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

fYear

2012

fDate

10-10 Dec. 2012

Firstpage

595

Lastpage

602

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on

Conference_Location

Brussels

Print_ISBN

978-1-4673-5164-5

Type

conf

DOI

10.1109/ICDMW.2012.95

Filename

6406406