Translating standard process models to BPEL

Standardisation of languages in the field of business process management has long been an elusive goal. Recently though, consensus has built around one process implementation language, namely BPEL, and two fundamentally similar process modelling notations, namely UML Activity Diagram (UML AD) and BPMN. This paper presents a technique for generating BPEL code from process models expressed in a core subset of BPMN and UML AD. This model-to-code translation is a necessary ingredient to the emergence of model-driven business process development environments based on these standards. The proposed translation has been implemented as an open source tool

Guided Interaction Exploration in Artifact-centric Process Models

Artifact-centric process models aim to describe complex processes as a collection of interacting artifacts. Recent development in process mining allow for the discovery of such models. However, the focus is often on the representation of the individual artifacts rather than their interactions. Based on event data we can automatically discover composite state machines representing artifact-centric processes. Moreover, we provide ways of visualizing and quantifying interactions among different artifacts. For example, we are able to highlight strongly correlated behaviours in different artifacts. The approach has been fully implemented as a ProM plug-in; the CSM Miner provides an interactive artifact-centric process discovery tool focussing on interactions. The approach has been evaluated using real life data sets, including the personal loan and overdraft process of a Dutch financial institution.Comment: 10 pages, 4 figures, to be published in proceedings of the 19th IEEE Conference on Business Informatics, CBI 201

Heuristics Miners for Streaming Event Data

More and more business activities are performed using information systems. These systems produce such huge amounts of event data that existing systems are unable to store and process them. Moreover, few processes are in steady-state and due to changing circumstances processes evolve and systems need to adapt continuously. Since conventional process discovery algorithms have been defined for batch processing, it is difficult to apply them in such evolving environments. Existing algorithms cannot cope with streaming event data and tend to generate unreliable and obsolete results. In this paper, we discuss the peculiarities of dealing with streaming event data in the context of process mining. Subsequently, we present a general framework for defining process mining algorithms in settings where it is impossible to store all events over an extended period or where processes evolve while being analyzed. We show how the Heuristics Miner, one of the most effective process discovery algorithms for practical applications, can be modified using this framework. Different stream-aware versions of the Heuristics Miner are defined and implemented in ProM. Moreover, experimental results on artificial and real logs are reported

Implementing knowledge building: analysis of a face to face discussion by grade four students

Researchers say that teachers can implement an educational innovation without adhering to the principles underpinning its design. Such principles may not adequately take typical classroom conditions into account. The goal of this study was to explore tensions between attempts to implement the principles underpinning knowledge building and the influence of contextual factors that compete for the teacher's attention. To this end, we discuss five excerpts from a discussion of the motion of spinning tops held by a class of Grade-4 students, coming at the end of a five-month implementation of knowledge building. Each excerpt is followed first by the teacher's perspective and then by the researcher's perspective. Our analysis highlights two tensions that constrain agency, arising from the students' need for social development and their need to learn scientific concepts. We offer some suggestions for addressing these tensions.postprintThis work was supported by a grant from the Social Sciences and Humanities Research Council of Canada to the first author (Grant 410-2000-0998)