A Knowledge-Based Approach to Interactive Workflow Composition

Complex applications in many areas, including scientific computations and business-related web services, are created from collections of components to form computational workflows. In many cases end users have requirements and preferences that depend on how the workflow unfolds, and that cannot be specified beforehand. Workflow editors therefore need to be augmented with intelligent assistance in order to help users in several key aspects of the task, namely: 1) keeping track of detailed constraints across selected components and their connections; 2) accommodating flexibly different strategies to construct workflows; e.g., from general knowledge of necessary tasks, from desired results, or from available data; and 3) taking partial or incomplete descriptions of workflows and understanding the steps needed for their completion. We have developed a system called CAT (Composition Analysis Tool) that analyzes workflows and generates error messages and suggestions in order to help users compose complete and consistent workflows. Our approach combines knowledge bases, which have rich representations of components and constraints, together with planning techniques that can track the relations and constraints among individual components. We have formalized our approach based on planning techniques, allowing us to formulate claims about the underlying algorithms as well as the resulting workflows.