Big Data Cleaning Algorithms in Cloud Computing

Authors

  • Feng Zhang School of Information Engineering, Yulin University, Yulin, 719000,China
  • Hui-Feng Xue
  • Dong-Sheng Xu
  • Yong-Heng Zhang
  • Fei You

DOI:

https://doi.org/10.3991/ijoe.v9i3.2765

Keywords:

big data, cleaning algorithms, cloud computing, data cleaning, Map-Reduce

Abstract


Big data cleaning is one of the important research issues in cloud computing theory. The existing data cleaning algorithms assume all the data can be loaded into the main memory at one-time, which are infeasible for big data. To this end, based on the knowledge base, a data cleaning algorithm is proposed in cloud computing by Map-Reduce. It extracts atomic knowledge of the selected nodes firstly, then analyzes their relations, deletes the same objects, builds an atomic knowledge sequence based on weights, lastly cleans data according to the sequence. The experimental results show that the cloud computing environment big data algorithm is effective and feasible, and has better expansibility.

Author Biography

Feng Zhang, School of Information Engineering, Yulin University, Yulin, 719000,China

School of Information Engineering, Yulin University, Yulin,719000, China

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Published

2013-06-11

How to Cite

Zhang, F., Xue, H.-F., Xu, D.-S., Zhang, Y.-H., & You, F. (2013). Big Data Cleaning Algorithms in Cloud Computing. International Journal of Online and Biomedical Engineering (iJOE), 9(3), pp. 77–81. https://doi.org/10.3991/ijoe.v9i3.2765

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Section

Papers