TY - GEN
T1 - A Scalable Microservice Infrastructure for Fleet Data Management
AU - Meindl, Rainer
AU - Papesh, Konstantin
AU - Baumgartner, David
AU - Helm, Emmanuel
N1 - Funding Information:
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.
Funding Information:
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.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Big data
KW - Data management
KW - Data processing
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=85136997587&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-14343-4_37
DO - 10.1007/978-3-031-14343-4_37
M3 - Conference contribution
AN - SCOPUS:85136997587
SN - 9783031143427
T3 - Communications in Computer and Information Science
SP - 392
EP - 401
BT - Database and Expert Systems Applications - DEXA 2022 Workshops - 33rd International Conference, DEXA 2022, Proceedings
A2 - Kotsis, Gabriele
A2 - Khalil, Ismail
A2 - Mashkoor, Atif
A2 - Sametinger, Johannes
A2 - Tjoa, A Min
A2 - Moser, Bernhard
A2 - Martinez-Gil, Jorge
A2 - Sobieczky, Florian
A2 - Fischer, Lukas
A2 - Ramler, Rudolf
A2 - Czech, Gerald
A2 - Taudes, Alfred
A2 - Khan, Maqbool
PB - Springer
T2 - 6th International Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2022, 4th International Workshop on Machine Learning and Knowledge Graphs, MLKgraphs 2022, 2nd International Workshop on Time Ordered Data, ProTime2022, 2nd International Workshop on AI System Engineering: Math, Modelling and Software, AISys2022, 1st International Workshop on Distributed Ledgers and Related Technologies, DLRT2022 and 1st International Workshop on Applied Research, Technology Transfer and Knowledge Exchange in Software and Data Science, ARTE2022 held at 33rd International Conference on Database and Expert Systems Applications, DEXA 2022
Y2 - 22 August 2022 through 24 August 2022
ER -