Data in Business Process Models. A Preliminary Empirical Study

Traditional activity-centric process modeling languages treat data as simple black boxes acting as input or output for activities. Many alternate and emerging process modeling paradigms, such as case handling and artifact-centric process modeling, give data a more central role. This is achieved by introducing lifecycles and states for data objects, which is beneficial when modeling data-or knowledge-intensive processes. We assume that traditional activity-centric process modeling languages lack the capabilities to adequately capture the complexity of such processes. To verify this assumption we conducted an online interview among BPM experts. The results not only allow us to identify various profiles of persons modeling business processes, but also the problems that exist in contemporary modeling languages w.r.t. The modeling of business data. Overall, this preliminary empirical study confirms the necessity of data-awareness in process modeling notations in general

A Survey on Handling Data in Business Process Models (Discussion Paper)

Traditional activity-centric process modeling languages treat data as simple black boxes acting as input or output for activities. Many alternate and emerging process modeling paradigms, such as case handling and artifact-centric process modeling, give data a more central role. This is achieved by introducing lifecycles and states for data objects, which is beneficial when modeling data- or knowledge-intensive processes. We assume that traditional activity-centric process modeling languages lack the capabilities to adequately capture the complexity of such processes. To verify this assumption, we conducted a survey among Business Process Management experts. The survey results allow us to identify the problems of contemporary modeling languages in regard to the modeling of business data. To this end, survey respondents rated the data modeling capabilities of a variety of business process modeling tools and notations. Overall, the paper confirms the need of data-awareness in process modeling notations in general

Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches

Engineering of knowledge-intensive processes (KiPs) is far from being mastered, since they are genuinely knowledge- and data-centric, and require substantial flexibility, at both design- and run-time. In this work, starting from a scientific literature analysis in the area of KiPs and from three real-world domains and application scenarios, we provide a precise characterization of KiPs. Furthermore, we devise some general requirements related to KiPs management and execution. Such requirements contribute to the definition of an evaluation framework to assess current system support for KiPs. To this end, we present a critical analysis on a number of existing process-oriented approaches by discussing their efficacy against the requirements

Automatic discovery of data-centric and artifact-centric processes

Process discovery is a technique that allows for automatically discovering a process model from recorded executions of a process as it happens in reality. This technique has successfully been applied for classical processes where one process execution is constituted by a single case with a unique case identifier. Data-centric and artifact-centric systems such as ERP systems violate this assumption. Here a process execution is driven by process data having various notions of interrelated identifiers that distinguish the various interrelated data objects of the process. Classical process mining techniques fail in this setting. This paper presents a fully automatic technique for discovering for each notion of data object in the process a separate process model that describes the evolution of this object, also known as artifact life-cycle model. Given a relational database that stores process execution information of a data-centric system, the technique extracts event information, case identifiers and their interrelations, discovers the central process data objects and their associated events, and decomposes the data source into multiple logs, each describing the cases of a separate data object. Then classical process discovery techniques can be applied to obtain a process model for each object. The technique is implemented and has been evaluated on the production ERP system of a large retailer