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 contribution

10 Citations (Scopus)


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 - BPM Forum 2018, Proceedings
EditorsMarco Montali, Mathias Weske, Jan vom Brocke, Ingo Weber
PublisherSpringer Verlag
Number of pages19
ISBN (Print)9783319986500
Publication statusPublished - 2018
Externally publishedYes
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
ISSN (Print)1865-1348


Conference16th International Conference on Business Process Management Forum, BPM Forum 2018


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

ASJC Scopus subject areas

  • Management Information Systems
  • Control and Systems Engineering
  • Business and International Management
  • Information Systems
  • Modelling and Simulation
  • Information Systems and Management

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