Affiliations: [a] Department de mathematiques et informatique, Universite des Sciences et Technologie, USTO-MB, Oran, Algeria | [b] Department of computer science, University of Oran 1, Ahmed Ben Bella, Oran, Algeria
Abstract: Nowadays, many NoSQL systems are developed to deal with data elasticity in distributed environments. This is very useful for Data mining such as association rules technique which generates a huge number of rules. To avoid any manual post-processing for selecting the interesting rules, many researchers suggest integrating expert users’ knowledge by using ontology and rule patterns. Nevertheless, with NoSQL Big Data that contain very large data, the number of generated rules is so huge that any post-processing becomes complicated especially in industrial areas. Also, any solution and results have to be tested and checked with a real Big Data context. In order to deal with this issue, we use an adjusted approach with ontology and rule patterns to reduce database NoSQL context before generating any rule. After that, we conduct a real experiment on distributed industrial MongoDB database to calculate execution time and generated rules. This work proves the gain in performance for using association rules with ontology in the NoSQL systems.
Keywords: Ontology, NoSQL, association rules, big data, MongoDB
