of blocks that best characterize data. Sequential pattern mining can help to automatically discover the blocks, and we called 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.
Keywords :
data mining; multimedia computing; FrameMiner; audio application decoding; golden blocks mining; k-golden set; large multimedia applications execution traces; low-level events; sequential pattern mining; video application decoding; Approximation algorithms; Approximation methods; Data mining; Decoding; Greedy algorithms; Multimedia communication; Streaming media; Data mining; Program Comprehension; Software Engineering; Trace Analysis;
Conference_Titel :
Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on
Conference_Location :
Brussels
Print_ISBN :
978-1-4673-5164-5
DOI :
10.1109/ICDMW.2012.95
