pour l’obtention du GRADE DE DOCTEUR

1) Numéro d’ordre a ̀ demander au Bureau de l’École Doctorale avant le tirage définitif de la thèse. ii Along with the development of hardware and software, more and more data is generated at a rate much faster than ever. Processing large volume of data is becoming a challenge for data analysis software. Additionally, short response time requirement is demanded by interactive operational data analysis tools. For addressing these issues, people look for solutions based on parallel computing. Traditional approaches rely on expensive high-performance hardware, like supercomputers. Another approach using com-modity hardware has been less investigated. In this thesis, we are aiming to utilize commodity hardware to resolve these issues. We propose to utilize a parallel programming model issued from cloud computing, MapReduce, to parallelize multidimensional analytical query processing for benefit it