Abstract
Business intelligence is used by companies for analysing business information, providing not only historical or current views on business operations, but also providing predictions about the business. Consequently, knowledge discovery in databases can support the implementation of business intelligence solutions, especially in order to deal with the reality of big data, using diverse data mining techniques that can help to better prepare the data and to create improved models. The current paper proposes a methodology to implement business intelligence and analytics solutions, based on the CRISP-DM methodology, where the application of simplification and equivalence algorithms in modelling data representations can be used for improving the process of business management. This promising approach can boost business intelligence and analytics by using alternative techniques for discovering and presenting new knowledge about the business. The application of simplification and equivalence algorithms within the business context enables finding the most comprehensive or relevant knowledge, represented for instance as association rules, and bringing a real competitive advantage for the stakeholders.
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Belfo, F.P., Andreica, A.B. (2018). A Comprehensive Methodology to Implement Business Intelligence and Analytics Through Knowledge Discovery in Databases. In: Groza, A., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2018. Lecture Notes in Computer Science(), vol 11308. Springer, Cham. https://doi.org/10.1007/978-3-030-05918-7_10
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