Matching over linked data streams in the internet of things

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dc.contributor.authorQin, Y.
dc.contributor.authorSheng, Q.
dc.contributor.authorCurry, E.
dc.date.issued2015
dc.description.abstractThe Internet of Things (IoT) envisions smart objects collecting and sharing data at a global scale via the Internet. One challenging issue is how to disseminate data to relevant data consumers efficiently. This article leverages semantic technologies, such as Linked Data, which can facilitate machine-to-machine communications to build an efficient stream dissemination system for Semantic IoT. The system integrates Linked Data streams generated from various data collectors and disseminates matched data to relevant data consumers based on user queries registered in the system. Here, the authors present a new data structure, TP-automata, designed to suit the high-performance needs of Linked Data stream dissemination. They evaluate the system using a real-world dataset generated in a Smart Building IoT Project. The proposed system can disseminate Linked Data streams at one million triples per second with 100,000 registered user queries, which is several orders of magnitude faster than existing techniques.
dc.description.statementofresponsibilityYongrui Qin, Quan Z. Sheng, Edward Curry
dc.identifier.citationIEEE Internet Computing, 2015; 19(3):21-27
dc.identifier.doi10.1109/MIC.2015.29
dc.identifier.issn1089-7801
dc.identifier.issn1941-0131
dc.identifier.urihttp://hdl.handle.net/2440/109524
dc.language.isoen
dc.publisherIEEE Computer Society
dc.rights© 2015, IEEE
dc.source.urihttps://doi.org/10.1109/mic.2015.29
dc.subjectCyber-physical-social systems; linked data; stream processing; stream dissemination; query index; CPSS; Internet/Web technologies
dc.titleMatching over linked data streams in the internet of things
dc.typeJournal article
pubs.publication-statusPublished

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