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Modelling in Support of Decision Making in Business Intelligence
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Author(s): Roumiana Ilieva (Technical University of Sofia, Bulgaria), Malinka Ivanova (Technical University of Sofia, Bulgaria), Tzvetilina Peycheva (IBS, Bulgaria)and Yoto Nikolov (Technical University of Sofia, Bulgaria)
Copyright: 2021
Pages: 30
Source title:
Integration Challenges for Analytics, Business Intelligence, and Data Mining
Source Author(s)/Editor(s): Ana Azevedo (CEOS.PP, ISCAP, Polytechnic of Porto, Portugal)and Manuel Filipe Santos (Algoritmi Centre, University of Minho, Guimarães, Portugal)
DOI: 10.4018/978-1-7998-5781-5.ch006
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Abstract
Modelling in support of decision making in business intelligence (BI) starts with exploring the BI systems, driven by artificial intelligence (AI). The purpose why AI will be the core of next-gen analytics and why BI will be empowered by it are determined. The role of AI and machine learning (ML) in business processes automation is analyzed. The benefits from AI integration in BI platforms are summarized. Then analysis goes through predictive modeling in the domain of e-commerce. The use of ML for predictive modeling is overviewed. Construction of predictive and clustering models is proposed. After that the importance of self-services in BI platforms is outlined. In this context the self-service BI is defined and what are the key steps to create successful self-service BI model are sketched. The effects of potential threads which are the results of the big data in the business world are examined and some suggestions for the future have been made. Lastly, game-changer trends in BI and future research directions are traced.
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