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Sensors 2018, 18(11), 3832; https://doi.org/10.3390/s18113832

Streaming MASSIF: Cascading Reasoning for Efficient Processing of IoT Data Streams

1
IDLab, Department of Information Technology, Ghent University—IMEC, B-9052 Ghent, Belgium
2
Politecnico di Milano, Department of Electronic, Informatics and Bioengineering, 20133 Milan, Italy
Current address: Technologiepark-Zwijnaarde 15, B-9052 Ghent, Belgium.
*
Author to whom correspondence should be addressed.
Received: 28 September 2018 / Revised: 4 November 2018 / Accepted: 5 November 2018 / Published: 8 November 2018
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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. View Full-Text
Keywords: Stream Reasoning; complex event processing; description logic reasoning; Cascading Reasoning; IoT Stream Reasoning; complex event processing; description logic reasoning; Cascading Reasoning; IoT
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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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, 3832.

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