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
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/6/29
Y1 - 2015/6/29
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 - Information Dissemination
KW - Linked Data
KW - Query Index
UR - https://www.scopus.com/pages/publications/84959510548
U2 - 10.1145/2791347.2791364
DO - 10.1145/2791347.2791364
M3 - Conference Publication
AN - SCOPUS:84959510548
T3 - ACM International Conference Proceeding Series
BT - SSDBM 2015 - Proceedings of the 27th International Conference on Scientific and Statistical Database Management
A2 - Gupta, Amarnath
A2 - Rathbun, Susan
PB - Association for Computing Machinery
T2 - 27th International Conference on Scientific and Statistical Database Management, SSDBM 2015
Y2 - 29 June 2015 through 1 July 2015
ER -