Abstract
Modern Internet of Things solutions using edge devices produce large amounts of raw data. In order to utilize this data, it needs to be processed, aggregated, and categorized to enable decision making for management and end-users. This data management is a non-trivial task, as the computational load is directly proportional to the amount of data. In order to tackle this issue, we provide an extensible and scalable microservice architecture that can receive, normalize, and filter the raw data and persist it in different levels of aggregation, as well as for time series analysis.
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Acknowledgements
This research was funded by the Austrian Research Promotion Agency (FFG) and the implementation of the presented framework is part of a research project with nexopt (https://www.nexopt.com/) in Austria.
The dissemination of the research reported in this paper has been funded by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK), the Federal Ministry for Digital and Economic Affairs (BMDW), and the Province of Upper Austria in the frame of the COMET-Competence Centers for Excellent Technologies Programme and the COMET Module S3AI managed by Austrian Research Promotion Agency FFG.
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Meindl, R., Papesh, K., Baumgartner, D., Helm, E. (2022). A Scalable Microservice Infrastructure for Fleet Data Management. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2022 Workshops. DEXA 2022. Communications in Computer and Information Science, vol 1633. Springer, Cham. https://doi.org/10.1007/978-3-031-14343-4_37
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DOI: https://doi.org/10.1007/978-3-031-14343-4_37
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