Mike Grasselt

BPELDT — Data-Aware Extension for Data-Intensive Service Applications

Aside from business processes, the service-oriented approach —currently realized with Web services and BPEL—should be utilizable for data-intensive applications as well. Fundamentally, data-intensive applications are characterized by (i) a sequence of functional operations processing large amounts of data and (ii) the delivery and transformation of huge data sets between those functional activities. However, for the efficient handling of massive data sets, a significant amount of data infrastructure is required and the predefined ‘by value’ data semantic within the invocation of Web services and BPEL is not well suited for this context. To tackle this problem on the BPEL level, we developed a seamless extension to BPEL—the ‘BPEL data transitions’.

Data-aware SOA for Gene Expression Analysis Processes

In the context of genome research, the method of gene expression analysis has been used for several years. Related microarray experiments are conducted all over the world, and consequently, a vast amount of microarray data sets are produced. Having access to this variety of repositories, researchers would like to incorporate this data in their analyses processes to increase the statistical significance of their results. Such analyses processes are typical examples of data-intensive processes. In general, data-intensive processes are characterized by (i) a sequence of functional operations processing large amount of data and (ii) the transportation and transformation of huge data sets between the functional operations. To support data-intensive processes, an efficient and scalable environment is required, since the performance is a key factor today. The service-oriented architecture (SOA) is beneficial in this area according to process orchestration and execution. However, the current realization of SOA with Web services and BPEL includes some drawbacks with regard to the performance of the data propagation between Web services. Therefore, we present in this paper our data-aware service-oriented approach to efficiently support such data-intensive processes.