Matching Over Linked Data Streams in the Internet of Things

Yongrui Qin, Quan Z. Sheng, Edward Curry

Research output: Contribution to journalArticle

14 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. 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.

Original languageEnglish
Pages (from-to)21-27
Number of pages7
JournalIEEE Internet Computing
Volume19
Issue number3
Early online date19 Feb 2015
DOIs
Publication statusPublished - 1 May 2015
Externally publishedYes

Fingerprint

Semantics
Intelligent buildings
Data structures
Internet
Internet of things
Machine-to-machine communication

Cite this

Qin, Yongrui ; Sheng, Quan Z. ; Curry, Edward. / Matching Over Linked Data Streams in the Internet of Things. In: IEEE Internet Computing. 2015 ; Vol. 19, No. 3. pp. 21-27.
@article{f838159f20fa424d94955e0a449deba5,
title = "Matching Over Linked Data Streams in the Internet of Things",
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. 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.",
keywords = "CPSS, cyber-physical-social systems, Internet/Web technologies, linked data, query index, stream dissemination, stream processing",
author = "Yongrui Qin and Sheng, {Quan Z.} and Edward Curry",
year = "2015",
month = "5",
day = "1",
doi = "10.1109/MIC.2015.29",
language = "English",
volume = "19",
pages = "21--27",
journal = "IEEE Internet Computing",
issn = "1089-7801",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

Matching Over Linked Data Streams in the Internet of Things. / Qin, Yongrui; Sheng, Quan Z.; Curry, Edward.

In: IEEE Internet Computing, Vol. 19, No. 3, 01.05.2015, p. 21-27.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Matching Over Linked Data Streams in the Internet of Things

AU - Qin, Yongrui

AU - Sheng, Quan Z.

AU - Curry, Edward

PY - 2015/5/1

Y1 - 2015/5/1

N2 - 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. 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.

AB - 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. 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.

KW - CPSS

KW - cyber-physical-social systems

KW - Internet/Web technologies

KW - linked data

KW - query index

KW - stream dissemination

KW - stream processing

UR - http://www.scopus.com/inward/record.url?scp=84930673995&partnerID=8YFLogxK

U2 - 10.1109/MIC.2015.29

DO - 10.1109/MIC.2015.29

M3 - Article

VL - 19

SP - 21

EP - 27

JO - IEEE Internet Computing

JF - IEEE Internet Computing

SN - 1089-7801

IS - 3

ER -