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

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.