Workflow Refactoring for Maximizing Concurrency and Block-Structuredness

In the era of Internet and big data, contemporary workflows become increasingly large in scale and complex in structure, introducing greater challenges for workflow modeling. Workflows are not with maximized concurrency and block-structuredness in terms of control flow, though languages supporting block-structuredness (e.g., BPEL) are employed. Existing workflow refactoring approaches mostly focus on maximizing concurrency according to dependences between activities, but do not consider the block-structuredness of the refactored workflow. It is easier to comprehend and analyze a workflow that is block-structured and to transform it into BPEL-like processes. In this paper, we aim at maximizing both concurrency and block-structuredness. Nevertheless, not all workflows can be refactored with a block-structured representation, and it is intractable to make sure that the refactored workflows are as block-structured as possible. We first define a well-formed dependence pattern of activities. The control flow among the activities in this pattern can be represented in block-structured forms with maximized concurrency. Then, we propose a greedy heuristics-based graph reduction approach to recursively find such patterns. In this way, the resulting workflow is with maximized concurrency and its block-structuredness approximates optimality. We show the effectiveness and efficiency of our approach with real-world scientific workflows.