%0 Conference Proceedings %T Metadata Systems for Data Lakes: Models and Features %+ Entrepôts, Représentation et Ingénierie des Connaissances (ERIC) %+ BIAL-X %+ BIAL-X %A Sawadogo, Pegdwendé, Nicolas %A Scholly, Etienne %A Favre, Cécile %A Ferey, Eric %A Loudcher, Sabine %A Darmont, Jérôme %< avec comité de lecture %( Communications in Computer and Information Science %B 1st International Workshop on BI and Big Data Applications (BBIGAP@ADBIS 2019) %C Bled, Slovenia %I Springer %3 Communications in Computer and Information Science %V 1064 %P 440-451 %8 2019-09-08 %D 2019 %Z 1909.09377 %R 10.1007/978-3-030-30278-8 %K Data lakes %K Metadata modeling %K Metadata management %Z Computer Science [cs]/Databases [cs.DB]Conference papers %X 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. %G English %2 https://hal.science/hal-02157195/document %2 https://hal.science/hal-02157195/file/bbigap2019.pdf %L hal-02157195 %U https://hal.science/hal-02157195 %~ UNIV-LYON1 %~ UNIV-LYON2 %~ ERIC %~ LYON2 %~ UDL %~ UNIV-LYON