Lazy approaches for interval timing correlation of sensor data streams

We propose novel algorithms for the timing correlation of streaming sensor data. The sensor data are assumed to have interval timestamps so that they can represent temporal uncertainties. The proposed algorithms can support efficient timing correlation for various timing predicates such as deadline,...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2010-06, Vol.10 (6), p.5329-5345
Main Authors: Lee, Kiseong, Lee, Chan-Gun
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Biosensing Techniques - instrumentation
Biosensing Techniques - statistics & numerical data
correlation
Data Interpretation, Statistical
Data processing
Efficiency
Humans
interval
lazy
Probability
Probability distribution
sensor
Sensors
Signal Processing, Computer-Assisted - instrumentation
Time Factors
timestamps
User-Computer Interface
Workload
Workloads
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Summary:We propose novel algorithms for the timing correlation of streaming sensor data. The sensor data are assumed to have interval timestamps so that they can represent temporal uncertainties. The proposed algorithms can support efficient timing correlation for various timing predicates such as deadline, delay, and within. In addition to the classical techniques, lazy evaluation and result cache are utilized to improve the algorithm performance. The proposed algorithms are implemented and compared under various workloads.
ISSN:1424-8220
1424-8220
DOI:10.3390/s100605329