A Generic Parallel Processing Model for Facilitating Data Mining and Integration

To facilitate Data Mining and Integration (DMI) processes in a generic way, we investigate a parallel pipeline streaming model. We model a DMI task as a streaming data-flow graph: a directed acyclic graph (DAG) of Processing Elements PEs. The composition mechanism links PEs via data streams, which may be in memory, buffered via disks or inter-computer data-flows. This makes it possible to build arbitrary DAGs with pipelining and both data and task parallelisms, which provides room for performance enhancement. We have applied this approach to a real DMI case in the Life Sciences and implemented a prototype. To demonstrate feasibility of the modelled DMI task and assess the efficiency of the prototype, we have also built a performance evaluation model. The experimental evaluation results show that a linear speedup has been achieved with the increase of the number of distributed computing nodes in this case study.