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Design of Effective Indexing Technique in Hadoop-Based Database

  • Jae-Sung Shim
  • Young-Hwan Jang
  • Yong-Wan Ju
  • Seok-Cheon Park
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 474)

Abstract

The recent rapid increase in the amount of data to be processed has led to the increased use of dispersed parallel processing of large-scale data analysis using open-source Hadoop’s MapReduce framework. The large-data processing method proposed by Google and Hadoop which implemented this are representative dispersed parallel processing methods, and the data are dispersedly saved on the HDFS(Hadoop Distributed File System). Such HDFS uses its own indexing technique when it comes to searching specific values from the saved files. Techniques that use conventional index, however, leads to problems like reduced search performance by not considering update and saving index in the disc. Therefore, the paper proposes effective DB indexing technique on Hadoop-based database.

Keywords

Hadoop Indexing technique BigData DB index B+-tree 

References

  1. 1.
    Ryu, H.-S., Choi, H.-S., Son, J.-H., Chung, Y.-D.: An Implementation of a BST index on a relational data warehouse system based on hadoop cloud. In: Proceeding of Korea Information Science Society, pp. 10–12 (2012)Google Scholar
  2. 2.
    Lee, H.-J., Kim, T.-H.: A MapReduce-based kNN join query processing algorithm for analyzing large-scale data. J. Korea Inf. Sci. Soc. 42, 504–511 (2015)Google Scholar
  3. 3.
    Kim, D.-M., Choi, J.-W., Woo, C.-W.: A design and development of big data indexing and search system using lucene. J. Internet Comput. Serv. 15, 107–115 (2014)CrossRefGoogle Scholar
  4. 4.
    Park, J.-H., Bok, K.-S., Yoo, J.-S.: Big data parallel processing technology trend. Commun. Korean Inst. Inf. Sci. Eng. 32, 18–26 (2014)Google Scholar
  5. 5.
    Park, H.-J., Gwon, Y.-H., An, Y.-M.: Big data and big data refinement technology. Korean Soc. Comput. Inf. Rev. 21, 1–8 (2013)Google Scholar
  6. 6.
    Oh, H.-J., Yun, B.-H., Choi, N.-H., Yoo, C.-J., Kim, Y.: Visualization for preferred locations and moving patterns according to user groups based on contents analysis in social big data. J. Korean Inst. Inf. Technol. 12, 195–203 (2014)Google Scholar
  7. 7.
    Kim, C.-S.: Hadoop based spatial bigdata index creation and processing. In: Proceeding of Korea Information Science Society, pp. 87–89 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Jae-Sung Shim
    • 1
  • Young-Hwan Jang
    • 1
  • Yong-Wan Ju
    • 2
  • Seok-Cheon Park
    • 3
  1. 1.Department of IT Convergence EngineeringGachon UniversitySeongnamSouth Korea
  2. 2.Correspondence Center of Korea Internet & Security AgencySeoulSouth Korea
  3. 3.Department of Computer EngineeringGachon UniversitySeongnamSouth Korea

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