Nabil Benayadi

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We introduce the problem of mining sequential patterns in large database of sequences using a Stochastic Approach. An example of patterns we are interested in is : 50% of cases of engine stops in the car are happened between 0 and 2 minutes after observing a lack of the gas in the engine, produced between 0 and 1 minutes after the fuel tank is empty. We(More)
This paper is concerned with the discovering of temporal knowledge from a sequence of timed observations provided by a system monitoring of dynamic process. The discovering process is based on the Stochastic Approach framework where a series of timed observations is represented with a Markov chain. From this representation, a set of timed sequential binary(More)
Les systèmes de supervision de la plupart des applications industrielles génèrent une très grande quantité d’informations et les collectent dans des bases de données. Ce papier concerne la découverte de modèles de chroniques à partir de séquences d’événements. Chaque événement appartient à une certaine classe. Selon l’approche stochastique (Le Goc et al.(More)
Résumé. Cet article propose d’utiliser l’entropie informationnelle pour analyser des modèles de chroniques découverts selon une approche stochastique (Bouché et Le Goc, 2005). Il décrit une adaptation de l’algorithme TemporalID3 (Console et Picardi, 2003) permettant de découvrir des modèles de chroniques à partir d’un ensemble d’apprentissage contenant des(More)
Modelling manufacturing process of complex products like electronic ships is crucial to maximize the quality of the production. The Process Mining methods developed since a decade aims at modelling such manufacturing process from the timed messages contained in the database of the supervision system of this process. Such process can complex making difficult(More)