Batch matching of conjunctive triple patterns over linked data streams in the internet of things

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

doi:10.1145/2791347.2791364

Batch matching of conjunctive triple patterns over linked data streams 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 proceeding › Conference proceeding contribution › peer-review

1 Citation (Scopus)

Abstract

The Internet of Things (IoT) envisions smart objects col-lecting and sharing data at a global scale via the Internet. One challenging issue is how to disseminate data to relevant consumers efficiently. This paper leverages semantic tech-nologies, such as Linked Data, which can facilitate machine-To-machine (M2M) communications to build an efficient in-formation dissemination system for semantic IoT. The sys-Tem integrates Linked Data streams generated from various data collectors and disseminates matched data to relevant data consumers based on conjunctive triple pattern queries registered in the system by the consumers. We also design a new data structure, CTP-Automata, to meet the high perfor-mance needs of Linked Data dissemination. We evaluate our system using a real-world dataset generated from a Smart Building Project. With CTP-Automata, the proposed sys-Tem can disseminate Linked Data at a speed of an order of magnitude faster than the existing approach with thousands of registered conjunctive queries.

Original language	English
Title of host publication	SSDBM 15
Subtitle of host publication	Proceedings of the 27th International Conference on Scientific and Statistical Database Management
Editors	Amarnath Gupta, Susan Rathbun
Place of Publication	New York, NY
Publisher	Association for Computing Machinery
Number of pages	6
ISBN (Electronic)	9781450337090
DOIs	https://doi.org/10.1145/2791347.2791364
Publication status	Published - 2015
Externally published	Yes
Event	27th International Conference on Scientific and Statistical Database Management, SSDBM 2015 - San Diego, United States Duration: 29 Jun 2015 → 1 Jul 2015

Other

Other	27th International Conference on Scientific and Statistical Database Management, SSDBM 2015
Country/Territory	United States
City	San Diego
Period	29/06/15 → 1/07/15

Keywords

linked data
information dissemination
query index

Access to Document

10.1145/2791347.2791364

Cite this

Qin, Y., Sheng, Q. Z., Falkner, N. J. G., Shemshadi, A., & Curry, E. (2015). Batch matching of conjunctive triple patterns over linked data streams in the internet of things. In A. Gupta, & S. Rathbun (Eds.), SSDBM 15: Proceedings of the 27th International Conference on Scientific and Statistical Database Management Article 41 Association for Computing Machinery. https://doi.org/10.1145/2791347.2791364

Qin, Yongrui ; Sheng, Quan Z. ; Falkner, Nickolas J. G. et al. / Batch matching of conjunctive triple patterns over linked data streams in the internet of things. SSDBM 15: Proceedings of the 27th International Conference on Scientific and Statistical Database Management. editor / Amarnath Gupta ; Susan Rathbun. New York, NY : Association for Computing Machinery, 2015.

@inproceedings{b69ea931f4374deb85f9ac154aee61a4,

title = "Batch matching of conjunctive triple patterns over linked data streams in the internet of things",

abstract = "The Internet of Things (IoT) envisions smart objects col-lecting and sharing data at a global scale via the Internet. One challenging issue is how to disseminate data to relevant consumers efficiently. This paper leverages semantic tech-nologies, such as Linked Data, which can facilitate machine-To-machine (M2M) communications to build an efficient in-formation dissemination system for semantic IoT. The sys-Tem integrates Linked Data streams generated from various data collectors and disseminates matched data to relevant data consumers based on conjunctive triple pattern queries registered in the system by the consumers. We also design a new data structure, CTP-Automata, to meet the high perfor-mance needs of Linked Data dissemination. We evaluate our system using a real-world dataset generated from a Smart Building Project. With CTP-Automata, the proposed sys-Tem can disseminate Linked Data at a speed of an order of magnitude faster than the existing approach with thousands of registered conjunctive queries.",

keywords = "linked data, information dissemination, query index",

author = "Yongrui Qin and Sheng, {Quan Z.} and Falkner, {Nickolas J. G.} and Ali Shemshadi and Edward Curry",

year = "2015",

doi = "10.1145/2791347.2791364",

language = "English",

editor = "Amarnath Gupta and Susan Rathbun",

booktitle = "SSDBM 15",

publisher = "Association for Computing Machinery",

note = "27th International Conference on Scientific and Statistical Database Management, SSDBM 2015 ; Conference date: 29-06-2015 Through 01-07-2015",

}

Qin, Y, Sheng, QZ, Falkner, NJG, Shemshadi, A & Curry, E 2015, Batch matching of conjunctive triple patterns over linked data streams in the internet of things. in A Gupta & S Rathbun (eds), SSDBM 15: Proceedings of the 27th International Conference on Scientific and Statistical Database Management., 41, Association for Computing Machinery, New York, NY, 27th International Conference on Scientific and Statistical Database Management, SSDBM 2015, San Diego, United States, 29/06/15. https://doi.org/10.1145/2791347.2791364

Batch matching of conjunctive triple patterns over linked data streams in the internet of things. / Qin, Yongrui; Sheng, Quan Z.; Falkner, Nickolas J. G. et al.
SSDBM 15: Proceedings of the 27th International Conference on Scientific and Statistical Database Management. ed. / Amarnath Gupta; Susan Rathbun. New York, NY: Association for Computing Machinery, 2015. 41.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Batch matching of conjunctive triple patterns over linked data streams in the internet of things

AU - Qin, Yongrui

AU - Sheng, Quan Z.

AU - Falkner, Nickolas J. G.

AU - Shemshadi, Ali

AU - Curry, Edward

PY - 2015

Y1 - 2015

N2 - The Internet of Things (IoT) envisions smart objects col-lecting and sharing data at a global scale via the Internet. One challenging issue is how to disseminate data to relevant consumers efficiently. This paper leverages semantic tech-nologies, such as Linked Data, which can facilitate machine-To-machine (M2M) communications to build an efficient in-formation dissemination system for semantic IoT. The sys-Tem integrates Linked Data streams generated from various data collectors and disseminates matched data to relevant data consumers based on conjunctive triple pattern queries registered in the system by the consumers. We also design a new data structure, CTP-Automata, to meet the high perfor-mance needs of Linked Data dissemination. We evaluate our system using a real-world dataset generated from a Smart Building Project. With CTP-Automata, the proposed sys-Tem can disseminate Linked Data at a speed of an order of magnitude faster than the existing approach with thousands of registered conjunctive queries.

AB - The Internet of Things (IoT) envisions smart objects col-lecting and sharing data at a global scale via the Internet. One challenging issue is how to disseminate data to relevant consumers efficiently. This paper leverages semantic tech-nologies, such as Linked Data, which can facilitate machine-To-machine (M2M) communications to build an efficient in-formation dissemination system for semantic IoT. The sys-Tem integrates Linked Data streams generated from various data collectors and disseminates matched data to relevant data consumers based on conjunctive triple pattern queries registered in the system by the consumers. We also design a new data structure, CTP-Automata, to meet the high perfor-mance needs of Linked Data dissemination. We evaluate our system using a real-world dataset generated from a Smart Building Project. With CTP-Automata, the proposed sys-Tem can disseminate Linked Data at a speed of an order of magnitude faster than the existing approach with thousands of registered conjunctive queries.

KW - linked data

KW - information dissemination

KW - query index

UR - http://www.scopus.com/inward/record.url?scp=84959510548&partnerID=8YFLogxK

U2 - 10.1145/2791347.2791364

DO - 10.1145/2791347.2791364

M3 - Conference proceeding contribution

AN - SCOPUS:84959510548

BT - SSDBM 15

A2 - Gupta, Amarnath

A2 - Rathbun, Susan

PB - Association for Computing Machinery

CY - New York, NY

T2 - 27th International Conference on Scientific and Statistical Database Management, SSDBM 2015

Y2 - 29 June 2015 through 1 July 2015

ER -

Qin Y, Sheng QZ, Falkner NJG, Shemshadi A, Curry E. Batch matching of conjunctive triple patterns over linked data streams in the internet of things. In Gupta A, Rathbun S, editors, SSDBM 15: Proceedings of the 27th International Conference on Scientific and Statistical Database Management. New York, NY: Association for Computing Machinery. 2015. 41 doi: 10.1145/2791347.2791364

Batch matching of conjunctive triple patterns over linked data streams in the internet of things

Abstract

Other

Keywords

Access to Document

Other files and links

Fingerprint

Cite this