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A Method of HBase Multi-Conditional Query for Ubiquitous Sensing Applications.

Bo Shen1,2, Yi-Chen Liao3, Dan Liu4

  • 1School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China. bshen@bjtu.edu.cn.

Sensors (Basel, Switzerland)
|September 15, 2018
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Summary

This study introduces a novel Hilbert space-filling curve method for HBase to enable efficient multi-conditional queries on big data. This approach improves data retrieval performance for ubiquitous sensing applications without secondary indexes.

Keywords:
HBaseHilbert space-filling curvemulti-conditional queryubiquitous sensing

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Area of Science:

  • Computer Science
  • Data Science
  • Database Systems

Background:

  • Big data from sensor networks holds significant value but requires efficient storage and retrieval.
  • HBase is crucial for storing large unstructured data but lacks native multi-conditional query support.
  • Ubiquitous sensing applications frequently require multi-conditional data analysis.

Purpose of the Study:

  • To develop a method for enhancing HBase's multi-conditional query capabilities.
  • To improve data analysis performance for big data from sensor networks.
  • To address the limitations of HBase in handling complex query demands.

Main Methods:

  • Constructing a linear index using a Hilbert space-filling curve.
  • Mapping multiple index-columns into a one-dimensional encoded sequence for RowKey generation.
  • Implementing R-tree-based optimization to reduce query encoding computational cost.

Main Results:

  • The proposed method enables efficient multi-conditional queries in HBase.
  • Experimental results show improved performance compared to existing methods.
  • The approach avoids the need for secondary index modes.

Conclusions:

  • The Hilbert space-filling curve method effectively enhances HBase for multi-conditional queries.
  • This technique offers a viable solution for big data analysis in sensor networks.
  • The R-tree optimization further boosts the efficiency of the proposed indexing schema.