DIVIDE : adaptive context-aware query derivation for IoT data streams
- Author
- Mathias De Brouwer (UGent) , Dörthe Arndt (UGent) , Pieter Bonte (UGent) , Filip De Turck (UGent) and Femke Ongenae (UGent)
- Organization
- Abstract
- In the Internet of Things, it is a challenging task to inte-grate & analyze high velocity sensor data with domain knowledge &context information in real-time. Semantic IoT platforms typically con-sist of stream processing components that use Semantic Web technologiesto run a set of fixed queries processing the IoT data streams. Configur-ing these queries is still a manual task. To deal with changes in contextinformation, which happen regularly in IoT domains, queries typicallyrequire reasoning on all sensor data in real-time to derive relevant sen-sors & events. This can be an issue in real-time, as expressive reasoningis required to deal with the complexity of many IoT domains. To solvethese issues, this paper presents DIVIDE. DIVIDE automatically derivesqueries for stream processing components in an adaptive, context-awareway. When the context changes, it derives through reasoning which sen-sors & observations to filter, given the context & a use case goal, withoutrequiring any more reasoning in real-time. This paper presents the detailsof DIVIDE, and performs evaluations on a healthcare example showinghow it can reduce real-time processing times, scale better when there aremore sensors & observations, and can run efficiently on low-end devices.
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8665820
- MLA
- De Brouwer, Mathias, et al. “DIVIDE : Adaptive Context-Aware Query Derivation for IoT Data Streams.” Joint Proceedings of the International Workshops on Sensors and Actuators on the Web, and Semantic Statistics Co-Located with 18th International Semantic Web Conference (ISWC 2019), vol. 2549, CEUR, 2019, pp. 1–16.
- APA
- De Brouwer, M., Arndt, D., Bonte, P., De Turck, F., & Ongenae, F. (2019). DIVIDE : adaptive context-aware query derivation for IoT data streams. Joint Proceedings of the International Workshops on Sensors and Actuators on the Web, and Semantic Statistics Co-Located with 18th International Semantic Web Conference (ISWC 2019), 2549, 1–16. CEUR.
- Chicago author-date
- De Brouwer, Mathias, Dörthe Arndt, Pieter Bonte, Filip De Turck, and Femke Ongenae. 2019. “DIVIDE : Adaptive Context-Aware Query Derivation for IoT Data Streams.” In Joint Proceedings of the International Workshops on Sensors and Actuators on the Web, and Semantic Statistics Co-Located with 18th International Semantic Web Conference (ISWC 2019), 2549:1–16. CEUR.
- Chicago author-date (all authors)
- De Brouwer, Mathias, Dörthe Arndt, Pieter Bonte, Filip De Turck, and Femke Ongenae. 2019. “DIVIDE : Adaptive Context-Aware Query Derivation for IoT Data Streams.” In Joint Proceedings of the International Workshops on Sensors and Actuators on the Web, and Semantic Statistics Co-Located with 18th International Semantic Web Conference (ISWC 2019), 2549:1–16. CEUR.
- Vancouver
- 1.De Brouwer M, Arndt D, Bonte P, De Turck F, Ongenae F. DIVIDE : adaptive context-aware query derivation for IoT data streams. In: Joint Proceedings of the International Workshops on Sensors and Actuators on the Web, and Semantic Statistics co-located with 18th International Semantic Web Conference (ISWC 2019). CEUR; 2019. p. 1–16.
- IEEE
- [1]M. De Brouwer, D. Arndt, P. Bonte, F. De Turck, and F. Ongenae, “DIVIDE : adaptive context-aware query derivation for IoT data streams,” in Joint Proceedings of the International Workshops on Sensors and Actuators on the Web, and Semantic Statistics co-located with 18th International Semantic Web Conference (ISWC 2019), Auckland, New Zealand, 2019, vol. 2549, pp. 1–16.
@inproceedings{8665820,
abstract = {{In the Internet of Things, it is a challenging task to inte-grate & analyze high velocity sensor data with domain knowledge &context information in real-time. Semantic IoT platforms typically con-sist of stream processing components that use Semantic Web technologiesto run a set of fixed queries processing the IoT data streams. Configur-ing these queries is still a manual task. To deal with changes in contextinformation, which happen regularly in IoT domains, queries typicallyrequire reasoning on all sensor data in real-time to derive relevant sen-sors & events. This can be an issue in real-time, as expressive reasoningis required to deal with the complexity of many IoT domains. To solvethese issues, this paper presents DIVIDE. DIVIDE automatically derivesqueries for stream processing components in an adaptive, context-awareway. When the context changes, it derives through reasoning which sen-sors & observations to filter, given the context & a use case goal, withoutrequiring any more reasoning in real-time. This paper presents the detailsof DIVIDE, and performs evaluations on a healthcare example showinghow it can reduce real-time processing times, scale better when there aremore sensors & observations, and can run efficiently on low-end devices.}},
author = {{De Brouwer, Mathias and Arndt, Dörthe and Bonte, Pieter and De Turck, Filip and Ongenae, Femke}},
booktitle = {{Joint Proceedings of the International Workshops on Sensors and Actuators on the Web, and Semantic Statistics co-located with 18th International Semantic Web Conference (ISWC 2019)}},
issn = {{1613-0073}},
language = {{und}},
location = {{Auckland, New Zealand}},
pages = {{1--16}},
publisher = {{CEUR}},
title = {{DIVIDE : adaptive context-aware query derivation for IoT data streams}},
volume = {{2549}},
year = {{2019}},
}