Enterprise Integration Patterns (EIP) are a collection of widely used stencils for integrating enterprise applications and business processes. These patterns represent a "de-facto" standard reference for design decisions when integrating enterprise applications. For each of these patterns we present the integration semantics (model) and the conceptual translation (syntax) to the Business Process Model and Notation (BPMN), which is a "de-facto" standard for modelling business process semantics and their runtime behavior.
Workflow management promises a new solution to an age-old problem: controlling, monitoring, optimizing and supporting business processes. What is new about workflow management is the explicit representation of the business process logic which allows for computerized support. This paper discusses the use of Petri nets in the context of workflow management. Petri nets are an established tool for modeling and analyzing processes. On the one hand, Petri nets can be used as a design language for the specification of complex workflows. On the other hand, Petri net theory provides for powerful analysis techniques which can be used to verify the correctness of workflow procedures. This paper introduces workflow management as an application domain for Petri nets, presents state-of-the-art results with respect to the verification of workflows, and highlights some Petri-net-based workflow tools. Process and data are equally important for business process management. Process data is especially relevant in the context of automated business processes, process controlling, and representation of organizations’ core assets. One can discover many process modeling languages, each having a specific set of data modeling capabilities and the level of data awareness. The level of data awareness and data modeling capabilities vary significantly from one language to another. This paper evaluates several process modeling languages with respect to the role of data. To find a common ground for comparison, we develop a framework, which systematically organizes processand data-related aspects of the modeling languages elaborating on the data aspects. Once the framework is in place, we compare twelve process modeling languages against it. We generalize the results of the comparison and identify clusters of similar languages with respect to data awareness. Process model abstraction is an effective approach to reduce the complexity and increase the understandability of process models. Several techniques provide process model abstraction capabilities, but none of them includes data in the abstraction procedure. To overcome this gap, we propose data abstraction capabilities for process model abstraction. The approach is based on use cases found in literature as well as encountered in practice. Altogether, we introduce a framework for data abstraction in process models and provide algorithmic guidance to apply it in practice. The approach is evaluated by an implementation and a scenario of a workshop organization, that contains process models on different levels of abstraction. Unternehmen erreichen ihre Geschaftsziele zunehmend durch das systematische Management ihrer Geschaftsprozesse. Um komplexe Geschaftsziele zu realisieren, lassen sich diese Prozesse meist verknupfen und so Prozessstrukturen aufbauen. Ein sehr komplexes Geschaftsziel ist beispielsweise die Entwicklung der Fahrzeugelektronik im Automobilbau. Hierbei mussen insbesondere die zahlreichen Abhangigkeiten zwischen elektronischen Systemen erfasst und in entsprechende Abhangigkeiten zwischen Entwicklungsprozessen umgesetzt werden. Das Ergebnis ist eine datengetriebene Prozessstruktur, die eine starke Beziehung zwischen der Struktur des Produkts und den auszufuhrenden Prozessen beschreibt. Sie enthalt hunderte bis tausende Prozesse mit entsprechenden Abhangigkeiten. Die Erstellung und Koordination einer datengetriebenen Prozessstruktur ist sehr aufwandig und kann manuell kaum bewerkstelligt werden. Die vorliegende Arbeit stellt mit COREPRO (Configuration Based Release Processes) eine durchgangige IT-Losung fur die Unterstutzung datengetriebener Prozessstrukturen vor. COREPRO erlaubt ihre formale Beschreibung und Ausfuhrung basierend auf einem intuitiven Basismodell. Wir fuhren eine Modellierungsunterstutzung ein, die die Modellierungsaufwande fur datengetriebene Prozessstrukturen signifikant reduziert. Ferner erlaubt COREPRO die Adaption datengetriebener Prozessstrukturen auf einer hohen Abstraktionsebene, indem Anderungen einer Produktstruktur direkt auf Adaptionen der zugehorigen Prozessstruktur transformiert werden. Geeignete Konsistenzanalysen stellen sicher, dass bei der Adaption zur Laufzeit mogliche Ausnahmesituationen erkannt werden. Diese lassen sich in COREPRO durch verschiedene innovative Mechanismen behandeln. Sie erlauben dem Nutzer nicht nur flexible Eingriffe in den Ablauf einer Prozessstruktur, sondern zeigen ihm auch die Konsequenzen derartiger Eingriffe an. Die korrekte, verklemmungsfreie Ausfuhrung der Prozessstruktur wird hierbei durchgehend garantiert. In the engineering domain, the development of complex products (e.g., cars) necessitates the coordination of thousands of (sub-) processes. One of the biggest challenges for process management systems is to support the modeling, monitoring and maintenance of the many interdependencies between these sub-processes. The resulting process structures are large and can be characterized by a strong relationship with the assembly of the product; i.e., the sub-processes to be coordinated can be related to the different product components. So far, subprocess coordination has been mainly accomplished manually, resulting in high efforts and inconsistencies. IT support is required to utilize the information about the product and its structure for deriving, coordinating and maintaining such data-driven process structures. In this paper, we introduce the COREPRO framework for the data-driven modeling of large process structures. The approach reduces modeling efforts significantly and provides mechanisms for maintaining data-driven process structures. The coordination of complex process structures is a fundamental task for enterprises, such as in the automotive industry. Usually, such process structures consist of several (sub-)processes whose execution must be coordinated and synchronized. Effecting this manually is both ineffective and error-prone. However, we can benefit from the fact that these processes are correlated with product structures in many application domains, such as product engineering. Specifically, we can utilize the assembly of a complex real object, such as a car consisting of different mechanical, electrical or electronic subcomponents. Each sub-component has related design or testing processes, which have to be executed within an overall process structure according to the product structure. Our goal is to enable product-driven (i.e., data-driven) process modeling, execution and adaptation. We show the necessity of considering the product life cycle and the role of processes, which are triggering state transitions within the product life cycle. This paper discusses important issues related to the design, enactment and change of data-driven process structures. Our considerations are based on several case studies we conducted for engineering processes in the automotive industry. Enacting business processes in process engines requires the coverage of control flow, resource assignments, and process data. While the first two aspects are well supported in current process engines, data dependencies need to be added and maintained manually by a process engineer. Thus, this task is error-prone and time-consuming. In this paper, we address the problem of modeling processes with complex data dependencies, e.g., m:n relationships, and their automatic enactment from process models. First, we extend BPMN data objects with few annotations to allow data dependency handling as well as data instance differentiation. Second, we introduce a pattern-based approach to derive SQL queries from process models utilizing the above mentioned extensions. Therewith, we allow automatic enactment of data-aware BPMN process models. We implemented our approach for the Activiti process engine to show applicability.