Abstract
Business Intelligence, with data warehouses, reporting and OnLine Analytical Processing (OLAP) are about twenty years old technologies, they are mastered and widely used in companies. Their goal is to collect, organize, store and analyse data to support decision-making. In parallel, there are many algorithms from Data Science for conducting advanced data analyses, including the ability to conduct predictive analyses. However, the reflection on the integration of Data Science methods into reporting or OLAP analysis is relatively incomplete, although there is a real demand from companies to integrate prediction into decision-making processes. In the meantime, with the rise of the Internet, the proliferation of multimedia data (sound, image, video, etc.), and the fast development of social networks, data has become massive, heterogeneous, of diverse and rapid varieties. The Big Data phenomenon challenges the process of data storage and analysis and creates new research problems.
The PhD thesis is at the junction of these three main topics: Business Intelligence, Data Science and Big Data. The objective is to propose an approach, a framework and finally an architecture allowing prediction to be made in a decision-making process, but with a Big Data perspective.
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Scholly, É. (2019). Business Intelligence & Analytics Applied to Public Housing. In: Welzer, T., et al. New Trends in Databases and Information Systems. ADBIS 2019. Communications in Computer and Information Science, vol 1064. Springer, Cham. https://doi.org/10.1007/978-3-030-30278-8_53
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