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An Efficient Incremental Mining Algorithm for Discovering Sequential Pattern in Wireless Sensor Network Environments.

Xin Lyu1, Hongxu Ma2

  • 1College of Computer and Information, HoHai University, Nanjing 210098, China. lvxin@hhu.edu.cn.

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|December 23, 2018
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Summary
This summary is machine-generated.

This study introduces a novel incremental algorithm (NIA) for efficient sequential pattern mining in wireless sensor networks (WSNs). NIA enhances data processing speed and accuracy for intelligent decision-making from large WSN datasets.

Keywords:
WSNsbig dataincremental miningprefix projection databasereticular sequence tree

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Area of Science:

  • Computer Science
  • Data Mining
  • Wireless Sensor Networks

Background:

  • Wireless sensor networks (WSNs) generate vast amounts of data, necessitating efficient processing for intelligent management.
  • Traditional data mining algorithms face challenges with the rapid growth and frequent updates characteristic of WSN data.
  • Time constraints for decision-making in WSNs require optimized data analysis methods.

Purpose of the Study:

  • To propose an efficient incremental mining algorithm, novel incremental algorithm (NIA), for discovering sequential patterns in WSN data.
  • To enhance the overall efficiency of the sequential pattern mining process for WSN applications.
  • To address the challenges posed by large, dynamic datasets in WSNs.

Main Methods:

  • Developed a reasoned proof for incrementally updating frequent sequences, significantly reducing the mining space.
  • Improved the PrefixSpan algorithm (PrefixSpan+) using a mapping structure for more efficient prefix extension to sequential patterns.
  • Introduced a fast support number-counting algorithm employing a reticular tree to store potential frequent sequences and calculate support degrees efficiently.

Main Results:

  • The novel incremental algorithm (NIA) demonstrated superior performance compared to existing mining algorithms.
  • Experiments on real, benchmark, and synthetic datasets confirmed NIA's efficiency in terms of time cost, sensitivity, and space cost.
  • NIA effectively handles the challenges of large-scale and frequently updated data from WSNs.

Conclusions:

  • NIA provides an efficient and effective solution for sequential pattern mining in wireless sensor networks.
  • The proposed algorithm enhances decision-making capabilities by enabling faster and more accurate data analysis.
  • NIA represents a significant advancement in processing and analyzing the complex data generated by WSNs.