Publication - Calcul parallèle de dépendances Envoyer
Publications

Calcul parallèle de dépendances

Auteurs : Eve Garnaud, Nicolas Hanusse, Sofian Maabout, Noël Novelli

Conférence : 29e journées Bases de Données Avancées (BDA 2013), Nantes France (2013)

 
Publication - Adapting Simulation Modeling to Model-Driven Architecture for Model Requirement Verification Envoyer
Publications

Adapting Simulation Modeling to Model-Driven Architecture for Model Requirement Verification

Auteurs : Fuqi Song, Gregory Zacharewicz, David Chen

Conference3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications, Jul 2013, Reykjavik, Iceland. Proceedings of 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications, pp. 302-309

Web : http://hal.archives-ouvertes.fr/hal-00849770

 
Publication - Pattern-Based Core Word Recognition to Support Ontology Matching Envoyer
Publications

Pattern-Based Core Word Recognition to Support Ontology Matching

Auteurs : Fuqi Song, Gregory Zacharewicz, David Chen

Journal : International Journal of Knowledge-Based and Intelligent Engineering Systems, 2013, 17 (2), pp. 167-176

Webhttp://hal.archives-ouvertes.fr/hal-00816045

 
Publication - Using Functional Dependencies for Reducing the Size of a Data Cube Envoyer
Publications

Using Functional Dependencies for Reducing the Size of a Data Cube

Auteurs : Eve Garnaud, Sofian Maabout, Mohamed Mosbah

Conf : Foundations of Information and Knowledge Systems (FoIKS?12);Kiel, Allemagne (2012)

Web : http://link.springer.com/chapter/10.1007%2F978-3-642-28472-4_9

Functional dependencies (FD’s) are a powerful concept in data organization. They have been proven very useful in e.g., relational databases for reducing data redundancy. Little work however has been done so far for using them in the context of data cubes. In the present paper, we propose to characterize the parts of a data cube to be materialized with the help of the FD’s present in the underlying data. For this purpose, we consider two applications: (i) how to choose the best cuboids of a data cube to materialize in order to guarantee a fixed performance of queries and, (ii) how to choose the best tuples, hence partial cuboids, in order to reduce the size of the data cube without loosing information. In both cases we show how FD’s are fundamental.

 
Publication - A Parallel Algorithm for Computing Borders Envoyer
Publications

A Parallel Algorithm for Computing Borders

Auteurs : Hanusse Nicolas, Maabout Sofian

Conf : 20th Conference on Information and Knowledge Management (CIKM?11) 24-28 octobre,Glasgow, Ecosse (2011)

Web : http://dl.acm.org/citation.cfm?doid=2063576.2063814

The border concept has been introduced by Mannila and Toivonen in their seminal paper [20]. This concept finds many applications, e.g maximal frequent itemsets, minimal functional dependencies, emerging patterns between consecutive database instances and materialized view selection. For large transactions and relational databases defined on n items or attributes, the running time of any border computations are mainly dominated by the time T (for standard sequential algorithms) required to test the interestingness, in general the frequencies, of sets of candidates.

In this paper we propose a general parallel algorithm for computing borders whatever the application is. We prove the efficiency of our algorithm by showing that: (i) it generates exactly the same number of candidates as the standard sequential algorithm and, (ii) if the interestingness test time of a candidate is bounded by Δ then for a multi-processor shared memory machine with p cores, we prove that the total interestingness time Tp < T/p + 2 Δ n. We implemented our algorithm in the maximal frequent itemset (MFI) mining setting and our experiments confirm our theoretical performance guarantee.

 
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