iProcess

enabling IoT platforms in data-driven knowledge-intensive processes

Amin Beheshti, Francesco Schiliro, Samira Ghodratnama, Farhad Amouzgar, Boualem Benatallah, Jian Yang, Quan Z. Sheng, Fabio Casati, Hamid Reza Motahari-Nezhad

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

6 Citations (Scopus)

Abstract

The Internet of Things (IoT), the network of physical objects augmented with Internet-enabled computing devices to enable those objects sense the real world, has the potential to transform many industries. This includes harnessing real-time intelligence to improve risk-based decision making and supporting adaptive processes from core to edge. For example, modern police investigation processes are often extremely complex, data-driven and knowledge-intensive. In such processes, it is not sufficient to focus on data storage and data analysis; and the knowledge workers (e.g., investigators) will need to collect, understand and relate the big data (scattered across various systems) to process analysis: in order to communicate analysis findings, supporting evidences and to make decisions. In this paper, we present a scalable and extensible IoT-Enabled Process Data Analytics Pipeline (namely iProcess) to enable analysts ingest data from IoT devices, extract knowledge from this data and link them to process (execution) data. We introduce the notion of process Knowledge Lake and present novel techniques to summarize the linked IoT and process data to construct process narratives. This enables us to put the first step towards enabling storytelling with process data
Original languageEnglish
Title of host publicationBusiness Process Management Forum
Subtitle of host publicationBPM Forum 2018, Proceedings
EditorsMathias Weske, Marco Montali, Ingo Weber, Jan vam Brocke
PublisherSpringer, Springer Nature
Pages108-126
Number of pages19
Volume329
ISBN (Electronic)9783319986517
ISBN (Print)9783319986500
DOIs
Publication statusPublished - 12 Aug 2018
Event16th International Conference on Business Process Management Forum, BPM Forum 2018 - Sydney, Australia
Duration: 9 Sep 201814 Sep 2018

Publication series

NameLecture Notes in Business Information Processing
PublisherSpringer
Volume329
ISSN (Print)1865-1348
ISSN (Electronic)1865-1356

Conference

Conference16th International Conference on Business Process Management Forum, BPM Forum 2018
CountryAustralia
CitySydney
Period9/09/1814/09/18

Keywords

  • Process data science
  • Process Data Analytics
  • Data-driven business processes
  • Knowledge-intensive business processes

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  • Cite this

    Beheshti, A., Schiliro, F., Ghodratnama, S., Amouzgar, F., Benatallah, B., Yang, J., ... Motahari-Nezhad, H. R. (2018). iProcess: enabling IoT platforms in data-driven knowledge-intensive processes. In M. Weske, M. Montali, I. Weber, & J. vam Brocke (Eds.), Business Process Management Forum: BPM Forum 2018, Proceedings (Vol. 329, pp. 108-126). (Lecture Notes in Business Information Processing; Vol. 329). Springer, Springer Nature. https://doi.org/10.1007/978-3-319-98651-7_7