Supporting Task Constraints and Dependencies in Knowledge-intensive Processes

Knowledge-intensive processes are widely found in modern societies and important to many fields of work. Driven by knowledge gained only during their execution, this type of process brings along challenges like a gradually emerging structure, uncertainty and frequent changes. Through contribution and communication the knowledge workers involved in the process shape and improve it while advancing towards a common goal. One very important part of the knowledge are the dependencies that naturally exist between the tasks on which these workers perform. With the latter often being spatially divided, expressing the dependencies existing for their jobs is a determining factor for success. Relying on insufficient software systems or, even worse, on paperbased solutions for coordination proves to be error-prone and lacks reliance in practical scenarios. Adequate aid in form of information systems, specifically designed for knowledge-intensive processes and their accompanying challenges, and operated on by the knowledge workers themselves, is needed. The work on such systems is a still ongoing endeavor and in need of concepts and solutions. This work presents a concept for the support of constraints to express task dependencies in knowledge-intensive processes. With the focus on the unique challenges coming with the latter, the concept puts great emphasis on providing guidance instead of strict ruling, adaptability to frequent changes and usability in practical scenarios. A rule-based, declarative approach is laid out for applying the concept, designed to be ready for extension, to various systems alongside other, already existing functionality. Based on it, a catalog of constraints is given, with clear semantic meanings and effects, tailored towards the use in knowledge-intensive processes. A proof-of-concept prototype for it was implemented for the process-aware Support for Collaborative Knowledge Workers (proCollab) system, an example of an adequate and sophisticated solution

Personalized adaptation in pervasive systems via non-functional requirements

Pervasive environments are socio-technical systems that support the daily routines of their users in an invisible and unobtrusive manner. These systems are aware of and adapt to both the operational context and the characteristics and preferences of their users. Designing adaptation mechanisms that guarantee maximal user satisfaction is challenging, due to the inherent differences between users and the changing context where the system operates. In order to tackle this problem, we propose an approach that compares alternative system behaviors in terms of how well they satisfy the preferences of the current user concerning Non-Functional Requirements (NFRs) such as efficiency, comfort, energy saving, etc. Specifically, we propose a model-driven framework in which the models represent the user routines that the pervasive system helps to achieve. These routines include variability points, thereby enabling their behavior to be adapted at runtime in order to fit the context and the user preferences over NFRs. Our contributions include: (1) user-adaptive task models, a modeling language to describe user routines that accounts for user preferences over NFRs; (2) algorithms that use our models at runtime to guide a pervasive system in adapting its behavior to user preferences and context; and (3) an implementation and evaluation of our techniques.status: publishe