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Current trends in medicine regarding issues of accessibility to and the quantity and quality of information and quality of service are very different compared to former decades. The current state requires new methods for addressing the challenge of dealing with enormous amounts of data present and growing on the Web and other heterogeneous data sources such as sensors and social networks and unstructured data, normally referred to as big data. Traditional approaches are not enough, at least on their own, although they were frequently used in hybrid architectures in the past. In this paper, we propose an architecture to process big data, including heterogeneous sources of information. We have defined an ontology-oriented architecture, where a core ontology has been used as a knowledge base and allows data integration of different heterogeneous sources. We have used natural language processing and artificial intelligence methods to process and mine data in the health sector to uncover the knowledge hidden in diverse data sources. Our approach has been applied to the field of personalized medicine (study, diagnosis, and treatment of diseases customized for each patient) and it has been used in a telemedicine system. A case study focused on diabetes is presented to prove the validity of the proposed model.

An Ontology-Oriented Architecture for Dealing With Heterogeneous Data Applied to Telemedicine Systems