Knowledge integration of distributed enterprises using cloud based big data analytics

Abstract

An efficient and adaptive centralization of disparate knowledge sources is a key factor for the success of knowledge management systems (KMS) in distributed heterogeneous enterprises such as healthcare and production systems with large scale and disparate knowledge sources. Professional end users of such systems (e.g. physicians and production engineers) should have nearly real-time access to the records to provide relevant decisions without any difficulty of dealing with IT systems. Analysis and reasoning of professional end users' feedback lead helpful knowledge bases which can be used for future decision making situations such as diagnostics and predictions. This paper represents an overview of the cloud computing based conceptual framework for the integration of distributed knowledge sources in large scale heterogeneous enterprises such as grouped hospitals, clinic chains, production systems, supply chains and etc. The NoSQL database is used for providing a scalable data analytics. This framework facilitates accessible, efficient and always available knowledge bases for collaborative systems and reduces redundancy and costs by sharing the knowledge between individuals and experts. Three different application scenarios will be used to test and evaluate the efficiency of the proposed framework. Distributed product design and development, healthcare knowledge integration and providing readability in drug leaflets are target application domains of this research.