Pattern Matching Over Linked Data Streams

Yongrui Qin; Quan Z. Sheng

doi:10.1007/978-3-319-49340-4_12

Pattern Matching Over Linked Data Streams

Yongrui Qin, Quan Z. Sheng

School of Computing and Engineering

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

Abstract

This chapter 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 triple pattern queries registered in the system by the consumers. We also design two new data structures, TP-automata and CTPautomata, 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 the new data structures, the proposed system can disseminate Linked Data faster than the existing approach with thousands of registered queries.

Original language	English
Title of host publication	Handbook of Big Data Technologies
Publisher	Springer International Publishing AG
Pages	409-427
Number of pages	19
ISBN (Electronic)	9783319493404
ISBN (Print)	9783319493398
DOIs	https://doi.org/10.1007/978-3-319-49340-4_12
Publication status	Published - 25 Feb 2017

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AB - This chapter 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 triple pattern queries registered in the system by the consumers. We also design two new data structures, TP-automata and CTPautomata, 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 the new data structures, the proposed system can disseminate Linked Data faster than the existing approach with thousands of registered queries.

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BT - Handbook of Big Data Technologies

PB - Springer International Publishing AG

ER -

Pattern Matching Over Linked Data Streams

Abstract

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