Customer profiling using CEP architecture in a Big Data context

Today Big Data tools are not just a phenomenon of the massive information collection; they are also the best way to approach a customer target. These technologies allow the profiling of the customers of an organization thanks to the histories of purchases, the products that they consult; the data that they share through the social networks. They also make it possible to anticipate the purchase of actions via behavioral analysis. Therefore, the combination of the power of CRM and the performance of BIG DATA tools brings a great added value for customers profile analysis, especially if it is about events triggered in real time. It is in this context that the present work is positioned. Our goal is to intercept events (customer behaviors) and analyze them in real time. We will use the Complex Events Process (CEP) architecture that perfectly meets this need. In order to successfully implement our CEP architecture, we will use the ontology approach.

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