Process modeling

About: Process modeling is a research topic. Over the lifetime, 11639 publications have been published within this topic receiving 223996 citations. The topic is also known as: process simulation.

Exception handling in the BPEL4WS language

Abstract: Graph oriented models are at the core of most business process management systems. In recent years, "algebraic" business process modeling languages based on different process calculi have been proposed. The semantics of these algebraic process languages are quite different, and seemingly incompatible, with those of graph oriented approaches. In this paper we study how the BPEL4WS exception handling mechanism is used to integrate the algebraic and graph process models. Unlike other approaches to exception handling in business processes, the BPEL4WS model does not require that the process topology be constrained by the exception handling hierarchy, thus allowing both highly structured and graph based processes to benefit from it. Based on this exception handling model, we explain "dead path elimination" (the runtime mechanism by which process termination is ensured) as a form of exception processing. The integration of dead path elimination with the exception handling mechanism provides the semantic base for the integration of the graph and algebraic processes models in BPEL4WS.

Modified independent component analysis and Bayesian network-based two-stage fault diagnosis of process operations

Abstract: Statistical fault detection techniques are able to detect fault and diagnose root-cause(s) from the monitored process variables. For complex process operations, it is not feasible to screen all the process variables due to monitoring cost and flooding of alarms. Thus, if a fault is originated from a process variable that is not monitored, conventional statistical techniques are incapable of locating the true root-cause. To relax this limitation, a two-stage fault diagnosis technique is proposed for process operations. In the first-stage, the modified independent component analysis is used for fault detection and to identify the faulty monitored variable. In the second-stage, a Bayesian Network model is constructed considering the process variables and their dependence obtained from the process flow diagram. Evidence is then generated at the network node corresponding to the faulty variable identified in the first-stage. Subsequently, the network is updated and analyzed using deductive and abductive reasoning to identify the true root-cause. To verify the applicability of the proposed technique it is tested on two process models. The results of both case studies have demonstrated the effectiveness of the proposed technique to diagnose the true root-cause that originated from process variables that are not monitored. Once integrated with process loss functions, the proposed technique will serve as an important element of dynamic operational risk management framework.

A coordination theory approach to process description and redesign

Abstract: Managers must understand, influence, and redesign organizational processes to improve business performance. In this paper we present a technique for documenting a business process. The technique has six steps: defining process boundaries, collecting data, determining actors and resources, determining activities, determining dependencies and model verification. While similar to other processmapping techniques, our approach is novel in incorporating ideas from coordination theory, thus the attention to dependencies. As a result, the technique is useful both for documenting a process and suggesting ways in which the process could be redesigned. We present an extended illustration with the hope that the technique can be used by readers of this article.

Integrating Risks in Business Process Models with Value focused Process Engineering

Abstract: As enterprise systems develop, the integration of various business management dimensions becomes increasingly important However, historical disciplinary boundaries between information systems and management sciences can obstruct this path to integration For example, risk management is generally considered as a business process within process engineering, while in the context of management sciences risk is treated as a threat to business objectives that needs to be minimized Both views are essential for a complete approach to identifying, understanding and managing risks in order to optimally meet business requirements In this paper, we address the need for a holistic business view of risk management in the enterprise systems space by drawing on the strengths of the respective disciplines and identifying links between their complementary views of risk, which enables us to integrate these apparently diverging views Through the application of value-focused process engineering principles to risk management models, we develop a framework that extends the capabilities of existing enterprise systems and enables risk-oriented process management which incorporates a multi-disciplinary view of risk The proposed framework is illustrated in the context of a critical administrative process in a university to demonstrate the practical application of and the resulting benefits from the use of this framework

On the Usage of Labels and Icons in Business Process Modeling

Abstract: The value of business process models is dependent on the choice of graphical elements in the model and their annotation with additional textual and graphical information. This research discusses the use of text and icons for labeling the graphical constructs in a process model. The authors use two established verb classification schemes to examine the choice of activity labels in process modeling practice. Based on the author's findings, this paper synthesizes a set of twenty-five activity label categories. Proposed is a systematic approach for graphically representing these label categories through the use of graphical icons, such that the resulting process models are easier and more readily understandable by end users. The author's findings contribute to an ongoing stream of research investigating the practice of process modeling and thereby contribute to the body of knowledge about conceptual modeling quality overall.