Patricia Lopez Cueva - Debugging Embedded Multimedia Application Execution Traces through Periodic Pattern Mining

Organisé par : 

Patricia Lopez Cueva

Équipes : 

- Location

  • Grenoble University main campus
  • UFR IM2AG building F, room F018.
  • Address : 60 rue de la Chimie, Saint Martin d’Hères

- Jury

  • President : Frédéric Pétrot, Pr, Grenoble INP
  • Reviewer : Hiroki Arimura, Pr, Hokkaido University, Japan
  • Reviewer : Gilles Sassatelli, DR CNRS, LIRMM
  • Examiner : Jean-François Boulicaut, Pr, INSA Lyon
  • Examiner : Takashi Washio, Osaka University, Japan
  • Advisor : Jean-François Méhaut, Pr, Université Joseph Fourier
  • Co-advisor : Alexandre Termier, MCF, Université Joseph Fourier
  • Co-advisor : Miguel Santana, STMicroelectronics

- See also :

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 software debugging and validation phases can help to reduce development time significantly. A powerful tool used extensively when debugging embedded systems is the analysis of execution traces. However, evolution in embedded system tracing techniques leads to execution traces with a huge amount of information, making manual trace analysis unmanageable. In such situations, pattern mining techniques can help by automatically discovering interesting patterns in large amounts of data. Concretely, in this thesis, we are interested in discovering periodic behaviors in multimedia applications. Therefore, the contributions of this thesis are focused on the definition of periodic pattern mining techniques for the analysis of multimedia applications execution traces.

Regarding periodic pattern mining contributions, we propose a definition of periodic pattern adapted to the characteristics of concurrent software. We then propose a condensed representation of the set of frequent periodic patterns, called Core Periodic Concepts (CPC), by adopting an approach originated in triadic concept approach. Moreover, we define certain connectivity properties of these patterns that allow us to implement an evercient CPC mining algorithm, called PerMiner. Then, we perform a thorough analysis to show the everciency and scalability of PerMiner algorithm. We show that PerMiner algorithm is at least two orders of magnitude faster than the state of the art. Moreover, we evaluate the everciency of PerMiner algorithm over a real multimedia application trace and also present the speedup achieved by a parallel version of the algorithm.

Then, regarding embedded systems contributions, we propose a first step towards a methodology which aims at giving the first guidelines of how to use our approach in the analysis of multimedia applications execution traces. Besides, we propose several ways of preprocessing execution traces and a competitors finder tool to postprocess the mining results. Moreover, we present a CPC visualization tool, called CPCViewer, that facilitates the analysis of a set of CPCs. Finally, we show that our approach can help in debugging multimedia applications through the study of two use cases over real multimedia application execution traces.