Skip to Main content Skip to Navigation
Journal articles

Event Correlation Analytics: Scaling Process Mining Using Mapreduce-Aware Event Correlation Discovery Techniques

Abstract : This paper introduces a scalable process event analysis approach, including parallel algorithms, to support efficient event correlation for big process data. It proposes a two-stages approach for finding potential event relationships, and their verification over big event datasets using MapReduce framework. We report on the experimental results, which show the scalability of the proposed methods, and also on the comparative analysis of the approach with traditional non-parallel approaches in terms of time and cost complexity.
Document type :
Journal articles
Complete list of metadatas

Cited literature [22 references]  Display  Hide  Download

https://hal.uca.fr/hal-02024283
Contributor : Farouk Toumani <>
Submitted on : Tuesday, February 19, 2019 - 8:39:52 AM
Last modification on : Wednesday, March 4, 2020 - 12:28:03 PM
Long-term archiving on: : Monday, May 20, 2019 - 1:05:09 PM

File

tsc-toumani-2476463-proof.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Hicham Reguieg, Boualem Benatallah, Hamid Nezhad, Farouk Toumani. Event Correlation Analytics: Scaling Process Mining Using Mapreduce-Aware Event Correlation Discovery Techniques. IEEE Transactions on Services Computing, IEEE, 2015, 8 (6), pp.847-860. ⟨10.1109/tsc.2015.2476463⟩. ⟨hal-02024283⟩

Share

Metrics

Record views

110

Files downloads

262