Building cloud data-intensive applications often requires using multiple data stores (NoSQL,
HDFS, RDBMS, etc.), each optimised for one kind of data and tasks. However, the wide …

The blooming of different cloud data management infrastructures, specialized for different
kinds of data and tasks, has led to a wide diversification of DBMS interfaces and the loss of a …

The blooming of different cloud data management infrastructures has turned multistore
systems to a major topic in the nowadays cloud landscape. In this demonstration, we present …

Big Data has recently gained popularity and has strongly questioned relational databases
as universal storage systems, especially in the presence of analytical workloads. As result …

Modern business intelligence requires data processing not only across a huge variety of
domains but also across different paradigms, such as relational, stream, and graph models …

Multistore systems have been recently proposed to provide integrated access to multiple,
heterogeneous data stores through a single query engine. In particular, much attention is …

In this paper, we try to provide a synthetic and comprehensive state of the art concerning big
data management in cloud environments. In this perspective, data management based on …

With the constant rise of data volumes in many disciplines, various new Big data
management systems have emerged to provide scalable tools for efficient data integration …

In the context of the cloud, we are witnessing a proliferation of data management solutions,
including NoSQL data stores (eg Hbase, MongoDB, Neo4J), file systems (eg HDFS) and …

1Inria and LIRMM, University of Montpellier, France 2LeanXcale and Universidad
Politécnica de Madrid, Madrid, Spain 3Sparsity Technologies, Barcelona, Spain 4INESC …