Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Time Series Query

  • Like GaoEmail author
  • X. Sean Wang
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_428


Time sequence query; Time sequence search; Time series search


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.


This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Agrawal R, Faloutsos C, Swami AN Efficient similarity search in sequence databases. In: Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms; 1993. p. 69–84.CrossRefGoogle Scholar
  2. 2.
    Chan KP, Fu AW-C. Efficient time series matching by wavelets. In: Proceedings of the 15th International Conference on Data Engineering; 1999. p. 126–33.Google Scholar
  3. 3.
    Chen Y, Nascimento MA, Ooi BC, Tung AKH. Spade: on shape-based pattern detection in streaming time series. In: Proceedings of the 23rd International Conference on Data Engineering; 2007. p. 786–95.Google Scholar
  4. 4.
    Chen L, Ng R. On the marriage of lp-norms and edit distance. In: Proceedings of the 30th International Conference on Very Large Data Bases; 2004. p. 792–803.CrossRefGoogle Scholar
  5. 5.
    Das G, Gunopulos D, Mannila H. Finding similar time series. In: Principles of Data Mining and Knowledge Discovery, 1st European Symposium; 1997. p. 88–100.CrossRefGoogle Scholar
  6. 6.
    Faloutsos C, Ranganathan M, Manolopoulos Y. Fast subsequence matching in time-series databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1994. p. 419–29.CrossRefGoogle Scholar
  7. 7.
    Gao L, Wang XS. Continuous similarity-based queries on streaming time series. IEEE Trans Knowl Data Eng. 2005;17(10):1320–32.CrossRefGoogle Scholar
  8. 8.
    Keogh EJ, Pazzani MJ. Scaling up dynamic time warping for datamining applications. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2000. p. 285–89.Google Scholar
  9. 9.
    Keogh EJ, (Ann) Ratanamahatana C. Exact indexing of dynamic time warping. Knowl Inform Syst. 2005;7(3):358–86.CrossRefGoogle Scholar
  10. 10.
    Korn F, Jagadish HV, Faloutsos C. Efficiently supporting ad hoc queries in large datasets of time sequences. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1997. p. 289–300.Google Scholar
  11. 11.
    Qu Y, Wang C, Gao L, Wang XS. Supporting movement pattern queries in user-specified scales. IEEE Trans Knowl Data Eng. 2003;15(1):26–42.CrossRefGoogle Scholar
  12. 12.
    Rafiei D, Mendelzon A. Similarity-based queries for time series data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1997. p. 13–25.Google Scholar
  13. 13.
    Revesz P, Chen R, Ouyang M. Approximate query evaluation using linear constraint databases. In: Proceedings of the 8th International Symposium on Temporal Representation and Reasoning; 2001. p. 170–75.Google Scholar
  14. 14.
    Yi B-K, Faloutsos C. Fast time sequence indexing for arbitrary LP norms. In: Proceedings of the 26th International Conference on Very Large Data Bases; 2000. p. 385–94.Google Scholar
  15. 15.
    Zhu Y, Shasha D. Statstream: statistical monitoring of thousands of data streams in real time. In: Proceedings of the 28th International Conference on Very Large Data Bases; 2002. p. 358–69.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Teradata CorporationSan DiegoUSA
  2. 2.School of Computer ScienceFudan UniversityShanghaiChina

Section editors and affiliations

  • Richard T. Snodgrass
    • 1
  • Christian S. Jensen
    • 2
  1. 1.University of ArizonaTucsonUSA
  2. 2.Aalborg UniversityAalborg ØstDenmark