research-article

Stream Reasoning for the Internet of Things: Challenges and Gap Analysis

Authors:

Ekaterina Gilman,

Teemu LeppänenAuthors Info & Claims

WIMS '16: Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics

June 2016

Article No.: 1, Pages 1 - 10

https://doi.org/10.1145/2912845.2912853

Published: 13 June 2016 Publication History

Abstract

The Internet of Things (IoT) is not only about interconnecting embedded devices to the Internet, but also about providing knowledge on such devices and what they sense from the physical world. One focus of IoT is put on extracting actionable knowledge and providing value-added services by means of reasoning techniques. Stream reasoning techniques offer a promising solution for processing dynamic, heterogeneous, and volume data for IoT. In this article, we identify the challenges for utilizing stream reasoners from the IoT point of view, review the landscape of stream reasoning techniques, and examine their capabilities to meet the challenges of IoT. Moreover, we present an experimental IoT system implementing stream reasoning and perform a gap analysis to evaluate stream reasoners. Finally, based on the analysis, we suggest several recommendations for future development of stream reasoners in order to overcome the identified gaps.

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cover image ACM Other conferences

WIMS '16: Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics

June 2016

309 pages

ISBN:9781450340564

DOI:10.1145/2912845

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 June 2016

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Research-article
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Walter Ahlströmin sääatiö
Tekniikan edistämissaäätiö
Tauno Tönningin saäätiö

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WIMS '16

WIMS '16: International Conference on Web Intelligence, Mining and Semantics

June 13 - 15, 2016

Nîmes, France

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WIMS '16 Paper Acceptance Rate 36 of 53 submissions, 68%;

Overall Acceptance Rate 140 of 278 submissions, 50%

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Cited By

Sowiński PSzmeja PGanzha MPaprzycki M(2024)RDF Stream Taxonomy: Systematizing RDF Stream Types in Research and PracticeElectronics10.3390/electronics1313255813:13(2558)Online publication date: 29-Jun-2024
https://doi.org/10.3390/electronics13132558
Soularidis AKotis KVouros G(2024)Real-Time Semantic Data Integration and Reasoning in Life- and Time-Critical Decision Support SystemsElectronics10.3390/electronics1303052613:3(526)Online publication date: 28-Jan-2024
https://doi.org/10.3390/electronics13030526
De Brouwer MBonte PArndt DVander Sande MDimou AVerborgh RDe Turck FOngenae F(2024)Optimized continuous homecare provisioning through distributed data-driven semantic services and cross-organizational workflowsJournal of Biomedical Semantics10.1186/s13326-024-00303-415:1Online publication date: 6-Jun-2024
https://doi.org/10.1186/s13326-024-00303-4
De Brouwer MDe Turck FOngenae F(2024)Enabling Efficient Semantic Stream Processing Across the IoT Network Through Adaptive Distribution with DIVIDEJournal of Network and Systems Management10.1007/s10922-023-09797-232:2Online publication date: 21-Feb-2024
https://dl.acm.org/doi/10.1007/s10922-023-09797-2
De Brouwer MSteenwinckel BFang ZStojchevska MBonte PDe Turck FVan Hoecke SOngenae F(2023)Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by designSemantic Web10.3233/SW-22328114:5(893-941)Online publication date: 8-May-2023
https://doi.org/10.3233/SW-223281
Gruber NGlimm B(2023)A Comparative Study of Stream Reasoning EnginesThe Semantic Web10.1007/978-3-031-33455-9_2(21-37)Online publication date: 28-May-2023
https://dl.acm.org/doi/10.1007/978-3-031-33455-9_2
Cena FHaller ALefrançois MCena FHaller ALefrançois M(2020)Semantics in the EdgeSemantic Web10.3233/SW-20037911:4(571-580)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.3233/SW-200379
De Brouwer MBonte PArndt DVander Sande MHeyvaert PDimou AVerborgh RDe Turck FOngenae F(2020)Distributed Continuous Home Care Provisioning through Personalized Monitoring & Treatment PlanningCompanion Proceedings of the Web Conference 202010.1145/3366424.3383528(143-147)Online publication date: 20-Apr-2020
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Jang ILee DChoi JSon Y(2019)An Approach to Share Self-Taught Knowledge between Home IoT Devices at the EdgeSensors10.3390/s1904083319:4(833)Online publication date: 18-Feb-2019
https://doi.org/10.3390/s19040833
Kolajo TDaramola OAdebiyi A(2019)Big data stream analysis: a systematic literature reviewJournal of Big Data10.1186/s40537-019-0210-76:1Online publication date: 6-Jun-2019
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