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Journal Article
Similarity-based Change Detection for RDF in MapReduce
- Authors
- Taewhi Lee, Dong-Hyuk Im, Jongho Won
- Issue Date
- 2016-08
- Citation
- Procedia Computer Science, v.91, pp.789-797
- ISSN
- 1877-0509
- Publisher
- Elsevier
- Language
- English
- Type
- Journal Article
- Abstract
- Managing and analyzing huge amount of heterogeneous data are essential for various services in the Internet of Things (IoT). Such analysis requires keeping track of data versions over time, and consequently detecting changes between them. However, it is challenging to identify the differences between datasets in Resource Description Framework (RDF), which has gained great attention as a format for the semantic annotation of sensor data. This results from the property of RDF triples as an unordered set and the existence of blank nodes. Existing change detection techniques have limitations in terms of scalability or utilization of structural RDF features. In this paper, we describe the implementation details of similarity-based RDF change detection techniques on the well-known distributed processing framework, MapReduce. In addition, we present an experimental comparison of these change detection techniques.
- KSP Keywords
- Blank nodes, Change detection, Experimental comparison, Heterogeneous Data, Over time, RDF triples, Resource Description Framework(RDF), Similarity-based, detection techniques, distributed processing, internet of things(IoT)
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC ND)
(CC BY NC ND)
