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Type: Conference paper
Title: Batch matching of conjunctive triple patterns over linked data streams in the internet of things
Author: Qin, Y.
Sheng, Q.
Falkner, N.
Shemshadi, A.
Curry, E.
Citation: Proceedings of the 27th International Conference on Scientific and Statistical Database Management, 2015 / Gupta, A., Rathbun, S. (ed./s), vol.29-June-2015, pp.41-1-41-6
Publisher: ACM
Issue Date: 2015
ISBN: 9781450337090
Conference Name: 27th International Conference on Scientific and Statistical Database Management (SSDBM) (29 Jun 2015 - 01 Jul 2015 : La Jolla, California)
Statement of
Yongrui Qin, Quan Z. Sheng, Nickolas J.G. Falkner, Ali Shemshadi, Edward Curry
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 technologies, such as Linked Data, which can facilitate machine- to-machine (M2M) communications 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 conjunctive triple pattern queries registered in the system by the consumers. We also design a new data structure, CTP-automata, to meet the high performance 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 system 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
Rights: ©2015 ACM
RMID: 0030045238
DOI: 10.1145/2791347.2791364
Appears in Collections:Computer Science publications

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