Increasing need for large-scale data analytics in a number of application domains has led to
a dramatic rise in the number of distributed data management systems, both parallel …

Systems performing large data-parallel computations, including online analytical processing
(OLAP) systems like Druid and search engines like Elasticsearch, are increasingly being …

Cloud computing is increasingly being seen as a way to reduce infrastructure costs and add
elasticity, and is being used by a wide range of organizations. Cloud data management …

A key feature of database systems is to provide transparent access to stored data. In
distributed database systems, this includes data allocation and fragmentation. Transparent …

Running parallel database systems in an environment with heterogeneous resources has
become increasingly common, due to cluster evolution and increasing interest in moving …

Providing the ability to elastically use more or fewer servers on demand (scale out and scale
in) as the load varies is essential for database management systems (DBMSes) deployed on …

With the widespread use of shared-nothing clusters of servers, there has been a proliferation
of distributed object stores that offer high availability, reliability and enhanced performance …

Running parallel database systems in an environment with heterogeneous resources has
become increasingly common, due to cluster evolution and increasing interest in moving …

To take full advantage of the parallelism offered by a multi-core machine, one must write
parallel code. Writing parallel code is difficult. Even when one writes correct code, there are …

MapReduce effectively partitions and distributes computation workloads to a cluster of
servers, facilitating today's big data processing. Given the massive data to be dispatched …