Abstract The technique of association rules is very useful in Data Mining, but it generates a huge number of rules. So, a manual post-processing is required to target only the interesting rules. Several researchers suggest integrating users' knowledge by using ontology and rule patterns, and then select automatically the interesting rules after generating all possible rules. However, nowadays the business data are extremely increasing, and many companies have already opted for Big Data systems deployed in cloud environments, then the process of generating association rules becomes very hard. To deal with this issue, we propose an approach using ontology with rule patterns to integrate users' knowledge early in the preprocessing step before searching or generating any rule. So, only the interesting rules which respect the rule patterns will be generated. This approach allows reducing execution time and minimizing the cost of the post-processing especially for Big Data. To confirm the performance results, experiments are carried out on Not Only Strutured Query Language (NoSQL) databases which are distributed in a cloud environment.

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[25] Zaki M. and Wagner M., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, 2014. Djilali Dahmani Graduated from Department of computer science, Faculty of exact and applied sciences, University of Science and Technology MB, USTO, Algeria, where he received PhD degree in computer science in 2017. His current research interests are Big data, Cloud computing, Data mining, database models, data science, replication, consistency, fault tolerance, resource management, energy consumption, mobile environment, High Performance Computing. Sidi Ahmed Rahal He is Doctor in computer science since 1989 in Pau University, France. Currently, he is a professor at Department of computer science, University of Science and Technology MB, USTO, Algeria. His current research interests are Data mining, Object-Oriented database, Data Mining, Agents Expert Systems, Big data, Cloud computing, database models. He is a member of SSD (Signal, System and Data) laboratory. Ghalem Belalem Graduated from Department of computer science, Faculty of exact and applied sciences, University of Oran1 Ahmed Ben Bella, Algeria, where he received PhD degree in computer science in 2007. His current research interests are distributed system; grid computing, cloud computing, replication, consistency, fault tolerance, resource management, economic models, energy consumption, Big data, IoT, mobile environment, images processing, Supply chain optimization, Decision support systems, High Performance Computing.