Michaela Baumann

Resource-Aware Process Model Similarity Matching

As business process models are widely used and essential for most organizations, the problem of redundantly modeled processes rises. This can happen when a process is modeled by different modelers or when organizations merge. In order to cope with this issue, typically process model similarity matching methods are used. Thereby, pure textual matching algorithms operating on single activities are often not suitable. One alternative is to include further information like data and resources and to check for M:N-matchings. The work at hand describes how to use resource information to match process models, even if they are modeled on different levels of granularity. The approach can be used for both human and non-human resources. Furthermore, the differences between intra- and inter-organizational matchings are pointed out.

Towards Multi-perspective Process Model Similarity Matching

Organizations increasingly determine process models to support documentation and redesign of workflows. In various situations correspondences between activities of different process models have to be found. The challenge is to find a similarity measure to identify similar activities in different process models. Current matching techniques predominantly consider lexical matching based on a comparison of activity labels and 1-to-1-matchings. However, label based matching probably fails, e.g., when modellers use different vocabulary or model activities at different levels of granularity. That is why we extend existing methods to compute candidate sets for N-to-M-matchings based on power-sets of nodes. Therefore, we impose higher demands on process models as we do not only consider labels, but also involved actors, data objects and the order of appearing. This information is used to identify similarities in process models that use different vocabulary and are modelled at different levels of granularity.

Enhancing Feasibility of Human-driven Processes by Transforming Process Models to Process Checklists

In traditional approaches business processes are executed on top of IT-based Workflow-Management Systems (WfMS). The key benefits of the application of a WfMS are task coordination, step-by-step guidance through process execution and traceability supporting compliance issues. However, when dealing with human-driven workflows, conventional WfMS turn out to be too restrictive. Especially, the only way to handle exceptions is to bypass the system. If users are forced to bypass WfMS frequently, the system is more a liability than an asset. In order to diminish the dependency from IT-based process management systems, we propose an alternative way of supporting workflow execution that is especially suitable for human-driven processes. We introduce the so-called process checklist representation of process models where processes are described as a paper-based step-by-step instruction handbook.

The Process Checklist

When enterprises are determined to introduce process management, they usually aim at IT system supported execution of processes by Workflow Management Systems (WfMSs) or Process-aware Information Systems. In constrast to this common tendency of process technology, we introduce a paper-based scheme to enact and execute human-driven processes in the work at hand. Our approach is motivated by insights into problems of firms that tried to establish process technology and failed with conventional methods. One of the design objectives for our scheme was to provide a straightforward, quickly viable alternative to WfMS-based process execution at a reasonable effort. The paper-based scheme we introduce follows classical checklist concepts and builds upon the checklist idea in order to reach the same objectives as WfMSs: task coordination, execution guidance, traceability. In this article, we describe how to transform Business Process Model and Notation (BPMN) process models into Process Checklists. We also present extensive evaluations of this approach both in the academic and in the business domain.