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Data in business processes
(2011)
Prozesse und Daten sind gleichermaßen wichtig für das Geschäftsprozessmanagement. Prozessdaten sind dabei insbesondere im Kontext der Automatisierung von Geschäftsprozessen, dem Prozesscontrolling und der Repräsentation der Vermögensgegenstände von Organisationen relevant. Es existieren viele Prozessmodellierungssprachen, von denen jede die Darstellung von Daten durch eine fest spezifizierte Menge an Modellierungskonstrukten ermöglicht. Allerdings unterscheiden sich diese Darstellungenund damit der Grad der Datenmodellierung stark untereinander. Dieser Report evaluiert verschiedene Prozessmodellierungssprachen bezüglich der Unterstützung von Datenmodellierung. Als einheitliche Grundlage entwickeln wir ein Framework, welches prozess- und datenrelevante Aspekte systematisch organisiert. Die Kriterien legen dabei das Hauptaugenmerk auf die datenrelevanten Aspekte. Nach Einführung des Frameworks vergleichen wir zwölf Prozessmodellierungssprachen gegen dieses. Wir generalisieren die Erkenntnisse aus den Vergleichen und identifizieren Cluster bezüglich des Grades der Datenmodellierung, in welche die einzelnen Sprachen eingeordnet werden.
Business process models are abstractions of concrete operational procedures that occur in the daily business of organizations. To cope with the complexity of these models, business process model abstraction has been introduced recently. Its goal is to derive from a detailed process model several abstract models that provide a high-level understanding of the process. While techniques for constructing abstract models are reported in the literature, little is known about the relationships between process instances and abstract models. In this paper we show how the state of an abstract activity can be calculated from the states of related, detailed process activities as they happen. The approach uses activity state propagation. With state uniqueness and state transition correctness we introduce formal properties that improve the understanding of state propagation. Algorithms to check these properties are devised. Finally, we use behavioral profiles to identify and classify behavioral inconsistencies in abstract process models that might occur, once activity state propagation is used.
Business process models are used within a range of organizational initiatives, where every stakeholder has a unique perspective on a process and demands the respective model. As a consequence, multiple process models capturing the very same business process coexist. Keeping such models in sync is a challenge within an ever changing business environment: once a process is changed, all its models have to be updated. Due to a large number of models and their complex relations, model maintenance becomes error-prone and expensive. Against this background, business process model abstraction emerged as an operation reducing the number of stored process models and facilitating model management. Business process model abstraction is an operation preserving essential process properties and leaving out insignificant details in order to retain information relevant for a particular purpose. Process model abstraction has been addressed by several researchers. The focus of their studies has been on particular use cases and model transformations supporting these use cases. This thesis systematically approaches the problem of business process model abstraction shaping the outcome into a framework. We investigate the current industry demand in abstraction summarizing it in a catalog of business process model abstraction use cases. The thesis focuses on one prominent use case where the user demands a model with coarse-grained activities and overall process ordering constraints. We develop model transformations that support this use case starting with the transformations based on process model structure analysis. Further, abstraction methods considering the semantics of process model elements are investigated. First, we suggest how semantically related activities can be discovered in process models-a barely researched challenge. The thesis validates the designed abstraction methods against sets of industrial process models and discusses the method implementation aspects. Second, we develop a novel model transformation, which combined with the related activity discovery allows flexible non-hierarchical abstraction. In this way this thesis advocates novel model transformations that facilitate business process model management and provides the foundations for innovative tool support.