Learning process models in IoT Edge

Long Cheng, Cong Liu, Qingzhi Liu, Yucong Duan, John Murphy

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

Process models as knowledge graph representation have been widely used in various domains to create products and deliver services. Although different process model discovery approaches have been proposed in recent years, few of them are designed for distributed computing environments. Specifically, none of them has been studied in the emerging edge computing application scenarios. In this paper, based on the requirements of some real-time process services, we propose a system design for learning process models in IoT edge. We present the details of our solution and our preliminary results on a simulated IoT network show that our method can discover real-time process models in less than a second.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE World Congress on Services, SERVICES 2019
EditorsCarl K. Chang, Peter Chen, Michael Goul, Katsunori Oyama, Stephan Reiff-Marganiec, Yanchun Sun, Shangguang Wang, Zhongjie Wang
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages147-150
Number of pages4
ISBN (Electronic)978-1-7281-3851-0
DOIs
Publication statusPublished - 1 Jul 2019
Event2019 IEEE World Congress on Services, SERVICES 2019 - Milan, Italy
Duration: 8 Jul 201913 Jul 2019

Conference

Conference2019 IEEE World Congress on Services, SERVICES 2019
CountryItaly
CityMilan
Period8/07/1913/07/19

Keywords

  • Edge computing
  • IoT
  • Model discovery
  • Process mining
  • Service computing

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