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Yoann PITARCH

MONTPELLIER

En résumé

I am currently looking for a postdoc or a research engineer position in the fields of databases, data warehouses and data mining.

Indeed, I have recently obtained a PhD in computer science at the Université Montpellier 2 where I also obtained a Master of Computer Science.

During my PhD, my main research domain was Data Stream Summarization. More specifically, my work addresses the problem of constructing compact cubes from multidimensional data streams and then mainly focuses on :

- Original and new pattern mining approaches for efficiently summarize the stream ;
- Efficient summarization technique for limiting the number of stored cuboids as well as a query extension to provide the best answer according to the summarization ;
- A new approach for aggregating data according to the context.

My PhD was founded by the French National Agency of Research (ANR) through the MIDAS project involving numerous academic partners (such as LIRMM, INRIA or Telecom ParisTech) and major business firms (such as EDF R&D or Orange Labs)

In parallel, I am also working on several other topics such as:

- (Multidimensional) Sequential Pattern Mining in Static Databases
- Spatiotemporal Data Mining
- Data Warehouse Design
- Flexible Queries
- Interaction of Multidimensional Data


During my PhD, I have also taught at the Institute of Technology at the University of Montpellier 2. My courses were mainly shared among the following modules: Data Mining, Database, Programming Language (ADA, Scheme, PHP), Information System, Database Design, Object Oriented Design, Computer Architecture.

Do not hesitate to contact me if you are interested in one of those topics!

Compétences

Data mining, Data warehouse conception, Data stream management system

Entreprises

  • LIRMM (CNRS/UM2) - Ph.D. student in Computer Sciences

    2008 - maintenant Keywords: Data stream, Hierarchy, Frequent pattern, Data warehouse, Summarization technique

    Abstract:
    Due to the rapid increase of information and communication technologies, the amount of generated and available data exploded and a new kind of data, stream data, has appeared. One possible and common definition of data stream is an unbounded sequence of very precise data incoming at high rate. Thus, it is impossible to store such a stream to perform a posteriori analysis. Moreover, more and more streams concern multidimensional and multilevel data and very few approaches take these specificities into account. Thus, we propose some practical and efficient solutions to deal with such particular data in a dynamic context. More specifically, we are interested in adapting OLAP (On Line Analytical Processing ) techniques to build relevant summaries of the data. First, after describing and discussing existent similar approaches, we propose two solutions allowing to build a data cube on stream data. Second, we investigate the combination of frequent patterns and hierarchies to build a summary based on new generalized sequences. Third, even if there exist a lot of types of hierarchies in the literature, none of them integrates the expert knowledge during the generalization phase. However, such an integration could be very relevant to build semantically richer summaries. We tackled this issue by proposing a new type of hierarchies, namely the contextual hierarchies. Thanks to this new type of hierarchies, we propose a new conceptual, graphical and logical data warehouse model, namely the contextual data warehouse. Finally, since this work is founded by the ANR through the MIDAS project, we evaluate our approaches on real datasets provided by the industrial partners of this project (e.g., Orange Labs or EDF R&D).
  • Université Montpellier 2 - Assistant professor

    Montpellier 2008 - maintenant My courses were mainly shared among the following modules: Data Mining, Database, Programming Language (ADA, Scheme, PHP), Information System, Database Design, Object Oriented Design, Computer Architecture.

Formations

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