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Time Series Query

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Encyclopedia of Database Systems

Synonyms

Time sequence query; Time series search; Time sequence search

Definition

A time series query refers to one that finds, from a set of time series, the time series or subseries that satisfy a given search criteria. Time series are sequences of data points spaced at strictly increasing times. The search criteria are domain specific rules defined with time series statistics or models, temporal dependencies, similarity between time series or patterns, etc. In particular, similarity queries are of great importance for many real world applications like stock analysis, weather forecasting, network traffic monitoring, etc., which often involve high volumes of time series data and may use different similarity measures or pattern descriptions. In many cases, query processing consists of evaluating these queries in real-time or quasi-real time by using time series approximation techniques, indexing methods, incremental computation, and specialized searching strategies.

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Recommended Reading

  1. Agrawal R., Faloutsos C., and Swami A.N. Efficient similarity search in sequence databases. In Proc. 4th Int. Conf. on Foundations of Data Organization and Algorithms, 1993, pp. 69–84.

    Google Scholar 

  2. Chan K.P. and Fu A.W.-C. Efficient time series matching by wavelets. In Proc. 15th Int. Conf. on Data Engineering, 1999, pp. 126–133.

    Google Scholar 

  3. Chen Y., Nascimento M.A., Ooi B.C., and Tung A.K.H. Spade: on shape-based pattern detection in streaming time series. In Proc. 23rd Int. Conf. on Data Engineering, 2007, pp. 786–795.

    Google Scholar 

  4. Chen L. and Ng R. On the marriage of lp-norms and edit distance. In Proc. 30th Int. Conf. on Very Large Data Bases, 2004, pp. 792–803.

    Google Scholar 

  5. Das G., Gunopulos D., and Mannila H. Finding similar time series. In Principles of Data Mining and Knowledge Discovery, 1st European Symp., 1997, pp. 88–100.

    Google Scholar 

  6. Faloutsos C., Ranganathan M., and Manolopoulos Y. Fast subsequence matching in time-series databases, In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1994, pp. 419–429.

    Google Scholar 

  7. Gao L. and Wang X.S. Continuous similarity-based queries on streaming time series. IEEE Trans. Knowl. Data Eng., 17(10):1320–1332, 2005.

    Article  MathSciNet  Google Scholar 

  8. Keogh E.J. and Pazzani M.J. Scaling up dynamic time warping for datamining applications. In Proc. 6th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2000, pp. 285–289.

    Google Scholar 

  9. Keogh E.J. and (Ann) Ratanamahatana C. Exact indexing of dynamic time warping. Knowl. Inform. Syst., 7(3):358–386, 2005.

    Google Scholar 

  10. Korn F., Jagadish H.V., and Faloutsos C. Efficiently supporting ad hoc queries in large datasets of time sequences. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1997, pp. 289–300.

    Google Scholar 

  11. Qu Y., Wang C., Gao L., and Wang X.S. Supporting movement pattern queries in user-specified scales. IEEE Trans. Knowl. Data Eng., 15(1):26–42, 2003.

    Article  MathSciNet  Google Scholar 

  12. Rafiei D. and Mendelzon A. Similarity-based queries for time series data. In Proc. ACM SIGMOD Int. Conf. on Management of Data. pp. 13–25.1997,

    Google Scholar 

  13. Revesz P., Chen R., and Ouyang M. Approximate query evaluation using linear constraint databases. In Proc. 8th Int. Symp. Temporal Representation and Reasoning, 2001, pp. 170–175.

    Google Scholar 

  14. Yi B.-K. and Faloutsos C. Fast time sequence indexing for arbitrary LP norms. In Proc. 26th Int. Conf. on Very Large Data Bases, 2000, pp. 385–394.

    Google Scholar 

  15. Zhu Y. and Shasha D. Statstream: statistical monitoring of thousands of data streams in real time. In Proc. 28th Int. Conf. on Very Large Data Bases, 2002, pp. 358–369.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Teradata Corporation, San Diego, CA, USA

    Like Gao

  2. University of Vermont, Burlington, VT, USA

    X. Sean Wang

Authors
  1. Like Gao
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  2. X. Sean Wang
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Editor information

Editors and Affiliations

  1. College of Computing, Georgia Institute of Technology, 266 Ferst Drive, 30332-0765, Atlanta, GA, USA

    LING LIU (Professor) (Professor)

  2. Database Research Group David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada

    M. TAMER ÖZSU (Professor and Director, University Research Chair) (Professor and Director, University Research Chair)

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© 2009 Springer Science+Business Media, LLC

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Gao, L., Wang, X. (2009). Time Series Query. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_428

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