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Distributed Business Process Discovery in Cloud Clusters

Distributed Business Process Discovery in Cloud Clusters

Ishak H. A Meddah, Fatiha Guerroudji, Nour Elhouda Remil
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 18
ISSN: 2637-7888|EISSN: 2637-7896|EISBN13: 9781683183471|DOI: 10.4018/IJDAI.301213
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MLA

Meddah, Ishak H. A, et al. "Distributed Business Process Discovery in Cloud Clusters." IJDAI vol.14, no.1 2022: pp.1-18. http://doi.org/10.4018/IJDAI.301213

APA

Meddah, I. H., Guerroudji, F., & Remil, N. E. (2022). Distributed Business Process Discovery in Cloud Clusters. International Journal of Distributed Artificial Intelligence (IJDAI), 14(1), 1-18. http://doi.org/10.4018/IJDAI.301213

Chicago

Meddah, Ishak H. A, Fatiha Guerroudji, and Nour Elhouda Remil. "Distributed Business Process Discovery in Cloud Clusters," International Journal of Distributed Artificial Intelligence (IJDAI) 14, no.1: 1-18. http://doi.org/10.4018/IJDAI.301213

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Abstract

The processing of big data across different axes is becoming more and more difficult and the introduction of the Hadoop MapReduce framework seems to be a solution to this problem. With this framework, large amounts of data can be analyzed and processed. It does this by distributing computing tasks between a group of virtual servers operating in the cloud or a large group of devices. The mining process forms an important bridge between data mining and business process analysis. Its techniques make it possible to extract information from event reports. The extraction process generally consists of two phases: identification or discovery and innovation or education. Our first task is to extract small patterns from the log effects. These templates represent the implementation of the tracking from a business process report file. In this step we use the available technologies. Patterns are represented by finite state automation or regular expressions. And the final model is a combination of just two different styles.

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