Multivariate stream data reduction in sensor network applications

Sungbo Seo, Jaewoo Kang, Keun Ho Ryu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

We evaluated several multivariate stream data reduction techniques that can be used in sensor network applications. The evaluated techniques include Wavelet-based methods, sampling, hierarchical clustering, and singular value decomposition (SVD). We tested the reduction methods over the range of different parameters including data reduction rate, data types, number of dimensions and data window size of the input stream. Both real and synthetic time series data were used for the evaluation. The results of experiments suggested that the reduction techniques should be evaluated in the context of applications, as different applications generate different types of data and that has a substantial impact on the performance of different reduction methods. The findings reported in this paper can serve as a useful guideline for sensor network design and construction.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages198-207
Number of pages10
Volume3823 LNCS
DOIs
Publication statusPublished - 2005 Dec 1
Externally publishedYes
EventEUC 2005 Workshops: UISW, NCUS, SecUbiq, USN, and TAUES - Nagasaki, Japan
Duration: 2005 Dec 62005 Dec 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3823 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherEUC 2005 Workshops: UISW, NCUS, SecUbiq, USN, and TAUES
CountryJapan
CityNagasaki
Period05/12/605/12/9

Fingerprint

Data Reduction
Sensor networks
Sensor Networks
Data reduction
Reduction Method
Hierarchical Clustering
Sampling Methods
Singular value decomposition
Network Design
Synthetic Data
Time Series Data
Cluster Analysis
Time series
Wavelets
Guidelines
Sampling
Evaluation
Range of data
Experiment
Experiments

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Seo, S., Kang, J., & Ryu, K. H. (2005). Multivariate stream data reduction in sensor network applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3823 LNCS, pp. 198-207). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3823 LNCS). https://doi.org/10.1007/11596042_21

Multivariate stream data reduction in sensor network applications. / Seo, Sungbo; Kang, Jaewoo; Ryu, Keun Ho.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3823 LNCS 2005. p. 198-207 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3823 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Seo, S, Kang, J & Ryu, KH 2005, Multivariate stream data reduction in sensor network applications. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3823 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3823 LNCS, pp. 198-207, EUC 2005 Workshops: UISW, NCUS, SecUbiq, USN, and TAUES, Nagasaki, Japan, 05/12/6. https://doi.org/10.1007/11596042_21
Seo S, Kang J, Ryu KH. Multivariate stream data reduction in sensor network applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3823 LNCS. 2005. p. 198-207. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/11596042_21
Seo, Sungbo ; Kang, Jaewoo ; Ryu, Keun Ho. / Multivariate stream data reduction in sensor network applications. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3823 LNCS 2005. pp. 198-207 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{ef6eb223d4f545fc8029adb8e67c5364,
title = "Multivariate stream data reduction in sensor network applications",
abstract = "We evaluated several multivariate stream data reduction techniques that can be used in sensor network applications. The evaluated techniques include Wavelet-based methods, sampling, hierarchical clustering, and singular value decomposition (SVD). We tested the reduction methods over the range of different parameters including data reduction rate, data types, number of dimensions and data window size of the input stream. Both real and synthetic time series data were used for the evaluation. The results of experiments suggested that the reduction techniques should be evaluated in the context of applications, as different applications generate different types of data and that has a substantial impact on the performance of different reduction methods. The findings reported in this paper can serve as a useful guideline for sensor network design and construction.",
author = "Sungbo Seo and Jaewoo Kang and Ryu, {Keun Ho}",
year = "2005",
month = "12",
day = "1",
doi = "10.1007/11596042_21",
language = "English",
isbn = "3540308032",
volume = "3823 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "198--207",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Multivariate stream data reduction in sensor network applications

AU - Seo, Sungbo

AU - Kang, Jaewoo

AU - Ryu, Keun Ho

PY - 2005/12/1

Y1 - 2005/12/1

N2 - We evaluated several multivariate stream data reduction techniques that can be used in sensor network applications. The evaluated techniques include Wavelet-based methods, sampling, hierarchical clustering, and singular value decomposition (SVD). We tested the reduction methods over the range of different parameters including data reduction rate, data types, number of dimensions and data window size of the input stream. Both real and synthetic time series data were used for the evaluation. The results of experiments suggested that the reduction techniques should be evaluated in the context of applications, as different applications generate different types of data and that has a substantial impact on the performance of different reduction methods. The findings reported in this paper can serve as a useful guideline for sensor network design and construction.

AB - We evaluated several multivariate stream data reduction techniques that can be used in sensor network applications. The evaluated techniques include Wavelet-based methods, sampling, hierarchical clustering, and singular value decomposition (SVD). We tested the reduction methods over the range of different parameters including data reduction rate, data types, number of dimensions and data window size of the input stream. Both real and synthetic time series data were used for the evaluation. The results of experiments suggested that the reduction techniques should be evaluated in the context of applications, as different applications generate different types of data and that has a substantial impact on the performance of different reduction methods. The findings reported in this paper can serve as a useful guideline for sensor network design and construction.

UR - http://www.scopus.com/inward/record.url?scp=33744916281&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33744916281&partnerID=8YFLogxK

U2 - 10.1007/11596042_21

DO - 10.1007/11596042_21

M3 - Conference contribution

AN - SCOPUS:33744916281

SN - 3540308032

SN - 9783540308034

VL - 3823 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 198

EP - 207

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -