Today’s business workflows are very likely to include batch computations that periodically analyze subsets of data relative to specific time ranges in order to provide strategic information for stakeholders and other interested parties. The frequency of these computations directly impacts on how much updated such information can be, which provides an effective measure of their usefulness. This ...

We consider the problem of on-line scheduling a set of n jobs on two parallel batch processing machines. Each machine can handle an infinite number of jobs as a batch simultaneously. The processing time of a batch is the time required for processing the longest job in the batch. Each job becomes available at its release date, which is not known in advance, and its processing time only becomes k...

A streamlined scientific workflow system that can track the details of the data processing history is critical for the efficient handling of fundamental routines used in scientific research. In the scientific workflow research community, the information that describes the details of data processing history is referred to as "provenance" which plays an important role in most of the existing work...

EEG is a rich source of information regarding brain functioning, and is the most lightweight and affordable method of brain imaging. However, the pre-processing of EEG data is quite complicated and most existing tools present the experimenter with a large choice of methods for analysis, but no framework for method comparison to choose an optimal approach. Additionally, many tools still require ...

MapReduce is a popular framework for data-intensive distributed computing of batch jobs. To simplify fault tolerance, many implementations of MapReduce materialize the entire output of each map and reduce task before it can be consumed. In this paper, we propose a modified MapReduce architecture that allows data to be pipelined between operators. This extends the MapReduce programming model bey...