A Comprehensive Methodology to Implement Business Intelligence and Analytics Through Knowledge Discovery in Databases
| Autor: | Fernando Paulo Belfo, Alina Banca Andreica |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Business information
030504 nursing Association rule learning Database Computer science business.industry 05 social sciences Big data 050301 education computer.software_genre Business operations Competitive advantage 03 medical and health sciences Knowledge extraction Analytics Business intelligence 0305 other medical science business 0503 education computer |
| Zdroj: | Mining Intelligence and Knowledge Exploration ISBN: 9783030059170 MIKE |
| DOI: | 10.1007/978-3-030-05918-7_10 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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