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A complete software stack for IoT time-series analysis that combines semantics and machine learning-lessons learned from the dyversify project

Dieter De Paepe, Sander Vanden Hautte, Bram Steenwinckel (UGent) , Pieter Moens (UGent) , Jasper Vaneessen (UGent) , Steven Vandekerckhove, Bruno Volckaert (UGent) , Femke Ongenae (UGent) and Sofie Van Hoecke (UGent)
(2021) APPLIED SCIENCES-BASEL. 11(24).
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Abstract
Companies are increasingly gathering and analyzing time-series data, driven by the rising number of IoT devices. Many works in literature describe analysis systems built using either data-driven or semantic (knowledge-driven) techniques. However, little to no works describe hybrid combinations of these two. Dyversify, a collaborative project between industry and academia, investigated how event and anomaly detection can be performed on time-series data in such a hybrid setting. We built a proof-of-concept analysis platform, using a microservice architecture to ensure scalability and fault-tolerance. The platform comprises time-series ingestion, long term storage, data semantification, event detection using data-driven and semantic techniques, dynamic visualization, and user feedback. In this work, we describe the system architecture of this hybrid analysis platform and give an overview of the different components and their interactions. As such, the main contribution of this work is an experience report with challenges faced and lessons learned.
Keywords
ANOMALY DETECTION, PLATFORM, time series, data analytics, machine learning, semantic web, reasoning, microservice architecture

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MLA
De Paepe, Dieter, et al. “A Complete Software Stack for IoT Time-Series Analysis That Combines Semantics and Machine Learning-Lessons Learned from the Dyversify Project.” APPLIED SCIENCES-BASEL, vol. 11, no. 24, 2021, doi:10.3390/app112411932.
APA
De Paepe, D., Vanden Hautte, S., Steenwinckel, B., Moens, P., Vaneessen, J., Vandekerckhove, S., … Van Hoecke, S. (2021). A complete software stack for IoT time-series analysis that combines semantics and machine learning-lessons learned from the dyversify project. APPLIED SCIENCES-BASEL, 11(24). https://doi.org/10.3390/app112411932
Chicago author-date
De Paepe, Dieter, Sander Vanden Hautte, Bram Steenwinckel, Pieter Moens, Jasper Vaneessen, Steven Vandekerckhove, Bruno Volckaert, Femke Ongenae, and Sofie Van Hoecke. 2021. “A Complete Software Stack for IoT Time-Series Analysis That Combines Semantics and Machine Learning-Lessons Learned from the Dyversify Project.” APPLIED SCIENCES-BASEL 11 (24). https://doi.org/10.3390/app112411932.
Chicago author-date (all authors)
De Paepe, Dieter, Sander Vanden Hautte, Bram Steenwinckel, Pieter Moens, Jasper Vaneessen, Steven Vandekerckhove, Bruno Volckaert, Femke Ongenae, and Sofie Van Hoecke. 2021. “A Complete Software Stack for IoT Time-Series Analysis That Combines Semantics and Machine Learning-Lessons Learned from the Dyversify Project.” APPLIED SCIENCES-BASEL 11 (24). doi:10.3390/app112411932.
Vancouver
1.
De Paepe D, Vanden Hautte S, Steenwinckel B, Moens P, Vaneessen J, Vandekerckhove S, et al. A complete software stack for IoT time-series analysis that combines semantics and machine learning-lessons learned from the dyversify project. APPLIED SCIENCES-BASEL. 2021;11(24).
IEEE
[1]
D. De Paepe et al., “A complete software stack for IoT time-series analysis that combines semantics and machine learning-lessons learned from the dyversify project,” APPLIED SCIENCES-BASEL, vol. 11, no. 24, 2021.
@article{8733978,
  abstract     = {{Companies are increasingly gathering and analyzing time-series data, driven by the rising number of IoT devices. Many works in literature describe analysis systems built using either data-driven or semantic (knowledge-driven) techniques. However, little to no works describe hybrid combinations of these two. Dyversify, a collaborative project between industry and academia, investigated how event and anomaly detection can be performed on time-series data in such a hybrid setting. We built a proof-of-concept analysis platform, using a microservice architecture to ensure scalability and fault-tolerance. The platform comprises time-series ingestion, long term storage, data semantification, event detection using data-driven and semantic techniques, dynamic visualization, and user feedback. In this work, we describe the system architecture of this hybrid analysis platform and give an overview of the different components and their interactions. As such, the main contribution of this work is an experience report with challenges faced and lessons learned.}},
  articleno    = {{11932}},
  author       = {{De Paepe, Dieter and Vanden Hautte, Sander and Steenwinckel, Bram and Moens, Pieter and Vaneessen, Jasper and Vandekerckhove, Steven and Volckaert, Bruno and Ongenae, Femke and Van Hoecke, Sofie}},
  issn         = {{2076-3417}},
  journal      = {{APPLIED SCIENCES-BASEL}},
  keywords     = {{ANOMALY DETECTION,PLATFORM,time series,data analytics,machine learning,semantic web,reasoning,microservice architecture}},
  language     = {{eng}},
  number       = {{24}},
  pages        = {{22}},
  title        = {{A complete software stack for IoT time-series analysis that combines semantics and machine learning-lessons learned from the dyversify project}},
  url          = {{http://doi.org/10.3390/app112411932}},
  volume       = {{11}},
  year         = {{2021}},
}
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