Search

Automatically discovering a process model from an event log is the prime problem in process mining. This task is so far approached as an unsupervised learning problem through graph synthesis algorithms. Algorithmic design decisions and heuristics allow for efficiently finding models in a reduced search space. However, design decisions and heuristics are derived from assumptions about how a given behavioral description—an event log—translates into a process model and were not learned from actual models which introduce biases in the solutions. In this paper, we explore the problem of supervised learning of a process discovery technique. We introduce a technique for training an ML-based model using graph convolutional neural networks, which translates a given input event log into a sound Petri net. We show that training this model on synthetically generated pairs of input logs and output models allows it to translate previously unseen synthetic and several real-life event logs into sound, arbitrarily structured models of comparable accuracy and simplicity as existing state of the art techniques in imperative mining. We analyze the limitations of the proposed technique and outline alleys for future work.

Communication between organizations is formalized as process choreographies in daily business. While the correct ordering of exchanged messages can be modeled and enacted with current choreography techniques, no approach exists to describe and automate the exchange of data between processes in a choreography using messages. This paper describes an entirely model-driven approach for BPMN introducing a few concepts that suffice to model data retrieval, data transformation, message exchange, and correlation – four aspects of data exchange. For automation, this work utilizes a recent concept to enact data dependencies in internal processes. We present a modeling guideline to derive local process models from a given choreography; their operational semantics allows to correctly enact the entire choreography from the derived models only including the exchange of data. Targeting on successful interactions, we discuss means to ensure correct process choreography modeling. Finally, we implemented our approach by extending the camunda BPM platform with our approach and show its feasibility by realizing all service interaction patterns using only model-based concepts. Keywords: Process modeling; Data modeling; Process choreographies; Data exchange; BPMN; SQL

Workflow graphs represent the main control-flow constructs of industrial process modeling languages such as BPMN, EPC and UML Activity diagrams, whereas free-choice workflow nets is a well understood class of Petri nets that possesses many efficient analysis techniques. In this paper, we provide new results on the translation between workflow graphs and free-choice workflow nets. We distinguish workflow graphs with and without inclusive Or-logic. For workflow graphs without inclusive logic, we show that workflow graphs and free-choice workflow nets are essentially the same thing. More precisely, each workflow graph and each free-choice workflow net can be brought into an equivalent normal form such that the normal forms are, in some sense, isomorphic. This result gives rise to a translation from arbitrary free-choice workflow nets to workflow graphs. For workflow graphs with inclusive logic, we provide various techniques to replace inclusive Or-joins by subgraphs without inclusive logic, thus giving rise to translations from workflow graphs to free-choice nets. Additionally, we characterize the applicability of these replacements. Finally, we also display a simple workflow graph with an inclusive Or-join, which, in some sense, cannot be replaced. This shows a limitation of translating inclusive logic into free-choice nets and illustrates also a difficulty of translating inclusive logic into general Petri nets. Keywords: Workflow graphs; Petri nets; Free choice; Inclusive OR-join

Process models discovered from a process log using process mining tend to be complex and have problems balancing between overfitting and underfitting. An overfitting model allows for too little behavior as it just permits the traces in the log and no other trace. An underfitting model allows for too much behavior as it permits traces that are significantly different from the behavior seen in the log. This paper presents a post-processing approach to simplify discovered process models while controlling the balance between overfitting and underfitting. The discovered process model, expressed in terms of a Petri net, is unfolded into a branching process using the event log. Subsequently, the resulting branching process is folded into a simpler process model capturing the desired behavior. Keywords: Process mining; Model simplification; Petri nets; Branching processes