Distance-Based Trace Diagnosis for Multimedia Applications: Help Me TED

Execution traces have become essential resources that many developers analyze to debug their applications. Ideally, a developer wants to quickly detect whether there are anomalies on his application or not. However, in practice, the size of multimedia applications trace can reach gigabytes, which ma...

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Bibliographic Details
Published in2013 IEEE Seventh International Conference on Semantic Computing pp. 306 - 309
Main Authors Kengne, Christiane Kamdem, Ibrahim, Noha, Rousset, Marie-Christine, Tchuente, Maurice
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2013
Subjects
Audio/Video decoding
Decoding
Diagnosis
Distance
Execution traces
Motion pictures
Multimedia applications
Multimedia communication
Scalability
Semantics
Streaming media
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Summary:Execution traces have become essential resources that many developers analyze to debug their applications. Ideally, a developer wants to quickly detect whether there are anomalies on his application or not. However, in practice, the size of multimedia applications trace can reach gigabytes, which makes their exploitation very complex. Usually, developers use visualization tools before stating a hypothesis. In this paper, we argue that this solution is not satisfactory and propose to automatically provide a diagnosis by comparing execution traces. We use distance-based models and conduct a user case to show how TED, our automatic trace diagnosis tool, provides semantic added-value information to the developer. Performance evaluation over real world data shows that our approach is scalable.
DOI:10.1109/ICSC.2013.59