Towards efficient dissemination of linked data in the Internet of Things

Yongrui Qin, Quan Z. Sheng, Nickolas J G Falkner, Ali Shemshadi, Edward Curry

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

The 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. In this paper, we leverage semantic technologies which can facilitate machine-to-machine communications, such as Linked Data, to build an efficient information 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 Basic Graph Patterns (BGPs) registered in the system by those consumers. To efficiently match BGPs against Linked Data streams, we introduce two types of matching, namely semantic matching and pattern matching, by considering whether the matching process supports semantic relatedness computation. Two new data structures, namely MVR-tree and TP-automata, are introduced to suit these types of matching respectively. Experiments show that an MVR-tree designed for semantic matching can achieve a twofold increase in throughput compared with the naive R-tree based method. TP-automata, as the first approach designed for pattern matching over Linked Data streams, also provides two to three orders of magnitude improvements on throughput compared with semantic matching approaches.

Original languageEnglish
Title of host publicationProceedings of the 2014 ACM International Conference on Information and Knowledge Management
Subtitle of host publicationCIKM 2014
PublisherAssociation for Computing Machinery, Inc
Pages1779-1782
Number of pages4
ISBN (Electronic)9781450325981
DOIs
Publication statusPublished - 3 Nov 2014
Externally publishedYes
Event23rd ACM International Conference on Information and Knowledge Management - Shanghai, China
Duration: 3 Nov 20147 Nov 2014
Conference number: 23
https://dl.acm.org/citation.cfm?id=26618291 (Link to Conference Details)

Conference

Conference23rd ACM International Conference on Information and Knowledge Management
Abbreviated titleCIKM 2014
Country/TerritoryChina
CityShanghai
Period3/11/147/11/14
Internet address

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