A probabilistic-based approach to process model discovery

Malu Castellanos, Fabio Casati, Umeshwar Dayal

Результат исследования: Материалы для книги/типы отчетовМатериалы для конференции

2 Цитирования (Scopus)

Аннотация

Process discovery is crucial for understanding how business operations are performed and how to improve them. The opportunity to discover process models exists given that many systems underlying the execution of process steps log their execution times. However, there are many challenges to discover the actual processes particularly complex ones and without making unrealistic assumptions. In this paper we present a novel probabilistic-based approach to discover high quality process models of any complexity. The approach has a series of steps to discover links between nodes corresponding to execution dependencies between tasks and at the end it ranks these links according to their probabilities of actually existing and classifies them according to their type. In this paper we formulate the process discovery problem, describe the challenges and describe our solution.

Язык оригиналаАнглийский
Заголовок главной публикацииICDE Workshops 2011 - 2011 IEEE 27th International Conference on Data Engineering Workshops
Страницы232-237
Количество страниц6
DOI
Статус публикацииОпубликовано - 2011
Опубликовано для внешнего пользованияДа
Событие2011 IEEE 27th International Conference on Data Engineering Workshops, ICDE 2011 - Hannover, Германия
Длительность: 11 апр 201116 апр 2011

Конференция

Конференция2011 IEEE 27th International Conference on Data Engineering Workshops, ICDE 2011
СтранаГермания
ГородHannover
Период11.4.1116.4.11

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ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Цитировать

Castellanos, M., Casati, F., & Dayal, U. (2011). A probabilistic-based approach to process model discovery. В ICDE Workshops 2011 - 2011 IEEE 27th International Conference on Data Engineering Workshops (стр. 232-237). [5767637] https://doi.org/10.1109/ICDEW.2011.5767637