\section{CONCLUSION AND FUTURE WORK}
\label{sec:Conclusion}


In this paper, we introduced a generic and extensible approach to improve the local data processing in control-flow-oriented workflow execution environments. This approach is targeted at data-intensive workflows, but it is also applicable to other kinds of workflows in multiple scientific or business domains. It includes various techniques to partition the local data processing tasks to be performed during workflow execution in an improved way. These tasks are either assigned to the workflow runtime or to the tightly integrated local database engine. The techniques encompass the execution of variable assignments, expression evaluations for control flow decisions, and Web Service calls by the database engine, as well as storing literal values in this database during workflow deployment. Based on our ODE-TI prototype, which enforces these techniques, we evaluated their effectiveness by means of small test workflows and a more complex and data-intensive protein modeling workflow. The test results demonstrated that the techniques improve the efficiency and reliability of the local data processing in data-intensive workflows. So, our approach broadens the set of such workflows that may be described in terms of control flow. %This leads to a common level of abstraction for both data-intensive and orchestration workflows, which enables a comprehensive process re-engineering across all workflows inducing further performance improvements.

Future work will try to extend the scalability of our ODE-TI prototype with respect to larger \mbox{XML document} sizes. Furthermore, we will employ main-memory-based techniques to further improve performance. This might even entail that ODE-TI may already be used for smaller data sizes and for less complex expressions and workflows.

%The tests to evaluate each optimization technique in isolation have shown that the underlying database system causes performance degradations depending on the XML data size. As the data size for data-intensive workflows may reach several GBs or even more, we will extend our ODE-TI prototype with database capabilities that can cope with such amounts of data, \eg with XML-native DBSs. For data not having high persistence requirements, we will employ main-memory-based techniques to further improve performance. This might even entail that our optimization techniques may be applied for a larger set of orchestration workflows. Based on additional measurements, we will be able to detail on optimization decisions and integrate a data processing optimizer into ODE-TI.