Towards semantically enabled development of service-oriented architectures for integration of socio-medical data

The complexity and heterogeneity of “big data” sets in health systems are difficult to process using common database management tools or traditional processing applications. Stimulated by the promising solutions of Semantic Web for addressing the problems of management and monitoring of services shared by different parties, the service-oriented transformations over medical big data are today a rapidly growing demand in almost all sectors and areas. The originality of our paper is in the use of ontologies to bridge this gap between the “open” SOA and “closed” medical and social domains by leveraging ontologies' features to precisely and formally define a domain and yet allow for sharing domain knowledge between collaborating parties. Moreover, in this paper, we used the proven software engineering practices based on different intelligent reasoning data mining methods and techniques in order to develop advanced model of automatic processing and SOA configuration representing sophisticated services for predictions and recommendations based on historical medical and social data.