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Cluster Computing

, Volume 22, Supplement 4, pp 8007–8015 | Cite as

The methods of big data fusion and semantic collision detection in Internet of Thing

  • Ruo HuEmail author
  • Hui-min Zhao
  • Yantai Wu
Article

Abstract

We sometimes find ourselves with plenty of data fusion in Internet of Thing, which necessitates an automatic removing semantic collision. For this, it is necessary to detect semantic collision, with a fairly reliable method to find many semantic collision and powerful enough to run in a reasonable time. Big data fusion in Internet of Thing represents today an important data quality challenge which leads to bad decision-making. This paper proposes and compares on real data effective fusion matching methods for automatic removing semantic collision of files based on names, working with Chinese texts or English texts, and the names of people or places, in East or in the West. After conducting a more complete classification of big data fusion than the usual classifications, we introduce several methods for big data fusion. Through a simple model, we highlight a global efficiency, accuracy and recover. We propose a new measuring mechanism between records, as well as rules for automatic big data fusion. Analyses made on Internet of Thing containing real data in western cities, and on a known standard Internet of Thing containing names of companies in the China, have shown better results than those of known methods, with a lesser complexity.

Keywords

Big data fusion Semantic collision Internet of Thing Measuring mechanism Matching methods 

Notes

Acknowledgements

This study is supported by Natural Science Fund Project in Guangdong province (No.2015A030313671) and Major Project for Guangzhou collaborative innovation of industry-university-research (No.201704020196). This study is supported by Guangzhou Key Laboratory of Digital Content Processing and security technologies and Guangdong provincial Application-oriented technical research and development Special fund project (2016B010127006) and International Scientific and technological cooperation projects of Guangdong province (2017A050501039).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer ScienceGuangdong Polytechnic Normal UniversityGuangzhouChina
  2. 2.The Guangzhou Key Laboratory of Digital Content Processing and Security TechnologiesGuangzhouChina
  3. 3.School of Internet Finance and Information EngineeringGuangdong University of FinanceGuangzhouChina

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