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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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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. 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) (Vol. 2549, pp. 1–16). Auckland, New Zealand: 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},
}