Many big-data applications are batch applications that exploit dedicated frameworks to
perform massively parallel computations across clusters of machines. The time needed to …

Apache Spark is probably the most widely adopted framework for develo** big-data batch
applications and for executing them on a cluster of (virtual) machines. In general, the more …

We propose a new model for data processing programs. Our model generalizes the data
flow programming style implemented by systems such as Apache Spark, DryadLINQ …

This paper proposes a model for specifying data flow-based parallel data processing
programs agnostic of target Big Data processing frameworks. The paper focuses on the …

The execution of Spark applications is based on the execution order and parallelism of the
different jobs, given data and available resources. Spark reifies these dependencies in a …

Virtualization technologies have enabled a new way of thinking of computing resources and
cloud computing frameworks offer many pay-per-use solutions for renting these resources …

This paper proposes a model for specifying data flow based parallel data processing
programs agnostic of target Big Data processing frameworks. The paper focuses on the …

We propose a new model for data processing programs. Our model generalizes the data
flow programming style implemented by systems such as Apache Spark, DryadLINQ …

Executive summary DICE aims at defining a general-purpose methodology and toolset to
define data-intensive cloud applications. This means that applications defined with DICE …

The need to crunch a steadily growing amount of data generated by the modern applications
is driving an increasing demand of flexible computing power, often satisfied by cloud …