A New Approach to Improve Association Rules for Big Data in Cloud Environment

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