Hybrid Data Models for Industrial Internet of Things (IIoT) Analytics

  • Authors

    • Dr. Howard Aiken Professor, Harvard University, United States. Author
    • Dr. J. Presper Eckert Computer Engineer, University of Pennsylvania, United States. Author
    • Dr. John W. Mauchly Professor & Physicist, University of Pennsylvania, United States. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2022PII6B2R

    Published 11-09-2022

  • Industrial Internet of Things (IIoT), Hybrid Data Models, Predictive Analytics, NoSQL, Time-Series Data, Data Integration, Edge Computing, Semantic Interoperability, Big Data, Smart Manufacturing

    Issue

    Vol. 5 No. 2 (2022)

    Section

    Articles

    How to Cite

    [1]
    H. Aiken, P. E. J, and J. W. Mauchly, “Hybrid Data Models for Industrial Internet of Things (IIoT) Analytics”, IJDEIC, vol. 5, no. 2, pp. 01–14, Nov. 2022, doi: 10.67228/30715717/IJDEIC-2022PII6B2R.

  • Abstract

    The Industrial Internet of Things (IIoT) has ushered in a new era of connected devices, real-time data collection, and intelligent analytics. However, the complexity and heterogeneity of IIoT environments demand sophisticated data modeling techniques to harness the full potential of analytics. Hybrid data models—integrating structured, semi-structured, and unstructured data representations—are emerging as a critical solution. This paper presents a comprehensive study on the design and application of hybrid data models tailored for IIoT analytics. It explores data heterogeneity challenges, presents layered data model architectures, and discusses the integration of relational, NoSQL, and time-series databases. A proposed hybrid data model architecture is introduced, with a focus on real-time processing, scalability, and semantic interoperability. The model is validated using an industrial case study involving predictive maintenance in a smart factory. Results indicate a significant improvement in data query performance, storage efficiency, and analytical accuracy compared to traditional single-model approaches. Flowcharts and diagrams illustrate data flow, architectural layers, and analytics pipelines. The paper concludes by emphasizing the role of hybrid data models as enablers for scalable, resilient, and intelligent IIoT ecosystems.

  • References

    [1] M. Stonebraker et al., "The End of an Architectural Era (It's Time for a Complete Rewrite)," Proc. VLDB, 2007.

    [2] M. Fowler, "NoSQL Distilled," Addison-Wesley, 2013.

    [3] R. Cattell, "Scalable SQL and NoSQL Data Stores," ACM SIGMOD Record, 2011.

    [4] J. Gubbi et al., "Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions," Future Generation Computer Systems, 2013.

    [5] D. Evans, "The Internet of Things: How the Next Evolution of the Internet Is Changing Everything," Cisco IBSG, 2011.

    [6] L. Da Xu, W. He, and S. Li, "Internet of Things in Industries: A Survey," IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 2233–2243, Nov. 2014.

    [7] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, "Edge Computing: Vision and Challenges," IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, Oct. 2016.

    [8] M. Chiang and T. Zhang, "Fog and IoT: An Overview of Research Opportunities," IEEE Internet of Things Journal, vol. 3, no. 6, pp. 854–864, Dec. 2016.

    [9] H. Boyes, B. Hallaq, J. Cunningham, and T. Watson, "The Industrial Internet of Things (IIoT): An Analysis Framework," Computers in Industry, vol. 101, pp. 1–12, 2018.

    [10] M. H. ur Rehman, I. Yaqoob, K. Salah, M. Imran, P. P. Jayaraman, and C. Perera, "The Role of Big Data Analytics in Industrial Internet of Things," Future Generation Computer Systems, vol. 99, pp. 247–259, 2019.

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