The MapDS-Onto Framework for Matching Formula Factors of KPIs and Database Schema: A Case Study of the Prince of Songkla University

Abstract

Strategy monitoring is essential for business management and for administrators, including managers and executives, to build a data-driven organization. Having a tool that is able to visualize strategic data is significant for business intelligence. Unfortunately, there are gaps between business users and information technology departments or business intelligence experts that need to be filled to meet user requirements. For example, business users want to be self-reliant when using business intelligence systems, but they are too inexperienced to deal with the technical difficulties of the business intelligence systems. This research aims to create an automatic matching framework between the key performance indicators (KPI) formula and the data in database systems, based on ontology concepts, in the case study of Prince of Songkla University. The mapping data schema with ontology (MapDS-Onto) framework is created through knowledge adaptation from the literature review and is evaluated using sample data from the case study. String similarity methods are compared to find the best fit for this framework. The research results reveal that the “fuzz.token_set_ratio” method is suitable for this study, with a 91.50 similarity score. The two main algorithms, database schema mapping and domain schema mapping, present the process of the MapDS-Onto framework using the “fuzz.token_set_ratio” method and database structure ontology to match the correct data of each factor in the KPI formula. The MapDS-Onto framework contributes to increasing self-reliance by reducing the amount of database knowledge that business users need to use semantic business intelligence.

Keywords

• ontology
• business intelligence
• string similarity search
• key performance indicators
• case study-the prince of songkla university