Finding the Plateau in an Aggregated Time Series (original) (raw)

Abstract

Given d input time series, an aggregated series can be formed by aggregating the d values at each time position. It is often useful to find the time positions whose aggregated values are the greatest. Instead of looking for individual top-k time positions, this paper gives two algorithms for finding the time interval (called the plateau) in which the aggregated values are close to each other (within a given threshold) and are all no smaller than the aggregated values outside of the interval. The first algorithm is a centralized one assuming that all data are available at a central location, and the other is a distributed search algorithm that does not require such a central location. The centralized algorithm has a linear time complexity with respect to the length of the time series, and the distributed algorithm employs the Threshold Algorithm by Fagin et al. and is quite efficient in reducing the communication cost as shown by the experiments reported in the paper.

Preview

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Deligiannakis, A., Kotidis, Y., Roussopoulos, N.: Hierarchical in-network data aggregation with quality guarantees. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 658–675. Springer, Heidelberg (2004)
    Chapter Google Scholar
  2. Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. Journal of Computer and System Sciences 66, 614–656 (2003)
    Article MATH MathSciNet Google Scholar
  3. Gunopulos, D.: Data storage and analysis in sensor networks with large memories. Presentation at IBM Watson Research Center (2005)
    Google Scholar
  4. Lang, C.A., Chang, Y.-C., Smith, J.R.: Making the threshold algorithm access cost aware. IEEE Trans. Knowl. Data Eng. 16(10), 1297–1301 (2004)
    Article Google Scholar
  5. Madden, S., Franklin, M.J., Hellerstein, J.M.: TAG: A tiny aggregation service for ad-hoc sensor networks. In: Proceedings of OSDI (2002)
    Google Scholar
  6. Manjhi, A., Nath, S., Gibbons, P.B.: Tributaries and deltas: Efficient and robust aggregation in sensor network streams. In: Proceedings of ACM SIGMOD (2005)
    Google Scholar
  7. Sharifzadeh, M., Shahabi, C.: Supporting spatial aggregation in sensor network databases. In: Proceedings of ACM-GIS (2004)
    Google Scholar

Download references

Author information

Authors and Affiliations

  1. IBM T. J. Watson Research Center, Hawthorne, NY, 10532, USA
    Min Wang
  2. Department of Computer Science, The University of Vermont, Burlington, VT, 05405, USA
    X. Sean Wang

Authors

  1. Min Wang
  2. X. Sean Wang

Editor information

Editors and Affiliations

  1. Chinese University of Hong Kong, Hong Kong, China
    Jeffrey Xu Yu
  2. Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, 153-8505, Tokyo, Japan
    Masaru Kitsuregawa
  3. Department of Computing, Hong Kong Polytechnic University, Hong Kong
    Hong Va Leong

Rights and permissions

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wang, M., Wang, X.S. (2006). Finding the Plateau in an Aggregated Time Series. In: Yu, J.X., Kitsuregawa, M., Leong, H.V. (eds) Advances in Web-Age Information Management. WAIM 2006. Lecture Notes in Computer Science, vol 4016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11775300\_28

Download citation

Publish with us