Self-Adaptive Execution of Data-Aware Workflow Processes

Abstract

Self-adaptive execution of workflow processes by dynamically and autonomously updating their decisions is especially important for improving the quality of business management. However, the existing methods neglect the impacts of data relationships and temporal constraints on modeling and execution of workflow processes. In this article, based on sprouting graph we propose a new approach for self-adaptive decision-making for dynamic execution of data-aware workflow processes. First, a data-oriented sprouting graph is developed for retrieving information on data-aware workflow processes, so as to eliminate incorrect paths and handle waiting situations. Second, decision point setting and self-adaptive decision strategies are investigated for solving two fundamental decision problems: waiting for information and selecting one among several paths. Third, the whole procedure of automatic implementation is proposed for self-adaptive execution of data-aware workflow processes. Compared with the existing methods, our approach can improve the efficiency of analysis by reducing the model size and can significantly minimize the overall operational cost of the workflow processes.