IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v15y2023i2p77-d1070795.html
   My bibliography  Save this article

An Interactive Method for Detection of Process Activity Executions from IoT Data

Author

Listed:
  • Ronny Seiger

    (Institute of Computer Science, University of St. Gallen, 9000 St. Gallen, Switzerland)

  • Marco Franceschetti

    (Institute of Computer Science, University of St. Gallen, 9000 St. Gallen, Switzerland)

  • Barbara Weber

    (Institute of Computer Science, University of St. Gallen, 9000 St. Gallen, Switzerland)

Abstract

The increasing number of IoT devices equipped with sensors and actuators pervading every domain of everyday life allows for improved automated monitoring and analysis of processes executed in IoT-enabled environments. While sophisticated analysis methods exist to detect specific types of activities from low-level IoT data, a general approach for detecting activity executions that are part of more complex business processes does not exist. Moreover, dedicated information systems to orchestrate or monitor process executions are not available in typical IoT environments. As a consequence, the large corpus of existing process analysis and mining techniques to check and improve process executions cannot be applied. In this work, we develop an interactive method guiding the analysis of low-level IoT data with the goal of detecting higher-level process activity executions. The method is derived following the exploratory data analysis of an IoT data set from a smart factory. We propose analysis steps, sensor-actuator-activity patterns, and the novel concept of activity signatures that are applicable in many IoT domains. The method shows to be valuable for the early stages of IoT data analyses to build a ground truth based on domain knowledge and decisions of the process analyst, which can be used for automated activity detection in later stages.

Suggested Citation

  • Ronny Seiger & Marco Franceschetti & Barbara Weber, 2023. "An Interactive Method for Detection of Process Activity Executions from IoT Data," Future Internet, MDPI, vol. 15(2), pages 1-31, February.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:2:p:77-:d:1070795
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/15/2/77/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/15/2/77/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hui Yang & Soundar Kumara & Satish T.S. Bukkapatnam & Fugee Tsung, 2019. "The internet of things for smart manufacturing: A review," IISE Transactions, Taylor & Francis Journals, vol. 51(11), pages 1190-1216, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matsumoto, Takao & Chen, Yijun & Nakatsuka, Akihiro & Wang, Qunzhi, 2020. "Research on horizontal system model for food factories: A case study of process cheese manufacturer," International Journal of Production Economics, Elsevier, vol. 226(C).
    2. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    3. Masoud Zafarzadeh & Magnus Wiktorsson & Jannicke Baalsrud Hauge, 2021. "A Systematic Review on Technologies for Data-Driven Production Logistics: Their Role from a Holistic and Value Creation Perspective," Logistics, MDPI, vol. 5(2), pages 1-32, April.
    4. Juliana Basulo-Ribeiro & Carina Pimentel & Leonor Teixeira, 2024. "Digital Transformation in Maritime Ports: Defining Smart Gates through Process Improvement in a Portuguese Container Terminal," Future Internet, MDPI, vol. 16(10), pages 1-27, September.
    5. Asadi, Shahla & Nilashi, Mehrbakhsh & Iranmanesh, Mohammad & Hyun, Sunghyup Sean & Rezvani, Azadeh, 2022. "Effect of internet of things on manufacturing performance: A hybrid multi-criteria decision-making and neuro-fuzzy approach," Technovation, Elsevier, vol. 118(C).
    6. Gosavi, Abhijit & Gosavi, Aparna A., 2024. "CONWIP control in the digitized world: The case of the cyber-physical jobshop," International Journal of Production Economics, Elsevier, vol. 270(C).
    7. Miguel Castillo & Roberto Monroy & Rafiq Ahmad, 2024. "Scientometric analysis and systematic review of smart manufacturing technologies applied to the 3D printing polymer material extrusion system," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 3-33, January.
    8. Mingxing Li & Ray Y. Zhong & Ting Qu & George Q. Huang, 2022. "Spatial–temporal out-of-order execution for advanced planning and scheduling in cyber-physical factories," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1355-1372, June.
    9. Peng Zhan & Shaokun Wang & Jun Wang & Leigang Qu & Kun Wang & Yupeng Hu & Xueqing Li, 2021. "Temporal anomaly detection on IIoT-enabled manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1669-1678, August.
    10. Ye, Zhenggeng & Cai, Zhiqiang & Yang, Hui & Si, Shubin & Zhou, Fuli, 2023. "Joint optimization of maintenance and quality inspection for manufacturing networks based on deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    11. Ali Amiri, 2022. "The application grouping problem in Software-as-a-Service (SaaS) networks," Information Technology and Management, Springer, vol. 23(2), pages 125-137, June.
    12. Salem Ahmed Alabdali & Salvatore Flavio Pileggi & Dilek Cetindamar, 2023. "Influential Factors, Enablers, and Barriers to Adopting Smart Technology in Rural Regions: A Literature Review," Sustainability, MDPI, vol. 15(10), pages 1-38, May.
    13. Zhu, Minghao & Liang, Chen & Yeung, Andy C.L. & Zhou, Honggeng, 2024. "The impact of intelligent manufacturing on labor productivity: An empirical analysis of Chinese listed manufacturing companies," International Journal of Production Economics, Elsevier, vol. 267(C).
    14. Omar, Yamila M. & Minoufekr, Meysam & Plapper, Peter, 2019. "Business analytics in manufacturing: Current trends, challenges and pathway to market leadership," Operations Research Perspectives, Elsevier, vol. 6(C).
    15. Ragosebo Kgaugelo Modise & Khumbulani Mpofu & Olukorede Tijani Adenuga, 2021. "Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport Manufacturing," Energies, MDPI, vol. 14(24), pages 1-15, December.
    16. Rinaldi, Marta & Bottani, Eleonora, 2023. "How did COVID-19 affect logistics and supply chain processes? Immediate, short and medium-term evidence from some industrial fields of Italy," International Journal of Production Economics, Elsevier, vol. 262(C).
    17. Olukorede Tijani Adenuga & Khumbulani Mpofu & Ragosebo Kgaugelo Modise, 2022. "Energy–Carbon Emissions Nexus Causal Model towards Low-Carbon Products in Future Transport-Manufacturing Industries," Energies, MDPI, vol. 15(17), pages 1-13, August.
    18. Emily Opoku Aboagye-Dapaah & Michael Karikari Appiah & Joshua Caleb Dagadu, 2024. "Smart Manufacturing and Pro-Environmental Behavior: Moderated Serial Mediation Modelling and Analysis," Sustainability, MDPI, vol. 16(17), pages 1-20, September.
    19. Sun, Wenqiang & Wang, Qiang & Zhou, Yue & Wu, Jianzhong, 2020. "Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives," Applied Energy, Elsevier, vol. 268(C).
    20. Ye, Zhenggeng & Yang, Hui & Cai, Zhiqiang & Si, Shubin & Zhou, Fuli, 2021. "Performance evaluation of serial-parallel manufacturing systems based on the impact of heterogeneous feedstocks on machine degradation," Reliability Engineering and System Safety, Elsevier, vol. 207(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:15:y:2023:i:2:p:77-:d:1070795. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.