CCBR-Driven Business Process Evolution

Process-aware information systems (PAIS) allow coordinating the execution of business processes by providing the right tasks to the right people at the right time. In order to support a broad spectrum of business processes, PAIS must be flexible at run-time. Ad-hoc deviations from the predefined process schema as well as the quick adaptation of the process schema itself due to changes of the underlying business processes must be supported. This paper presents an integrated approach combining the concepts and methods provided by the process management systems ADEPT and CBRFlow. Integrating these two systems enables ad-hoc modifications of single process instances, the memorization of these modifications using conversational case-based reasoning, and their reuse in similar future situations. In addition, potential process type changes can be derived from cases when similar ad-hoc modifications at the process instance level occur frequently

Integrating Case-Based Reasoning with Adaptive Process Management

The need for more flexiblity of process-aware information systems (PAIS) has been discussed for several years and different approaches for adaptive process management have emerged. Only few of them provide support for both changes of individual process instances and the propagation of process type changes to a collection of related process instances. The knowledge about changes has not yet been exploited by any of these systems. To overcome this practical limitation, PAIS must capture the whole process life cycle and all kinds of changes in an integrated way. They must allow users to deviate from the predefined process in exceptional situations, and assist them in retrieving and reusing knowledge about previously performed changes. In this report we present a proof-of concept implementation of a learning adaptive PAIS. The prototype combines the ADEPT2 framework for dynamic process changes with concepts and methods provided by case-based reasoning(CBR) technology

Change Mining in Adaptive Process Management Systems

The wide-spread adoption of process-aware information systems has resulted in a bulk of computerized information about real-world processes. This data can be utilized for process performance analysis as well as for process improvement. In this context process mining offers promising perspectives. So far, existing mining techniques have been applied to operational processes, i.e., knowledge is extracted from execution logs (process discovery), or execution logs are compared with some a-priori process model (conformance checking). However, execution logs only constitute one kind of data gathered during process enactment. In particular, adaptive processes provide additional information about process changes (e.g., ad-hoc changes of single process instances) which can be used to enable organizational learning. In this paper we present an approach for mining change logs in adaptive process management systems. The change process discovered through process mining provides an aggregated overview of all changes that happened so far. This, in turn, can serve as basis for all kinds of process improvement actions, e.g., it may trigger process redesign or better control mechanisms

Improving Exception Handling by Discovering Change Dependencies in Adaptive Process Management Systems

Process-aware information systems should enable the flexible alignment of business processes to new requirements by supporting deviations from the predefined process model at runtime. To facilitate such dynamic process changes we have adopted techniques from casebased reasoning (CBR). In particular, our existing approach allows to capture the semantics of ad-hoc changes, to support their memorization, and to enable their reuse in upcoming exceptional situations. To further improve change reuse this paper presents an approach for discovering dependencies between ad-hoc modifications from change history. Based on this information better user assistance can be provided when dynamic process changes have to be made

Utilizing Successful Work Practice for Business Process Evolution

Business process management (BPM) has emerged as a dominant technology in current enterprise systems and business solutions. However, business processes are always evolving in current dynamic business environments where requirements and goals are constantly changing. Whereas literature reports on the importance of domain experts in process modelling and adaptations, current solutions have not addressed this issue effectively. In this paper, we present a framework that utilizes successful work practice to support business process evolution. The framework on one hand provides the ability to use domain expert knowledge and experience to tailor individual process instances according to case specific requirements; and on the other, provides a means of using this knowledge through learning techniques to guide subsequent process changes

On Managing Process Variants as an Information Resource

Many business solutions provide best practice process templates, both generic as well as for specific industry sectors. However, it is often the variance from template solutions that provide organizations with intellectual capital and competitive differentiation. Although variance must comply with various contractual, regulatory and operational constraints, it is still an important information resource, representing preferred work practices. In this paper, we present a modeling framework that is conducive to constrained variance, by supporting user driven process adaptations. The focus of the paper is on providing a means of utilizing the adaptations effectively for process improvement through effective management of the process variants repository (PVR). In particular, we will provide deliberations towards a facility to provide query functionality for PVR that is specifically targeted for effective search and retrieval of process variants

Service discovery and composition : PreDiCtS approach

The proliferation of Web Services is fostering the need for service-discovery and composition tools to provide more personalisation during the service retrieval process. In this paper, we describe the motivating details behind PreDiCtS, a framework for personalised service-retrieval. In our approach we consider that similar service composition problems can be tackled in a similar manner by reusing and adapting past composition best practices or templates. The proposed retrieval process uses a mixed- initiative technique based on Conversational Case-Based Reasoning (CCBR), that provides i) for a clearer identification of the user’s service requirements and ii) based on these requirements, finds suitable service templates that satisfy the user’s goal. We discuss how retrieval can vary through the use of different CCBR algorithms and how adaptation can be performed over the retrieved templates thus providing the personalisation feature in PreDiCtS.peer-reviewe

Identifying and Evaluating Change Patterns and Change Support Features in Process-Aware Information Systems.

In order to provide effective support, the introduction of process-aware information systems (PAIS) must not freeze existing business processes. Instead PAIS should allow authorized users to flexibly deviate from the predefined processes if required and to evolve business processes in a controlled manner over time. Many software vendors promise flexible system solutions for realizing such adaptive PAIS, but are often unable to cope with fundamental issues elated to process change (e.g., correctness and robustness). The existence of different process support paradigms and the lack of methods for comparing existing change approaches makes it difficult for PAIS engineers to choose the adequate technology. In this paper we suggest a set of changes patterns and change support features to foster systematic comparison of existing process management technology with respect to change support. Based on these change patterns and features, we provide a detailed analysis and evaluation of selected systems from both academia and industry

Flexible Support of Healthcare Processes

Traditionally, healthcare information systems have focused on the support of predictable and repetitive clinical processes. Even though the latter can be often prespecified in formal process models, process flexibility in terms of dynamic adaptability is indispensable to cope with exceptions and unforeseen situations. Flexibility is further required to accommodate the need for evolving healthcare processes and to properly support healthcare process variability. In addition, process-aware information systems are increasingly used to support less structured healthcare processes (i.e., patient treatment processes), which can be characterized as knowledge-intensive. Healthcare processes of this category are neither fully predictable nor repetitive and, therefore, they cannot be fully prespecified at design time. The partial unpredictability of these processes, in turn, demands a certain amount of looseness. This chapter deals with the characteristic flexibility needs of both prespecified and loosely specified healthcare processes. In addition, it presents fundamental flexibility features required to address these flexibility needs as well as to accommodate them in healthcare practice