Irina A. Lomazova

Antonina K. Begicheva, Irina A. Lomazova, Roman A. Nesterov

Process mining is a field of computer science that deals with discovery and analysis of process models based on automatically generated event logs. Currently, many companies use this technology for optimization and improving their processes. However, a discovered process model may be too detailed, sophisticated and difficult for experts to understand. In this paper, we consider the problem of discovering a hierarchical business process model from a low-level event log, i.e., the problem of automatic synthesis of more readable and understandable process models based on information stored in event logs of information systems. Discovery of better structured and more readable process models is intensively studied in the frame of process mining research from different perspectives. In this paper, we present an algorithm for discovering hierarchical process models represented as two-level workflow nets. The algorithm is based on predefined event ilustering so that the cluster defines a sub-process corresponding to a high-level transition at the top level of the net. Unlike existing solutions, our algorithm does not impose restrictions on the process control flow and allows for concurrency and iteration.

Nikita Shaimov, Irina Lomazova, Alexey Mitsyuk

Process mining is the common name for a range of methods and approaches aimed at analysing and improving processes. Specifically, methods that aim to derive process models from event logs fall under the category of process discovery. Within the range of processes, acyclic processes form a distinct category. In such processes, previously performed actions are not repeated, forming chains of unique actions. However, due to differences in the order of actions, existing process discovery methods can provide models containing cycles even if a process is acyclic. This paper presents a new process discovery algorithm that allows to discover acyclic DFG models for acyclic processes. A model is discovered by partitioning an event log into parts that provide acyclic DFG models and merging them while avoiding the formation of cycles. The resulting algorithm was tested both on real-life and artificial event logs. Absence of cycles improves model visual clarity and precision, also allowing to apply cycle-sensitive methods or visualisations to the model.

Irina A. Lomazova, Alexey A. Mitsyuk, Andrey Rivkin

Recently introduced Petri net-based formalisms advocate the importance of proper representation and management of case objects as well as their co-evolution. In this work we build on top of one of such formalisms and introduce the notion of soundness for it. We demonstrate that for nets with non-deterministic synchronization between case objects, the soundness problem is decidable.

Khalil Mecheraoui, Julio C. Carrasquel, Irina A. Lomazova

This paper presents a compositional conformance checking approach between nested Petri nets and event logs of multi-agent systems. By projecting an event log onto model components, one can perform conformance checking between each projected log and the corresponding component. We formally demonstrate the validity of our approach proving that, to check fitness of a nested Petri net is equivalent to check fitness of each of its components. Leveraging the multi-agent system structure of nested Petri nets, this approach may provide specific conformance diagnostics for each system component as well as to avoid to compute artificial boundaries when decomposing a model for conformance checking.