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Bonte P, Tommasini R, Della Valle E, et al. Streaming MASSIF: Cascading Reasoning for Efficient Processing of IoT Data Streams. Sensors (Basel). 2018;18(11)doi: 10.3390/s18113832.
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 (Basel, Switzerland), 18(11), . https://doi.org/10.3390/s18113832
Bonte, Pieter, et al. "Streaming MASSIF: Cascading Reasoning for Efficient Processing of IoT Data Streams." Sensors (Basel, Switzerland) vol. 18,11 (2018). doi: https://doi.org/10.3390/s18113832
Bonte P, Tommasini R, Della Valle E, De Turck F, Ongenae F. Streaming MASSIF: Cascading Reasoning for Efficient Processing of IoT Data Streams. Sensors (Basel). 2018 Nov 08;18(11). doi: 10.3390/s18113832. PMID: 30413104; PMCID: PMC6263684.
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Sensors (Basel). 2018 Nov 08;18(11). doi: 10.3390/s18113832.

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

Sensors (Basel, Switzerland)

Pieter Bonte, Riccardo Tommasini, Emanuele Della Valle, Filip De Turck, Femke Ongenae

Affiliations

  1. IDLab, Department of Information Technology, Ghent University-IMEC, B-9052 Ghent, Belgium. [email protected].
  2. Politecnico di Milano, Department of Electronic, Informatics and Bioengineering, 20133 Milan, Italy. [email protected].
  3. Politecnico di Milano, Department of Electronic, Informatics and Bioengineering, 20133 Milan, Italy. [email protected].
  4. IDLab, Department of Information Technology, Ghent University-IMEC, B-9052 Ghent, Belgium. [email protected].
  5. IDLab, Department of Information Technology, Ghent University-IMEC, B-9052 Ghent, Belgium. [email protected].

PMID: 30413104 PMCID: PMC6263684 DOI: 10.3390/s18113832

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.

Keywords: Cascading Reasoning; IoT; Stream Reasoning; complex event processing; description logic reasoning

Publication Types

Grant support