maisМоделирование и анализ информационных системModeling and Analysis of Information Systems1818-10152313-5417Yaroslavl State University10.18255/1818-1015-2014-4-181-198mais-106Research ArticleОригинальные статьиArticlesСинтез моделей процессов по журналам событий с шумомOn Process Model Synthesis Based on Event Logs with NoiseМицюкАлексей АлександровичMitsyukA. A.

Международная лаборатория процессно-ориентированных информационных систем, аналитик, 125319 Россия, г. Москва, Кочновский проезд, д. 3

аналитик, International laboratory of process-aware information systems, Kochnovskiy Proezd, 3, Moscow, 125319, Russia

amitsyuk@hse.ruШугуровИван СергеевичShugurovI. S.

Международная лаборатория процессно-ориентированных информационных систем, стажер-исследователь, 125319 Россия, г. Москва, Кочновский проезд, д. 3

стажер-исследователь, International laboratory of process-aware information systems, Kochnovskiy Proezd, 3, Moscow, 125319, Russia

shugurov94@gmail.comНациональный Исследовательский Университет Высшая Школа ЭкономикиРоссияNational Research University Higher School of EconomicsRussian Federation201420082014214181198Copyright © Мицюк А.А., Шугуров И.С., 20142014Мицюк А.А., Шугуров И.С.Mitsyuk A.A., Shugurov I.S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.mais-journal.ru/jour/article/view/106

Извлечение процессов (process mining) – новая и активно развивающаяся область исследований, тесно связанная с управлением процессами, формальными моделями процессов и извлечением данных (data mining). Одна из основных задач извлечения процессов – синтез (извлечение) модели процесса на основании анализа журнала событий. Разработан широкий спектр алгоритмов для извлечения, анализа и усовершенствования моделей процессов. Журналы событий реальных систем часто содержат шум различных видов. В данной работе описываются основные причины возникновения шума в журналах событий и изучается влияние шума на эффективность применения основных алгоритмов извлечения процессов. Приводятся экспериментальные результаты применения основных алгоритмов извлечения моделей процессов к искусственным журналам событий с шумами различного типа. Для этого специальным образом сгенерированные журналы событий с шумом обрабатывались с использованием четырех основных методов извлечения процессов. Хотя современные алгоритмы могут справляться с некоторыми типами шума, в большинстве случаев их применение не приводит к получению удовлетворительного результата. Таким образом, существует необходимость в разработке более совершенных подходов для журналов событий с шумом.

Process mining is a new emerging discipline related to process management, formal process models, and data mining. One of the main tasks of process mining is the model synthesis (discovery) based on event logs. A wide range of algorithms for process model discovery, analysis, and enhancement is developed. The real-life event logs often contain noise of different types. In this paper we describe the main causes of noise in the event logs and study the effect of noise on the performance of process discovery algorithms. The experimental results of application of the main process discovery algorithms to artificial event logs with noise are provided. Specially generated event logs with noise of different types were processed using the four basic discovery techniques. Although modern algorithms can cope with some types of noise, in most cases, their use does not lead to obtaining a satisfactory result. Thus, there is a need for more sophisticated algorithms to deal with noise of different types.

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The authors declare that there are no conflicts of interest present.