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KABACHI Nadia      
Position : Responsable relations internationales
Organism : Lyon1
Maître de Conférences

Doctoral co-advisor of : RAMDANE Y., RHAZLANE S.,
Team: SID
Room: , Build.: Nautibus - Site : La DOUA

Curriculum Vitae:     
Responsable des relations Internationales au département Informatique (Lyon1)
Membre du Conseil National des Universités (CNU27 - mandat 2012-2015)
Vise-présidente de l’association PrétopoloGics (depuis 2009)
Représentante de LYON1 au Consortium de l'
Research Activities:     
Mon projet de recherche se positionne au sein du domaine des Systèmes Multi-Agents (SMA). Mes activités de recherche s’articulent autour de : Modélisation et Simulation des Systèmes Complexes ; Modélisation Multi-Agents pour les Jeux Coopératifs ; Prétopologie, Théorie des Jeux et Formation de Coalitions ; Interaction et Négociation dans les Systèmes multi-Agents , Agents dans le Cloud.
Research Topics: ?
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Last scientific productions :
+ Optimizing OLAP Cubes Construction by Improving Data Placement on Multi-nodes Clusters doi link

Author(s): Arres B., Kabachi N.(Corresp.), Boussaid O.(Corresp.)

Conference: 23rd EuroPDP International Conference on Parallel, Distributed, and Network-Based P (Turku, FI, 2015-03-04)
Proceedings: , vol. p.520 - 524 (2015)

+ Columnar NoSQL CUBE: Agregation operator for columnar NoSQL data warehouse hal link

Author(s): Dehdouh K., Bentayeb F., Boussaid O., Kabachi N.

Conference: IEEE International Conference on Systems, Man, and Cybernetics (SMC) (San Diego, US, 2014)

+ Towards an OLAP Environment for Column-Oriented Data Warehouses hal link

Author(s): Dehdouh K., Bentayeb F., Boussaid O., Kabachi N.

Conference: Data Warehousing and Knowledge Discovery (DAWAK) (Munich, DE, 2014)

+ Intentional Data Placement Policy for Improving OLAP Cube Construction on Hadoop Clusters hal link

Author(s): Arres B., Kabachi N.(Corresp.), Boussaid O.(Corresp.), Bentayeb F.(Corresp.)

(Posters) 30ème édition de la Conférence Base Données Avancées - BDA 2014 (Autrans, FR), 2014-10,14

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Ref HAL: hal-01166222_v1

In the recent past, we have witnessed dramatic increases in the volume of data literally in every area: business, science, and daily life to name a few. The Hadoop framework - an open source project based on the MapReduce paradigm - is a popular choice for big data analytics. However, the performance gained from Hadoop’s features is currently limited by its default block placement policy, which does not take any data characteristics into account. Indeed, the efficiency of many operations can be improved by a careful data placement, including indexing, grouping, aggregation and joins. In our work we propose a data warehouse partitioning strategy to improve query gain performances. We investigate the performance gain for OLAP cube construction with and without data organization on a Hadoop cluster. And this, by varying the number of nodes and data warehouse size. Our experiments suggest that a good data placement on a cluster during the implementation of the data warehouse can significantly increase the OLAP cube construction and querying performances. In the next step, we will extend the experiments to study the effects of other configuration parameters on collocation data in the context of parallel data warehousing, such as partitions size, replication factor and OLAP query complexity. We plan also to study an in- telligent system for warehouses data placement on clusters by integrating Multi-Agent System (MAS) and Intelligent Agents to the process.

+ An Original Approach for Processing Public Open Data with MapReduce: A Case Study doi link

Author(s): Arres B.(Corresp.), Kabachi N., Bentayeb F., Boussaid O.

Conference: The 11th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA' 2014) (Doha, QA, 2014-11-10)
Proceedings: , vol. p.138- 143 (2014)