Complex applications in many areas, including scientific computations and business-related web services, are created from collections of components to form 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 enable users to formulate workflows, but the editors 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 the components selected and their connections; 2) specifying the workflow flexibly, e.g., topdown, bottom-up, from requirements, or from available data; and 3) taking partial or incomplete descriptions of workflows and understanding the steps needed for their completion. We present an approach that combines knowledge bases (that have rich representations of components) together with planning techniques (that can track the relations and constraints among individual steps). We illustrate the approach with an implemented 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.

Abstract

Complex applications in many areas, including scientific computations and business-related web services, are created from collections of components to form 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 enable users to formulate workflows, but the editors 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 the components selected and their connections; 2) specifying the workflow flexibly, e.g., topdown, bottom-up, from requirements, or from available data; and 3) taking partial or incomplete descriptions of workflows and understanding the steps needed for their completion. We present an approach that combines knowledge bases (that have rich representations of components) together with planning techniques (that can track the relations and constraints among individual steps). We illustrate the approach with an implemented 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.

Complex systems are difficult to analyze because of unknown interactions and dependencies among system components, and between the system and the environment. These interactions and dependencies tend to produce unpredictable behaviors that can often lead to detrimental outcomes. Traditional systems engineering that relies on reductive approaches are not suitable for the analysis and design of complex systems. This recognition has led to the development of new paradigms, methods, and tools that better enable: exploration of a complex system's behavior and tradespace; detection, diagnosis, and visualization of component/subsystem dependencies and interactions; and identification, alerting, and circumvention of potentially undesirable interactions. This paper presents a system model-driven interactive storytelling approach for analyzing interactions and dependencies within complex systems, and between complex systems and the environment. An exemplar complex system is used to convey key elements of the approach. The methodology is applicable for a variety of complex systems such as global supply chains, power and energy management, transportation networks, aerospace and aviation enterprises, and defense systems.