Efficient mining of association rules using closed itemset lattices
Information Systems
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ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
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ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
SoC-TRACE: handling the challenge of embedded software design and optimization
Proceedings of the Posters and Demo Track
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Increasing complexity in both the software and the underlying hardware, and ever tighter time-to-market pressures are some of the key challenges faced when designing multimedia embedded systems. Optimizing the debugging phase can help to reduce development time significantly. A powerful approach used extensively during this phase is the analysis of execution traces. However, huge trace volumes make manual trace analysis unmanageable. In such situations, Data Mining can help by automatically discovering interesting patterns in large amounts of data. In this paper, we are interested in discovering periodic behaviors in multimedia applications. Therefore, we propose a new pattern mining approach for automatically discovering all periodic patterns occurring in a multimedia application execution trace. Furthermore, gaps in the periodicity are of special interest since they can correspond to cracks or drop-outs in the stream. Existing periodic pattern definitions are too restrictive regarding the size of the gaps in the periodicity. So, in this paper, we specify a new definition of frequent periodic patterns that removes this limitation. Moreover, in order to simplify the analysis of the set of frequent periodic patterns we propose two complementary approaches: (a) a lossless representation that reduces the size of the set and facilitates its analysis, and (b) a tool to identify pairs of "competitors" where a pattern breaks the periodicity of another pattern. Several experiments were carried out on embedded video and audio decoding application traces, demonstrating that using these new patterns it is possible to identify abnormal behaviors.