ICB-Index: A New Indexing Technique for Continuous Time Sequences

  • Dmitry V. Maslov
  • Andrew A. Sidorov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4152)


Various application domains require databases to store time sequences. Very often time sequences describe some continuous processes at discrete time points. Many applications require queries to take into consideration not only explicit values of time sequences, but also the values of the processes represented by them (these values can be derived from explicit values by user-defined interpolation functions). For example, a user of industrial process control system may ask the following query: "Find those time intervals during which specified physical value, represented by a series of measurements, was greater than given limit value". We show that conventional secondary indexes are not suitable to support such queries. We also investigate the properties of IP-index – the first index structure supporting queries on time sequences taking into account the interpolation (so-called "queries on continuous time sequences"). We show that IP-index improves the performance of such queries, but its size is enormously big for many real-life sequences. This fact makes it nearly impossible to use IP-index in some application domains. In this paper we present a new indexing technique to support queries on continuous time sequences – ICB-index. ICB-index makes the performance of such queries as high as IP-index does, but it requires substantially less space than IP-index. The effectiveness of ICB-index is verified by experiments on sensor-generated time sequences from a power plant.


Time Sequence Interpolation Function Process Control System Indexing Technique Main Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Andre-Jönsson, H.: Indexing Strategies for Time Series Data. Linköping University Dissertation No 757 (2002)Google Scholar
  2. 2.
    Bettini, C., Wang, X.S., Bertino, E., Jajodia, S.: Semantic Assumptions and Query Evaluation in Temporal Databases. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 257–268 (1995)Google Scholar
  3. 3.
    Bonnet, P., Gehrke, J., Seshadri, P.: Towards sensor database systems. In: Tan, K.-L., Franklin, M.J., Lui, J.C.-S. (eds.) MDM 2001. LNCS, vol. 1987, pp. 3–14. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  4. 4.
    Bonnet, P., Seshadri, P.: Device Database Systems. In: Proceedings of the 16th International Conference on Data Engineering, pp. 194 (2000)Google Scholar
  5. 5.
    Chandra, R., Segev, A.: Managing Temporal Financial Data in an Extensible Database. In: Proceedings of 19th VLDB Conference, pp. 302–313 (1993)Google Scholar
  6. 6.
    Clifford, J., Warren, D.S.: Formal Semantics for Time in Databases. ACM Transactions on Database Systems 8(2), 214–254 (1983)CrossRefGoogle Scholar
  7. 7.
    Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast Subsequence Matching in Time-Series Databases. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 419–429 (1994)Google Scholar
  8. 8.
    Grumbach, S., Rigaux, P., Segoufin, L.: Manipulating Interpolated Data is Easier than You Thought. In: Proceedings of 26th VLDB Conference, pp. 156–165 (2000)Google Scholar
  9. 9.
    Grumbach, S., Rigaux, P., Segoufin, L.: Modeling and Querying Interpolated Spatial Data. In: Proceedings of 15th Journees Bases de Donnees Avancees (BDA), pp. 469–487 (1999)Google Scholar
  10. 10.
    Lin, L.: Management of 1-D Sequence Data – from Discrete to Continuous. Linköping University Dissertation No 561 (1999)Google Scholar
  11. 11.
    Lin, L., Risch, T.: Quering Continuous Time Sequences. In: Proceedings of 24th VLDB Conference, pp. 170–181 (1998)Google Scholar
  12. 12.
    Lin, L., Risch, T., Sköld, M., Badal, D.: Indexing Values of Time Sequences. In: Proceedings of the 5th International Conference on Information and Knowledge Management, pp. 223–232 (1996)Google Scholar
  13. 13.
    Nanopoulos, A., Manolopoulos, Y.: Indexing Time-Series Databases for Inverse Queries. In: Quirchmayr, G., Bench-Capon, T.J.M., Schweighofer, E. (eds.) DEXA 1998. LNCS, vol. 1460, pp. 551–560. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  14. 14.
    Neugebauer, L.: Optimization and Evaluation of Database Queries Including Embedded Interpolation Procedures. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 118–127 (1991)Google Scholar
  15. 15.
    Perng, C.-S., Wang, H., Zhang, S.R., Parker, D.S.: Landmarks: A New Model for Similarity Based Pattern Querying in Time Series Databases. In: Proceedings of the 16th International Confirence on Data Engineering, pp. 33–42 (2000)Google Scholar
  16. 16.
    Pratt, K.B., Fink, E.: Search for Patterns in Compressed Time Series. International Journal of Image and Graphics 2(1), 89–106 (2002)CrossRefGoogle Scholar
  17. 17.
    Ramakrishnan, R., Donjerkovic, D., Ranganathan, A., Beyer, K.S., Krishnaprasad, M.: SRQL: Sorted Relational Query Language. In: Proceedings of the 10th International Conference on Scientific and Statistical Database Management, pp. 84–95 (1998)Google Scholar
  18. 18.
    Richardson, J.: Supporting Lists in a Data Model (A Timely Approach). In: Proceedings of 18th VLDB Conference, pp. 127–138 (1992)Google Scholar
  19. 19.
    Sayood, K.: Introduction to data compression. The Morgan Kaufmann Publishers Inc., San Francisco (1996)MATHGoogle Scholar
  20. 20.
    Segev, A., Shoshani, A.: A Temporal Data Model Based on Time Sequences. In: Tansel, A.U., et al. (eds.) Temporal Databases – Theory, Design and Implementation, pp. 248–269. The Benjamin/Cummings Publishing Company (1993), ISBN 0-8053-2413-5Google Scholar
  21. 21.
    Segev, A., Shoshani, A.: Logical Modeling of Temporal Data. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 454–466 (1987)Google Scholar
  22. 22.
    Seshadri, P.: Management of Sequence Data. Ph.D. Thesis, University of Wisconsin, Computer Science Department (1996)Google Scholar
  23. 23.
    Seshadri, P., Livny, M., Ramakrishnan, R.: The Design and Implementation of a Sequence Database System. In: Proceedings of 22nd VLDB Conference, pp. 99–110 (1996)Google Scholar
  24. 24.
    Shasha, D.: Tuning Time Series Queries in Finance: Case Studies and Recommendations. Data Engineering Bulletin 22(2), 40–46 (1999)Google Scholar
  25. 25.
    Wolski, A., Kuha, J., Luukkanen, T., Pesonen, A.: Design of RapidBase – An Active Measurement Database System. In: Proceedings of International Database Engineering and Applications Symposium, pp. 75–82 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dmitry V. Maslov
    • 1
  • Andrew A. Sidorov
    • 1
  1. 1.Sensors Modules SystemsResearch and Innovation Company, Section 3SamaraRussia

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