Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches

Engineering of knowledge-intensive processes (KiPs) is far from being mastered, since they are genuinely knowledge- and data-centric, and require substantial flexibility, at both design- and run-time. In this work, starting from a scientific literature analysis in the area of KiPs and from three real-world domains and application scenarios, we provide a precise characterization of KiPs. Furthermore, we devise some general requirements related to KiPs management and execution. Such requirements contribute to the definition of an evaluation framework to assess current system support for KiPs. To this end, we present a critical analysis on a number of existing process-oriented approaches by discussing their efficacy against the requirements

Context-aware Process Management for the Software Engineering Domain

Historically, software development projects are challenged with problems concerning budgets, deadlines and the quality of the produced software. Such problems have various causes like the high number of unplanned activities and the operational dynamics present in this domain. Most activities are knowledge-intensive and require collaboration of various actors. Additionally, the produced software is intangible and therefore difficult to measure. Thus, software producers are often insufficiently aware of the state of their source code, while suitable software quality measures are often applied too late in the project lifecycle, if at all. Software development processes are used by the majority of software companies to ensure the quality and reproducibility of their development endeavors. Typically, these processes are abstractly defined utilizing process models. However, they still need to be interpreted by individuals and be manually executed, resulting in governance and compliance issues. The environment is sufficiently dynamic that unforeseen situations can occur due to various events, leading to potential aberrations and process governance issues. Furthermore, as process models are implemented manually without automation support, they impose additional work for the executing humans. Their advantages often remain hidden as aligning the planned process with reality is cumbersome. In response to these problems, this thesis contributes the Context-aware Process Management (CPM) framework. The latter enables holistic and automated support for software engineering projects and their processes. In particular, it provides concepts for extending process management technology to support software engineering process models in their entirety. Furthermore, CPM contributes an approach to integrate the enactment of the process models better with the real-world process by introducing a set of contextual extensions. Various events occurring in the course of the projects can be utilized to improve process support and activities outside the realm of the process models can be covered. That way, the continuously growing divide between the plan and reality that often occurs in software engineering projects can be avoided. Finally, the CPM framework comprises facilities to better connect the software engineering process with other important aspects and areas of software engineering projects. This includes automated process-oriented support for software quality management or software engineering knowledge management. The CPM framework has been validated by a prototypical implementation, various sophisticated scenarios, and its practical application at two software companies

Adaptive Process Management in Cyber-Physical Domains

The increasing application of process-oriented approaches in new challenging cyber-physical domains beyond business computing (e.g., personalized healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex processes in such domains. A cyber-physical domain is characterized by the presence of a cyber-physical system coordinating heterogeneous ICT components (PCs, smartphones, sensors, actuators) and involving real world entities (humans, machines, agents, robots, etc.) that perform complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and processes enacted in cyber-physical domains must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this chapter, we tackle the above issue and we propose a general approach, a concrete framework and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events. The adaptation mechanism provided by SmartPM is based on declarative task specifications, execution monitoring for detecting failures and context changes at run-time, and automated planning techniques to self-repair the running process, without requiring to predefine any specific adaptation policy or exception handler at design-time

Knowledge-based process management – an approach to handling adaptive workflow

In recent years, many organisations have found enterprise modelling, especially business process modelling, to be an effective tool for managing organisational change. The application of business processing modelling has brought benefits to many organisations, but the models developed tend to be used for reference during business operations and re-engineering activities; they rarely play an active role in supporting the day-to-day execution of the processes. While workflow management systems are widely used for the streamlined management of "administrative" business processes, current systems are unable to cope with the more dynamic situations encountered in ad-hoc and collaborative processes [1]. A system that supports complex and dynamically changing processes is required. There is increasing interest in making workflow systems more adaptive [8][20] and using knowledge-based techniques to provide more flexible process management support than is possible Published in Knowledge-based Systems, Vol 16, 2003, pp149-160 Page 2 using current workflow systems [4][21]. This paper describes the results of a collaborative project between Loughborough University and the University of Edinburgh. ICI and Unilever were industrial partners on the project, providing real business requirements in the application domain. The project investigated the use of ontologies, agents and knowledge based planning techniques to provide support for adaptive workflow or flexible workflow management, especially in the area of new product development within the chemical industries