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Lazy approaches for interval timing correlation of sensor data streams.

Kiseong Lee1, Chan-Gun Lee

  • 1Department of CSE, Chung-Ang University, 221 Heukseok, Dongjak, Seoul, Korea. goory00@gmail.com

Sensors (Basel, Switzerland)
|January 6, 2012
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Summary

We developed new algorithms for correlating streaming sensor data with uncertain timestamps. These methods efficiently handle timing queries like deadlines and delays, improving performance with techniques like lazy evaluation.

Keywords:
correlationintervallazysensortimestamps

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Area of Science:

  • Computer Science
  • Data Engineering
  • Algorithm Design

Background:

  • Streaming sensor data often contains temporal uncertainties, complicating accurate time-based analysis.
  • Existing timing correlation methods may struggle with interval timestamps and efficiency.

Purpose of the Study:

  • To propose novel algorithms for the timing correlation of streaming sensor data with interval timestamps.
  • To enhance the efficiency of timing correlation for various temporal predicates.

Main Methods:

  • Developed algorithms incorporating lazy evaluation and result caching for performance optimization.
  • Implemented and tested algorithms for timing correlation of streaming sensor data.
  • Evaluated performance under diverse workloads.

Main Results:

  • The proposed algorithms demonstrate efficient timing correlation for interval-stamped sensor data.
  • Lazy evaluation and result cache significantly improve algorithm performance.
  • Algorithms effectively support timing predicates including deadline, delay, and within.

Conclusions:

  • Novel algorithms provide efficient solutions for timing correlation of uncertain sensor data.
  • The techniques enhance the processing of time-based queries in streaming data environments.
  • Performance improvements were validated across various testing scenarios.