![]() ![]() Codd and Associates (1993)ĭiamantini, C., Giudice, P.L., Musarella, L., Potena, D., Storti, E., Ursino, D.: A new metadata model to uniformly handle heterogeneous data lake sources. 1417–1428 (2014)Ĭodd, E., Codd, S., Salley, C.: Providing OLAP (on-line analytical processing) to user-analysts, an IT mandate. ![]() 189, 4–7 (1996)Ĭhen, Z., Narasayya, V., Chaudhuri, S.: Fast foreign-key detection in Microsoft SQL server PowerPivot for excel. In: Proceedings of ICDE (2020)īrooke, J.: SUS: a quick and dirty usability scale. 1942–1945 (2018)īogatu, A., Fernandes, A., Paton, N., Konstantinou, N.: Dataset discovery in data lakes. 22–39 (2019)īeheshti, A., Benatallah, B., Nouri, R., Tabebordbar, A.: CoreKG: a knowledge lake service. In: Proceedings of CIDR (2021)īagozi, A., Bianchini, D., Antonellis, V.D., Garda, M., Melchiori, M.: Personalised exploration graphs on semantic data lakes. 167–174 (2000)Īrmbrust, M., Ghodsi, A., Xin, R., Zaharia, M.: Lakehouse: a new generation of open platforms that unify data warehousing and advanced analytics. KeywordsĪllan, J., Lavrenko, V., Malin, D., Swan, R.: Detections, bounds, and timelines: UMass and TDT-3. Finally, we show the feasibility of our approach using a real-word use case on the one hand, and a benchmark on the other hand. Furthermore, we also innovate by leveraging metadata to activate both data retrieval and content analysis, including Text-OLAP and SQL querying. We implement our approach in the AUDAL data lake, where we jointly exploit both textual documents and tabular data, in contrast with structured and/or semi-structured data typically processed in data lakes from the literature. Thus, we introduce a new approach to design a data lake and propose an extensive metadata system to activate richer features than those usually supported in data lake approaches. However, although trendy in both the industry and academia, the concept of data lake is still maturing, and there are still few methodological approaches to data lake design. ![]() Data lakes follow a schema-on-read approach to provide rich and flexible analyses. In 2010, the concept of data lake emerged as an alternative to data warehouses for big data management. ![]()
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