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Streaming MASSIF : cascading reasoning for efficient processing of iot data streams

(2018) SENSORS. 18(11).
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Abstract
In the Internet of Things (IoT), multiple sensors and devices are generating heterogeneous streams of data. To perform meaningful analysis over multiple of these streams, stream processing needs to support expressive reasoning capabilities to infer implicit facts and temporal reasoning to capture temporal dependencies. However, current approaches cannot perform the required reasoning expressivity while detecting time dependencies over high frequency data streams. There is still a mismatch between the complexity of processing and the rate data is produced in volatile domains. Therefore, we introduce Streaming MASSIF, a Cascading Reasoning approach performing expressive reasoning and complex event processing over high velocity streams. Cascading Reasoning is a vision that solves the problem of expressive reasoning over high frequency streams by introducing a hierarchical approach consisting of multiple layers. Each layer minimizes the processed data and increases the complexity of the data processing. Cascading Reasoning is a vision that has not been fully realized. Streaming MASSIF is a layered approach allowing IoT service to subscribe to high-level and temporal dependent concepts in volatile data streams. We show that Streaming MASSIF is able to handle high velocity streams up to hundreds of events per second, in combination with expressive reasoning and complex event processing. Streaming MASSIF realizes the Cascading Reasoning vision and is able to combine high expressive reasoning with high throughput of processing. Furthermore, we formalize semantically how the different layers in our Cascading Reasoning Approach collaborate.

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Citation

Please use this url to cite or link to this publication:

MLA
Bonte, Pieter et al. “Streaming MASSIF : Cascading Reasoning for Efficient Processing of Iot Data Streams.” SENSORS 18.11 (2018): n. pag. Print.
APA
Bonte, P., Tommasini, R., Della Valle, E., De Turck, F., & Ongenae, F. (2018). Streaming MASSIF : cascading reasoning for efficient processing of iot data streams. SENSORS, 18(11).
Chicago author-date
Bonte, Pieter, Riccardo Tommasini, Emanuele Della Valle, Filip De Turck, and Femke Ongenae. 2018. “Streaming MASSIF : Cascading Reasoning for Efficient Processing of Iot Data Streams.” Sensors 18 (11).
Chicago author-date (all authors)
Bonte, Pieter, Riccardo Tommasini, Emanuele Della Valle, Filip De Turck, and Femke Ongenae. 2018. “Streaming MASSIF : Cascading Reasoning for Efficient Processing of Iot Data Streams.” Sensors 18 (11).
Vancouver
1.
Bonte P, Tommasini R, Della Valle E, De Turck F, Ongenae F. Streaming MASSIF : cascading reasoning for efficient processing of iot data streams. SENSORS. 2018;18(11).
IEEE
[1]
P. Bonte, R. Tommasini, E. Della Valle, F. De Turck, and F. Ongenae, “Streaming MASSIF : cascading reasoning for efficient processing of iot data streams,” SENSORS, vol. 18, no. 11, 2018.
@article{8586329,
  abstract     = {In the Internet of Things (IoT), multiple sensors and devices are generating heterogeneous streams of data. To perform meaningful analysis over multiple of these streams, stream processing needs to support expressive reasoning capabilities to infer implicit facts and temporal reasoning to capture temporal dependencies. However, current approaches cannot perform the required reasoning expressivity while detecting time dependencies over high frequency data streams. There is still a mismatch between the complexity of processing and the rate data is produced in volatile domains. Therefore, we introduce Streaming MASSIF, a Cascading Reasoning approach performing expressive reasoning and complex event processing over high velocity streams. Cascading Reasoning is a vision that solves the problem of expressive reasoning over high frequency streams by introducing a hierarchical approach consisting of multiple layers. Each layer minimizes the processed data and increases the complexity of the data processing. Cascading Reasoning is a vision that has not been fully realized. Streaming MASSIF is a layered approach allowing IoT service to subscribe to high-level and temporal dependent concepts in volatile data streams. We show that Streaming MASSIF is able to handle high velocity streams up to hundreds of events per second, in combination with expressive reasoning and complex event processing. Streaming MASSIF realizes the Cascading Reasoning vision and is able to combine high expressive reasoning with high throughput of processing. Furthermore, we formalize semantically how the different layers in our Cascading Reasoning Approach collaborate.},
  articleno    = {3832},
  author       = {Bonte, Pieter and Tommasini, Riccardo and Della Valle, Emanuele and De Turck, Filip and Ongenae, Femke},
  issn         = {1424-8220},
  journal      = {SENSORS},
  language     = {eng},
  number       = {11},
  title        = {Streaming MASSIF : cascading reasoning for efficient processing of iot data streams},
  url          = {http://dx.doi.org/10.3390/s18113832},
  volume       = {18},
  year         = {2018},
}

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