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Assembler: Efficient Discovery of Spatial Co-evolving Patterns in Massive Geo-sensory Data.

Chao Zhang1, Yu Zheng2, Xiuli Ma3

  • 1Dept. of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.

KDD : Proceedings. International Conference on Knowledge Discovery & Data Mining
|December 26, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces Assembler, a method for discovering spatial co-evolving patterns (SCPs) in sensor data. Assembler efficiently identifies groups of sensors with correlated readings, overcoming challenges of noisy data and large search spaces.

Keywords:
Sensor networkco-evolving patternspatiotemporal data

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Area of Science:

  • Geospatial data analysis
  • Sensor networks
  • Pattern recognition

Background:

  • Geo-sensory applications are increasingly common, generating massive datasets.
  • Mining spatial co-evolving patterns (SCPs) is crucial but challenging due to noise and large search spaces.

Purpose of the Study:

  • To develop an efficient and scalable method for mining spatial co-evolving patterns (SCPs) from geo-sensory data.
  • To address the challenges of trivial fluctuations and large spatiotemporal search spaces in SCP mining.

Main Methods:

  • A two-stage approach named Assembler is proposed.
  • Stage 1: Wavelet transform filters noise, and a segment-and-group approach detects frequent individual sensor evolutions.
  • Stage 2: Frequent evolutions are assembled into SCPs, utilizing an SCP search tree for efficient pruning.

Main Results:

  • Assembler effectively filters trivial fluctuations and identifies frequent sensor evolutions.
  • The SCP search tree enables efficient generation of spatial co-evolving patterns.
  • Experiments demonstrate Assembler's effectiveness, efficiency, and scalability on real and synthetic data.

Conclusions:

  • Assembler provides an effective solution for mining spatial co-evolving patterns in large-scale geo-sensory data.
  • The method successfully addresses noise and search space challenges, offering a scalable approach for practical applications.