A Novel Method for Similarity Search over Meteorological Time Series Data based on the Coulomb’s Law

Claudinei Garcia de Andrade, Marcela Xavier Ribeiro, Cristiane Yaguinuma, Marilde Terezinha Prado Santos

Abstract

Several areas of knowledge use systematic and controlled observation, obtained from measurements taken at regular intervals, as a tool for behavioral analysis of phenomena, such as meteorology, which uses the observations to predict the climate behavior. Furthermore, with the advance of technology, the instruments used to measure observations have grown dramatically and the amount of data available for analysis has become greater than the ability to analyze them. In this context, this paper aims to propose a method, based on the principle of Coulomb's Law, for similarity search in time series and thus discovering intrinsic knowledge from these data. Experimental results conducted on climatic data of Brazilian cities and the sea surface temperature showed that the proposed method outperforms traditional methods on performance and accuracy and it is promising for finding similarity in series.

References

  1. Agrawal, R., Faloutsos, C. & Swami, A., 1993. Efficient Similarity Search in Sequence Databases. Proc. Fourth Int'l Conf. Foundations of Data Organization and Algorithms (FODO), pp. 1-15.
  2. Bozkaya, T. a. O. Z. M., 1999. Indexing large metric spaces for similarity search queries.. ACM Transactions on Database Systems, pp. 361-404.
  3. Chan, K. & Fu, A.-C., 1999. Efficient Time Series Matching by Wavelets. Proc. 15th Int'l Conf. Data Eng. (ICDE).
  4. Databases and Images Group, 2012. Agrodatamine: Development of Algorithms and Methods of Data Mining to Support Researches on Climate Changes Regarding Agrometeorology | AgroDataMine. [Online] Available at: http://www.gbdi.icmc.usp.br/ agrodatamine/ [Accessed 19 12 2012].
  5. Fukunaga, K., 1990. Introduction to Statistical Pattern Recognition. 2nd ed. s.l.:Academic Press.
  6. Kent, A., Berry, M. M., Luehrs, L. V. & Perry, J. W., 1955. Machine literature searching VIII: Operational criteria for designing information retrieval systems. American Documentation, pp. 93-101.
  7. Keogh, E., 1997. A Fast and Robust Method for Pattern Matching in Time Series Databases. Proceedings of WUSS-97.
  8. Keogh, E., Chakrabarti, K., Mehrotra, S. & Pazzani, M., 2001. Locally adaptive dimensionality reduction for indexing large time series databases. Proceedings of the ACM SIGMOD Conference.
  9. Keogh, E., Chakrabarti, K., Pazzani, M. & Mehrotra, S., 2000. Dimensionality reduction for fast similarity search in large time series databases. Journal of Knowledge and Information Systems.
  10. Korn, F., Jagadish, H. & Faloutsos, C., 1997. Efficiently Supporting Ad Hoc Queries in Large Datasets of Time Sequences. Proc. ACM SIGMOD.
  11. Meadow, C. T., 1992. Text Information Retrieval Systems. s.l.:Academic Press, Inc.,.
  12. Morettin, W. O. B. e. P. A., 1987. Estatística Básica. 4 ed. s.l.:Atual Editora.
  13. Morinaka, Y., Yoshikawa, M., Amagasa, T. & Uemura, S., 2001. The L - index: An indexing structure for efficient subsequence matching in time sequence. Pacific-Asia Conference on Knowledge Discovery and Data Mining - PAKDD.
  14. National Weather Service, 2012. Climate Prediction Center. [Online] Available at: http://www.cpc.ncep. noaa.gov/products/analysis_monitoring/ensostuff/ONI _change.shtml [Accessed 19 12 2012].
  15. Torres, R. d. S. & Falcão, A. X., 2006. Content-Based Image Retrieval: Theory and. Revista de Informática Teórica e Aplicada, p. 161-185.
  16. Weber, R., Schek, H. J. & Blott, S., 1998. A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces. 24th Int'l Conf. Very Large Data Bases.
  17. Wei, W. W. S., 1990. Time Series Analysis - Univariate and Multivariate Methods. Second ed. s.l.:Addison Wesley.
  18. Wilf, H. S., 2002. Algorithms and Complexity. 2nd ed. s.l.:A. K. Peters.
Download


Paper Citation


in Harvard Style

Garcia de Andrade C., Xavier Ribeiro M., Yaguinuma C. and Terezinha Prado Santos M. (2013). A Novel Method for Similarity Search over Meteorological Time Series Data based on the Coulomb’s Law . In Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8565-59-4, pages 209-216. DOI: 10.5220/0004446702090216


in Bibtex Style

@conference{iceis13,
author={Claudinei Garcia de Andrade and Marcela Xavier Ribeiro and Cristiane Yaguinuma and Marilde Terezinha Prado Santos},
title={A Novel Method for Similarity Search over Meteorological Time Series Data based on the Coulomb’s Law},
booktitle={Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2013},
pages={209-216},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004446702090216},
isbn={978-989-8565-59-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - A Novel Method for Similarity Search over Meteorological Time Series Data based on the Coulomb’s Law
SN - 978-989-8565-59-4
AU - Garcia de Andrade C.
AU - Xavier Ribeiro M.
AU - Yaguinuma C.
AU - Terezinha Prado Santos M.
PY - 2013
SP - 209
EP - 216
DO - 10.5220/0004446702090216