Discovering High-Level Process Models from Event Logs

Begicheva, Antonina; Lomazova, Irina A.

doi:10.18255/1818-1015-2017-2-125-140

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…A series of papers [19,20,21] proposed different approaches to improving the structure of discovered models by the additional localization of the environment of events in a log and by composing fragments of regular and frequent behavior with the rare "exceptional" scenarios. Discovery of hierarchical process models, where a high-level event represents a sub-process, was studied in [4]. The identification of low-level and high-level events in an event log is a natural way to improve the structural representation of a process model.…”

Section: Related Workmentioning

confidence: 99%

Discovering Process Models from Event Logs of Multi-Agent Systems Using Event Relations

Sherstyugina,

Nesterov

2023

Proceedings of ISP RAS

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The structure of a process model directly discovered from an event log of a multi-agent system often does not reflect the behavior of individual agents and their interactions. We suggest analyzing the relations between events in an event log to localize actions executed by different agents and involved in their asynchronous interaction. Then, a process model of a multi-agent system is composed from individual agent models between which we add channels to model the asynchronous message exchange. We consider agent interaction within the acyclic and cyclic behavior of different agents. We develop an algorithm that supports the analysis of event relations between different interacting agents and study its correctness. Experimental results demonstrate the overall improvement in the quality of process models discovered by the proposed approach in comparison to monolithic models discovered directly from event logs of multiagent systems.

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Section: Related Workmentioning

confidence: 99%

Discovering Process Models from Event Logs of Multi-Agent Systems Using Event Relations

Sherstyugina,

Nesterov

2023

Proceedings of ISP RAS

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“…Also, Begicheva et al (2017) demonstrate a method that requires a mapping from low-level activities to high-level activities as input. The method abstracts low-level events by directly replacing the low-level events with the corresponding high-level activities.…”

Section: Domain Knowledgementioning

confidence: 99%

An Integrated Approach for Discovering Process Models According to Business Process Types

Lamghari¹

2021

ASMScJ

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Process discovery technique aims at automatically generating a process model that accurately describes a Business Process (BP) based on event data. Related discovery algorithms consider recorded events are only resulting from an operational BP type. While the management community defines three BP types, which are: Management, Support and Operational. They distinguish each BP type by different proprieties like the main business process objective as domain knowledge. This puts forward the lack of process discovery technique in obtaining process models according to business process types (Management and Support). In this paper, we demonstrate that business process types can guide the process discovery technique in generating process models. A special interest is given to the use of process mining to deal with this challenge.

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“…In Begicheva and Lomazov (2017), the authors assume that each coarse-granular activity class is represented by the execution of a sub-process, tracked at the fine-granular level. Hence, such a mapping needs to be initially given, in order to effectively apply the event abstraction.…”

Section: Supervised Techniquesmentioning

confidence: 99%

Event abstraction in process mining: literature review and taxonomy

et al. 2020

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The execution of processes in companies generates traces of event data, stored in the underlying information system(s), capturing the actual execution of the process. Analyzing event data, i.e., the focus of process mining, yields a detailed understanding of the process, e.g., we are able to discover the control flow of the process and detect compliance and performance issues. Most process mining techniques assume that the event data are of the same and/or appropriate level of granularity. However, in practice, the data are extracted from different systems, e.g., systems for customer relationship management, Enterprise Resource Planning, etc., record the events at different granularity levels. Hence, pre-processing techniques that allow us to abstract event data into the right level of granularity are vital for the successful application of process mining. In this paper, we present a literature study, in which we assess the state-of-the-art in the application of such event abstraction techniques in the field of process mining. The survey is accompanied by a taxonomy of the existing approaches, which we exploit to highlight interesting novel directions.

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Discovering High-Level Process Models from Event Logs

Cited by 11 publications

References 16 publications

Discovering Process Models from Event Logs of Multi-Agent Systems Using Event Relations

Discovering Process Models from Event Logs of Multi-Agent Systems Using Event Relations

An Integrated Approach for Discovering Process Models According to Business Process Types

Event abstraction in process mining: literature review and taxonomy

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