1.Modern Data Management Many technological changes Networks Parallelism (Cluster computing & Multicore ) Storage systems Processor technology New applications Web apps Streaming data apps Business analysis Non-business apps From traditional data management to modern data management

2.New applications Very large amounts of data Very volatile data Data as streams Data is not always structured in the database sense Uncertain data …

3.Challenges ! 3 Data management infrastructure Distribution Query optimization (case of a sensor network )–parallel processing Intelligent indexing Stream processing Data quality Semantics of Data Intelligent interaction and information discovery Tools for data analysis Integrating symbolic computation, mining and analysis Data visualization and application development …

4.Presentations Agile data management challenges in enterprise big data landscapes (by Eric Simon) Unification of the management of both enterprise data and big data Governance at scale of an enterprise big data landscape through a unified view Development of an environment for the creation of analytics by the masses Querying and Exploring Big Scientific Data (by Thomas Heinis ) Brain simulation data : challenges for data management systems Neurosciences - Simulation of human brain : new approach (indexing and navigation) Benchmarking SQL-On- MapReduce systems (by Mohand-Saïd Hacid ) SQL MapReduce Systems for large data sets A new architecture (BSP (Bulk Synchronous Processing)- based)