Youcef Ouinten

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Mining maximal frequent itemsets is an important issue in many data mining applications. In our thesis work on selection and tuning of indices in data werhouses, we have proposed a strategy based on mining maximal frequent itemsets in order to determine a set of candidate indices from a given workload. In a first step we have to select an algorithm, for(More)
Vertical partitioning is a technique used to reduce disk access, when executing a given set of queries, by minimizing the access to irrelevant instance variables. In this paper we use the FP-Max data mining algorithm, for extracting frequent item set attributes. The frequently accessed instance variables are, then, grouped as vertical class fragments. We(More)
Indexing is a fundamental technique used by the administrator to reduce the cost of processing complex queries defined on a data warehouse. However, selecting a suitable configuration of indexes is a difficult problem to solve. The problem is classified as NP-hard. Automatic index selection has received significant attention in the databases field. Most(More)
This paper presents a new algorithm for simultaneous resources sharing by several processes in distributed system. This algorithm treats the problem of message complexity which is an important factor in such a problem, and the waiting time of the requesting sites which can be considered as a quality measure of the distributed algorithms. In this algorithm,(More)