2 BIAL-X https://www.bial-x.com/ Abstract. Over the past decade, the data lake concept has emerged as an alternative to data warehouses for storing and analyzing big data. A data lake allows storing data without any predefined schema. Therefore, data querying and analysis depend on a metadata system that must be efficient and comprehensive. However, metadata management in data lakes remains a current issue and the criteria for evaluating its effectiveness are more or less nonexistent. In this paper, we introduce MEDAL, a generic, graph-based model for metadata management in data lakes. We also propose evaluation criteria for data lake metadata systems through a list of expected features. Eventually, we show that our approach is more comprehensive than existing metadata systems.
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 decisionmaking 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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