As a result of the changing scope of data management towards the management of highly distributed systems and applications, integration processes have gained in importance. Such integration processes represent an abstraction of workflow-based integration tasks. In practice, integration processes are pervasive and the performance of complete IT infrastructures strongly depends on the performance of the central integration platform that executes the specified integration processes. In this area, the three major problems are: (1) significant development efforts, (2) low portability, and (3) inefficient execution. To overcome those problems, we follow a model-driven generation approach for integration processes. In this demo proposal, we want to introduce the so-called GCIP Framework (Generation of Complex Integration Processes) which allows the modeling of integration process and the generation of different concrete integration tasks. The model-driven approach opens opportunities for rule-based and workload-based optimization techniques.

The efficient execution of integration processes between distributed, heterogeneous data sources and applications is a challenging research area of data management. These integration processes are an abstraction for workflow-based integration tasks, used in EAI servers and WfMS. The major problem are significant workload changes during runtime. The performance of integration processes strongly depends on those dynamic workload characteristics, and hence workload-based optimization is important. However, existing approaches of workflow optimization only address the rule-based optimization and disregard changing workload characteristics. To overcome the problem of inefficient process execution in the presence of workload shifts, here, we present an approach for the workload-based optimization of instance-based integration processes and show that significant execution time reductions are possible.

So far the optimization of integration processes between heterogeneous data sources is still an open challenge. A first step towards sufficient techniques was the specification of a universal benchmark for integration systems. This DIPBench allows to compare solutions under controlled conditions and would help generate interest in this research area. However, we see the requirement for providing a sophisticated toolsuite in order to minimize the effort for benchmark execution. This demo illustrates the use of the DIPBench toolsuite. We show the macro-architecture as well as the micro-architecture of each tool. Furthermore, we also present the first reference benchmark implementation using a federated DBMS. Thereby, we discuss the impact of the defined benchmark scale factors. Finally, we want to give guidance on how to benchmark other integration systems and how to extend the toolsuite with new distribution functions or other functionalities.