Schema on read modeling approach as a basis of big data analytics integration in EIS

Abstract

ABSTRACTBig Data analysis is the process that can help organizations to make better business decisions. Organizations use data warehouses and business intelligence systems, i.e. enterprise information systems (EISs), to support and improve their decision-making processes. Since the ultimate goal of using EISs and Big Data analytics is the same, a logical task is to enable these systems to work together. In this paper we propose a framework of cooperation of these systems, based on the schema on read modeling approach and data virtualization. The goal of data virtualization process is to hide technical details related to data storage from applications and to display heterogeneous data sources as one integrated data source. We have tested the proposed model in a case study in the transportation domain. The study has shown that the proposed integration model responds flexibly and efficiently to the requirements related to adding new data sources, new data models and new data storage technologies.