No abstract

Process discovery is one of the main branches of process mining that allows the user to build a process model representing the process behavior as recorded in the logs. Standard process discovery techniques produce as output a procedural process model (e.g., a Petri net). Recently, several approaches have been developed to derive declarative process models from logs and have been proven to be more suitable to analyze processes working in environments that are less stable and predictable. However, a large part of these techniques are focused on the analysis of the control flow perspective of a business process. Therefore, one of the challenges still open in this field is the development of techniques for the analysis of business processes also from other perspectives, like data, time, and resources. In this paper, we present a full-fledged approach for the discovery of multi-perspective declarative process models from event logs that allows the user to discover declarative models taking into consideration all the information an event log can provide. The approach has been implemented and experimented in real-life case studies.

No abstract

No abstract

No abstract

The spectrum of an organization's business processes ranges from routine processes with a well-defined flow to agile processes with a degree of uncertainty. The Process Navigation platform aims at supporting both types of processes as well as combinations of them. It offers execution support for traditional flow-oriented notations like BPMN as they are well-suited for the routine type of processes. Rule-based notations for agile processes like CMMN are on the way of getting established but still have a number of weaknesses. As a consequence, the platform's agile part does not target one single notation but relies on a rule-based crossperspective and modal intermediate language. CMMN models are then translated to the intermediate language for execution. The contribution of this paper is built up in three parts: first of all, the overall architecture of the execution platform is explained. In a second step, the intermediate language is evaluated on the basis of a comprehensive and acknowledged framework of business process requirements. And finally, the translation of CMMN to the intermediate language is described by means of an example.

Process mining aims at discovering processes by extracting knowledge from event logs. Such knowledge may refer to different business process perspectives. The organisational perspective deals, among other things, with the assignment of human resources to process activities. Information about the resources that are involved in process activities can be mined from event logs in order to discover resource assignment conditions, which is valuable for process analysis and redesign. Prior process mining approaches in this context present one of the following issues: (i) they are limited to discovering a restricted set of resource assignment conditions; (ii) they do not aim at providing efficient solutions; or (iii) the discovered process models are difficult to read due to the number of assignment conditions included. In this paper we address these problems and develop an efficient and effective process mining framework that provides extensive support for the discovery of patterns related to resource assignment. The framework is validated in terms of performance and applicability.

Imperative languages like BPMN are eminently suitable for representing routine processes and are likewise cumbersome in case of flexible processes. The latter are easier to describe using declarative process modeling languages (DPMLs). However, understandability and tool support of DPMLs are comparatively poor. Additionally, there may be an affinity to a particular language caused by company guidelines or individual preferences. Hence, a technique for automatically translating process models between different languages is required. Process models usually describe several aspects of a process, such as activity orderings and role assignments. Therefore, our approach focuses on translating resourceaware process models. We utilize well-established techniques for process simulation and mining to avoid the definition of cumbersome model transformation rules. Since there is currently no simulation approach for resource-aware declarative process models we additionally describe a solution based on a well-known and expressive logic. The whole translation approach is discussed and evaluated at the example of BPMN and DPIL.