DEVELOPMENT OF A HYBRID MODEL FOR ENHANCING DATA INTEGRATION PROCESS OF BUSINESS INTELLIGENCE SYSTEM

Abstract

Business Intelligence (BI) is predicated in helping people in management and experts to make accurate decisions using relevant information at the right time. It can also help to make more accurate forecast and strategies for organisations’ business growth; data integration process that can handle very huge data (the big data phenomenon). The paper presents a hybrid model of two data integration techniques with case-based reasoning (CBR) as intelligent technique. It was developed with Java Script, Hypertext Pre-Processor (PhP), and My Structured Query Language (MySQL) programming languages using object-oriented analysis and design methodology (OOADM). The system was tested with disease control procedure in health sector industry in order to show the benefit of enhancing the data integration process in Business Intelligence systems. The results showed that the hybrid model of ontology-based data integration (OBDI) and virtual data integration (VDI) techniques had a higher enhanced BI process performance of 95% as against 75%and 65% for OBDI and VDI respectively. This shows that the hybrid model provided a better accuracy in predicting the disease control procedure, as it outperformed the existing model with 20% performance level.