Indexing linked data in a wireless broadcast system with 3D Hilbert space-filling curves
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Date
2014
Authors
Qin, Y.
Sheng, Q.
Falkner, N.
Zhang, W.
Wang, H.
Editors
Wang, X.
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Conference paper
Citation
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 2014 / Wang, X. (ed./s), pp.1775-1778
Statement of Responsibility
Yongrui Qin, Quan Z. Sheng, Nickolas J.G. Falkner, Wei Emma Zhang, Hua Wang
Conference Name
23rd ACM International Conference on Conference on Information and Knowledge Management (CIKM) (3 Nov 2014 - 7 Nov 2014 : Shanghai, China)
Abstract
Semantic technologies aim to facilitate machine-to-machine communication and are attracting more and more interest from both academia and industry, especially in the emerging Internet of Things (IoT). In this paper, we consider large-scale information sharing scenarios among mobile objects in IoT by leveraging semantic techniques. We propose to broadcast Linked Data on-air using RDF format to allow simultaneous access to the information and to achieve better scalability. We introduce a novel air indexing method to reduce the information access latency and energy consumption. To build air indexes, we firstly map RDF triples in the Linked Data into points in a 3D space and build B+-trees based on 3D Hilbert curve mappings for all of the 3D points. We then convert these trees into linear sequences so that they can be broadcast over a wireless channel. A novel search algorithm is also designed to efficiently evaluate queries against the air indexes. Experiments show that our indexing method outperforms the air indexing method based on traditional 3D R-trees.
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Copyright 2014 ACM