10,119 research outputs found

    On the Common Support of Workflow Type and Instance Changes under Correctness Constraints

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    The capability to rapidly adapt in-progress workflows (WF) is an essential requirement for any workflow system. Adaptations may concern single WF instances or a WF type as a whole. Especially for long-running business processes it is indispensable to propagate WF type changes to in-progress WF instances as well. Very challenging in this context is to correctly adapt a (potentially large) collection of WF instances, which may be in different states and to which various ad-hoc changes may have been previously applied. This paper presents a generic framework for the common support of both WF type and WF instance changes. We establish fundamental correctness principles, position formal theorems, and show how WF instances can be automatically and efficiently migrated to a modified WF schema. The adequate treatment of conflicting WF type and WF instance changes adds to the overall completeness of our approach. By offering more flexibility and adaptability the so promising WF technology will finally deliver

    Change Support in Process-Aware Information Systems - A Pattern-Based Analysis

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    In today's dynamic business world the economic success of an enterprise increasingly depends on its ability to react to changes in its environment in a quick and flexible way. Process-aware information systems (PAIS) offer promising perspectives in this respect and are increasingly employed for operationally supporting business processes. To provide effective business process support, flexible PAIS are needed which do not freeze existing business processes, but allow for loosely specified processes, which can be detailed during run-time. In addition, PAIS should enable authorized users to flexibly deviate from the predefined processes if required (e.g., by allowing them to dynamically add, delete, or move process activities) and to evolve business processes over time. At the same time PAIS must ensure consistency and robustness. The emergence of different process support paradigms and the lack of methods for comparing existing change approaches have made 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 the systematic comparison of existing process management technology with respect to process change support. Based on these change patterns and features, we provide a detailed analysis and evaluation of selected systems from both academia and industry. The identified change patterns and change support features facilitate the comparison of change support frameworks, and consequently will support PAIS engineers in selecting the right technology for realizing flexible PAIS. In addition, this work can be used as a reference for implementing more flexible PAIS

    Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud

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    With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00

    An Approach for Supporting Ad-hoc Modifications in Distributed Workflow Management Systems

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    Supporting enterprise-wide or even cross-organizational business processes is a characteristic challenge for any workflow management system (WfMS). Scalability at the presence of high loads as well as the capability to dynamically modify running workflow (WF) instances (e.g., to cope with exceptional situations) are essential requirements in this context. Should the latter one, in particular, not be met, the WfMS will not have the necessary flexibility to cover the wide range of process-oriented applications deployed in many organizations. Scalability and flexibility have, for the most part, been treated separately in the relevant literature thus far. Even though they are basic needs for a WfMS, the requirements related with them are totally different. To achieve satisfactory scalability, on the one hand, the system needs to be designed such that a workflow instance can be controlled by several WF servers that are as independent from each other as possible. Yet dynamic WF modifications, on the other hand, necessitate a (logical) central control instance which knows the current and global state of a WF instance. For the first time, this paper presents methods which allow ad-hoc modifications (e.g., to insert, delete, or shift steps) to be performed in a distributed WfMS; i.e., in a WfMS with partitioned WF execution graphs and distributed WF control. It is especially noteworthy that the system succeeds in realizing the full functionality as given in the central case while, at the same time, achieving extremely favorable behavior with respect to communication costs

    Adaptive Process Management in Cyber-Physical Domains

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    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

    Change Mining in Adaptive Process Management Systems

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    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
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