Refactoring Process Models in Large Process Repositories.

With the increasing adoption of process-aware information systems (PAIS), large process model repositories have emerged. Over time respective models have to be re-aligned to the real-world business processes through customization or adaptation. This bears the risk that model redundancies are introduced and complexity is increased. If no continuous investment is made in keeping models simple, changes are becoming increasingly costly and error-prone. Though refactoring techniques are widely used in software engineering to address related problems, this does not yet constitute state-of-the art in business process management. Process designers either have to refactor process models by hand or cannot apply respective techniques at all. This paper proposes a set of behaviour-preserving techniques for refactoring large process repositories. This enables process designers to eectively deal with model complexity by making process models better understandable and easier to maintain

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

Comprehensive Life Cycle Support for Access Rules in Information Systems: The CEOSIS Project

The definition and management of access rules (e.g., to control access to business documents and business functions) is a fundamental task in any enterprise information system (EIS). While there exists considerable work on how to specify and represent access rules, only little research has been spent on access rule changes. Examples include the evolution of organizational models with need for subsequent adaptation of related access rules as well as direct access rule modifications (e.g., to state a previously defined rule more precisely). This paper presents a comprehensive change framework for the controlled evolution of role-based access rules in EIS. First, we consider changes of organizational models and elaborate how they affect existing access rules. Second, we define change operations which enable direct adaptations of access rules. In the latter context, we define the formal semantics of access rule changes based on operator trees. Particularly, this enables their unambiguous application; i.e., we can precisely determine which effects are caused by respective rule changes. This is important, for example, to be able to efficiently and correctly adapt user worklists in process-aware information systems. Altogether this paper contributes to comprehensive life cycle support for access rules in (adaptive) EIS

How Advanced Change Patterns Impact the Process of Process Modeling

Process model quality has been an area of considerable research efforts. In this context, correctness-by-construction as enabled by change patterns provides promising perspectives. While the process of process modeling (PPM) based on change primitives has been thoroughly investigated, only little is known about the PPM based on change patterns. In particular, it is unclear what set of change patterns should be provided and how the available change pattern set impacts the PPM. To obtain a better understanding of the latter as well as the (subjective) perceptions of process modelers, the arising challenges, and the pros and cons of different change pattern sets we conduct a controlled experiment. Our results indicate that process modelers face similar challenges irrespective of the used change pattern set (core pattern set versus extended pattern set, which adds two advanced change patterns to the core patterns set). An extended change pattern set, however, is perceived as more difficult to use, yielding a higher mental effort. Moreover, our results indicate that more advanced patterns were only used to a limited extent and frequently applied incorrectly, thus, lowering the potential benefits of an extended pattern set

Semantic Correctness in Adaptive Process Management Systems

Abstract. Adaptivity in Process Management Systems (PMS) is key to their successful applicability in pratice. Approaches have already been de-veloped to ensure the system correctness after arbitrary process changes at the syntactical level. However, still errors may be caused at the se-mantical level. Therefore, the integration of application knowledge will flag a milestone in the development of process management technology. In this paper, we introduce a framework for defining semantic constraints over processes in such a way that they can express real-world applica-tion knowledge. On the other hand, these constraints are still manageable concerning the effort for maintenance and semantic process verification. This can be used, for example, to detect semantic conflicts when ap-plying process changes (e.g., drug incompatibilities). In order to enable the PMS to deal with such semantic conflicts we also introduce a notion of semantic correctness and discuss how to (efficiently) verify semantic correctness in the context of process changes

Optimization and closed loop guidance of drag modulated aeroassistedorbital transfer

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77291/1/AIAA-1983-2093-874.pd

Flexibility in Process-Aware Information Systems

Abstract. Process-aware information systems (PAIS) must be able to deal with uncertainty, exceptional situations, and environmental changes. Needed business agility is often hindered by the lacking flexibility of existing PAIS. Once a process is implemented, its logic cannot be adapted or refined anymore. This often leads to rigid behavior or gaps between real-world processes and implemented ones. In response to this drawback, adaptive PAIS have emerged, which allow to dynamically adapt or evolve the structure of process models under execution. This paper deals with fundamental challenges related to structural process changes, discusses how existing approaches deal with them, and shows how the various problems have been exterminated in ADEPT2 change framework. We also survey existing approaches fostering flexible process support.

Change Patterns in Use: A Critical Evaluation

Process model quality has been an area of considerable research efforts. In this context, the correctness-by-construction principle of change patterns provides promising perspectives. However, using change patterns for model creation imposes a more structured way of modeling. While the process of process modeling (PPM) based on change primitives has been investigated, little is known about this process based on change patterns. To obtain a better understanding of the PPM when using change patterns, the arising challenges, and the subjective perceptions of process designers, we conduct an exploratory study. The results indicate that process designers face little problems as long as control-flow is simple, but have considerable problems with the usage of change patterns when complex, nested models have to be created. Finally, we outline how effective tool support for change patterns should be realized.This research is supported by Austrian Science Fund (FWF): P23699-N23.Weber, B.; Pinggera, J.; Torres Bosch, MV.; Reichert, M. (2013). Change Patterns in Use: A Critical Evaluation. En Enterprise, Business-Process and Information Systems Modeling, BPMDS 2013. Springer Verlag. 261-276. https://doi.org/11007/978-3-642-38484-4_19S26127

Using process mining to learn from process changes in evolutionary systems

Abstract. Traditional information systems struggle with the requirement to provide flexibility and process support while still enforcing some degree of control. Accordingly, adaptive process management systems (PMSs) have emerged that provide some flexibility by enabling dynamic process changes during runtime. Based on the assumption that these process changes are recorded explicitly, we present two techniques for mining change logs in adaptive PMSs; i.e., we do not only analyze the execution logs of the operational processes, but also consider the adaptations made at the process instance level. The change processes discovered through process mining provide an aggregated overview of all changes that happened so far. This, in turn, can serve as basis for integrating the extrinsic drivers of process change (i.e., the stimuli for flexibility) with existing process adaptation approaches (i.e., the intrinsic change mechanisms). Using process mining as an analysis tool we show in this paper how better support can be provided for truly flexible processes by understanding when and why process changes become necessary

Supporting Data Collection in Complex Scenarios with Dynamic Data Collection Processes

Nowadays, companies have to report a large number of data sets (e.g., sustainability data) regarding their products to different legal authorities. However, in today's complex supply chains products are the outcome of the collaboration of many companies. To gather the needed data sets, companies have to employ cross-organizational and long-running data collection processes that imply great variability. To support such scenarios, we have designed a lightweight, automated approach for contextual process configuration. That approach can capture the contextual properties of the respective situations and, based on them, automatically configure a process instance accordingly, even without human involvement. Finally, we implemented our approach and started an industrial evaluation