Batch Matching of Conjunctive Triple Patterns over Linked Data Streams in the Internet of Things (bibtex)
by Yongrui Qin, Quan Z. Sheng, Nickolas J.G. Falkner, Ali Shemshadi, Edward Curry
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 consumers efficiently. This paper leverages semantic technologies, such as Linked Data, which can facilitate machineto-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.
Reference:
Yongrui Qin, Quan Z. Sheng, Nickolas J.G. Falkner, Ali Shemshadi, Edward Curry, "Batch Matching of Conjunctive Triple Patterns over Linked Data Streams in the Internet of Things", In 27th International Conference on Scientific and Statistical Database Management (SSDBM 2015), ACM New York, NY, USA, San Diego, California, USA,, 2015.
Bibtex Entry:
@inproceedings{Qin2015a,
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 consumers efficiently. This paper leverages semantic technologies, such as Linked Data, which can facilitate machineto-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.},
address = {San Diego, California, USA,},
author = {Qin, Yongrui and Sheng, Quan Z. and Falkner, Nickolas J.G. and Shemshadi, Ali and Curry, Edward},
booktitle = {27th International Conference on Scientific and Statistical Database Management (SSDBM 2015)},
doi = {10.1145/2791347.2791364},
file = {:Users/ed/Library/Application Support/Mendeley Desktop/Downloaded/Qin et al. - 2015 - Batch Matching of Conjunctive Triple Patterns over Linked Data Streams in the Internet of Things.pdf:pdf},
keywords = {Linked data,information dissemination,query index},
mendeley-tags = {Linked data,information dissemination,query index},
publisher = {ACM New York, NY, USA},
title = {{Batch Matching of Conjunctive Triple Patterns over Linked Data Streams in the Internet of Things}},
url = {http://www.edwardcurry.org/publications/Qin_SSDBM15.pdf},
year = {2015}
}
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