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Cluster Computing

, Volume 22, Supplement 1, pp 2383–2394 | Cite as

A distributed incremental information acquisition model for large-scale text data

  • Shengtao Sun
  • Jibing GongEmail author
  • Albert Y. Zomaya
  • Aizhi Wu
Article
  • 224 Downloads

Abstract

Timely discovering and acquiring information from incremental data on the Internet is a hot topic in a big data era. This paper presents a distributed incremental information acquisition model for large-scale text data. To obtain a lower false positive rate and higher efficiency of the traditional Bloom filter, a distributed multidimensional Bloom filter is designed and proposed to cope with the deduplication of large-scale Web URL text data. Three methods related to Bloom filter were compared based on the false positive rate and response efficiency. The results show that the distributed incremental information acquisition model for large-scale text data can achieve a high duplicate removal rate with a lower false positive rate.

Keywords

Big data analytics Deduplication of large-scale text data Distributed incremental information acquisition model Distributed multidimensional bloom filter False positive rate 

Notes

Acknowledgements

This work is supported by the National High Technology Research and Development 863 Program of China (No. 2015AA124102) and the Hebei Natural Science Foundation of China (No. F2015203280). Shengtao Sun also acknowledges the Chinese Scholarship Council (No. 201608130030) for a visiting scholarship at University of Sydney. The authors would like to show great appreciation for the works done by Lin Zhang, Yi Zhao and Lili Wang from the research group of Knowledge Engineering (KEG), in Yanshan University.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Information Science and EngineeringYanshan UniversityQinhuangdaoPeople’s Republic of China
  2. 2.School of Information TechnologiesUniversity of SydneySydneyAustralia
  3. 3.College of Vehicle and EnergyYanshan UniversityQinhuangdaoPeople’s Republic of China
  4. 4.Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, and Key Laboratory for Software Engineering of Hebei ProvinceQinhuangdaoPeople’s Republic of China

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