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EARS-DM: Efficient Auto Correction Retrieval Scheme for Data Management in Edge Computing.

Kai Fan1, Jie Yin2, Kuan Zhang3

  • 1State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an 710071, China. kfan@mail.xidian.edu.cn.

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|October 26, 2018
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Summary
This summary is machine-generated.

This study introduces EARS-DM, an efficient auto-correction retrieval scheme for edge computing data management. It enhances data retrieval accuracy and reduces storage complexity by correcting query keywords and prioritizing important terms.

Keywords:
R-treeautomatic error correctionedge computingmulti-keywordprivacyrelevance ranked

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Area of Science:

  • Computer Science
  • Data Management
  • Network Security

Background:

  • Edge computing processes data near the source, reducing cloud load and bandwidth.
  • Security and privacy are critical concerns in edge computing environments.
  • Existing retrieval methods may struggle with noisy data and storage efficiency.

Purpose of the Study:

  • To propose an efficient auto-correction retrieval scheme for data management in edge computing.
  • To address security and privacy challenges in edge data retrieval.
  • To improve the accuracy and efficiency of data retrieval in edge networks.

Main Methods:

  • Developed EARS-DM (Efficient Auto-correction Retrieval Scheme for Data Management).
  • Implemented automatic error correction for query keywords, avoiding similar word expansion.
  • Utilized TF-IDF and syntactic weight for keyword relevance scoring.
  • Constructed an R-tree index with encrypted keywords, file identifiers (FID), and Bloom filters (BF).

Main Results:

  • EARS-DM tolerates spelling mistakes in query keywords.
  • The scheme reduces the complexity of index storage space.
  • It achieves higher relevance scores for important keywords.
  • Edge devices efficiently sort matching encrypted file identifiers by relevance.

Conclusions:

  • EARS-DM provides an efficient and accurate solution for data management in edge computing.
  • The proposed scheme enhances data retrieval security and privacy.
  • It effectively manages massive data generated at the edge.