Séminaire Abdelkader Hameurlain

Abstract:
The purpose of this seminar is to provide a comprehensive state of the art regarding the different generations of Cloud Data Management Systems CDMS focusing on data analysis systems (OLAP).
In this objective, first, we try to draw up a synthetic state of the art of the three generations of CDMS: from the generation of MapReduce systems to the generation of Multistore/Polystore systems through Multi-tenant parallel relational DBMS.  Next, for each generation of these systems, we highlight the underlying concepts, their main features, their advantages and weakness with respect to the requirements of large-scale data applications in terms of software engineering (e.g. data-program independence), high performance,  availability, scalability and elasticity. Lastly, based on Seatle's report on DB Research, we describe some open issues that should be tackled to ensure the viability of the next generation of CDMS for new targeted large-scale data applications.

Short Biography: 
Abdelkader Hameurlain has been full Professor in Computer Science (until August 31, 2021) at Paul Sabatier University PSU (Toulouse, France) and currently (since 1st September 2021), Emerit. Professor at PSU (==>Toulouse University), IRIT Lab (https://www.irit.fr/). He is a member of the Institute of Research in Computer Science of Toulouse (IRIT). His current research interests are in query processing and optimization methods in parallel, cloud, and large-scale distributed environments. Prof. Hameurlain has been the general chair of the International Conference on Database and Expert Systems Applications (DEXA'02, DEXA'2011, DEXA'2017 and DEXA'2018). He is co-editors in Chief of the International Journal "Transactions on Large-scale Data and Knowledge Centered Systems" (https://www.irit.fr/tldks/; LNCS, Springer).

Key Words: Cloud Computing, Data Management, Databases, Parallelism, Data Partitioning, Parallel Relational Database Systems, Cloud Data Management Systems, Multistore/Polystore Systems, NewSQL Systems, High Performance, Scalability, Elasticity, Hadoop, MapReduce, Spark, Spanner, LeanXcale.