An Approximate Multi-step k-NN Search in Time-Series Databases

  • Sanghun Lee
  • Bum-Soo Kim
  • Mi-Jung Choi
  • Yang-Sae Moon
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 279)

Abstract

In this paper, we propose an approximate solution to the multi-step k-NN search. The traditional multi-step k-NN search (1) determines a tolerance through a k-NN query on a multidimensional index and (2) retrieves the final k results by evaluating the tolerance-based range query on the index and by accessing the actual database. The proposed tolerance reduction-based (approximate) solution reduces a large number of candidates by adjusting the tolerance of the range query on the index. To obtain the tight tolerance, the proposed solution forcibly decreases the tolerance by the average ratio of high-dimensional and low-dimensional distances. Experimental results show that the proposed approximate solution significantly reduces the number of candidates and the k-NN search time over the existing one.

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References

  1. 1.
    Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B.: The R∗-tree: An Effi- cient and Robust Access Method for Points and Rectangles. In: Proc. Int’l Conf. on Management of Data, ACM SIGMOD, Atlantic City, New Jersey, pp. 322–331 (May 1990)Google Scholar
  2. 2.
    Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast Subsequence Matching in Time-Series Databases. In: Proc. Int’l Conf. on Management of Data, ACM SIGMOD, Minneapolis, Minnesota, pp. 419–429 (May 1994)Google Scholar
  3. 3.
    Han, W.-S., Lee, J., Moon, Y.-S., Hwang, S.-W., Yu, H.: A New Approach for Processing Ranked Subsequence Matching Based on Ranked Union. In: Proc. of Int’l Conf. on Management of Data, ACM SIGMOD, Athens, Greece (June 2011)Google Scholar
  4. 4.
    Korn, F., Sidiropoulos, N., Faloutsos, C., Siegel, E., Protopapas, Z.: Fast Nearest Neighbor Search in Medical Image Databases. In: Proc. of the 22nd Int’l Conference on Very Large Data Bases, Bombay, India, pp. 215–226 (September 1996)Google Scholar
  5. 5.
    Moon, Y.-S., Whang, K.-Y., Han, W.-S.: General Match: A Subsequence Matching Method in Time-Series Databases Based on Generalized Windows. In: Proc. Intl Conf. on Management of Data, ACM SIGMOD, Madison, Wisconsin, pp. 382–393 (June 2002)Google Scholar
  6. 6.
    Moon, Y.-S., Kim, B.-S., Kim, M.S., Whang, K.-Y.: Scaling-Invariant Bound-ary Image Matching Using Time-Series Matching Techniques. Data & Knowledge Engineering 69(10), 1022–1042 (2010)CrossRefGoogle Scholar
  7. 7.
    Roh, G., Roh, J., Hwang, S., Yi, B.: Supporting Pattern Matching Queries over Trajectories on Road Networks. IEEE Trans. on Knowledge and Data Engineering 23(11), 1 758–1759 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Sanghun Lee
    • 1
  • Bum-Soo Kim
    • 2
  • Mi-Jung Choi
    • 1
  • Yang-Sae Moon
    • 1
  1. 1.Department of Computer ScienceKangwon National UniversityChuncheon-siSouth Korea
  2. 2.Advanced Information Technology Research Center (AITrc)Korea Advanced Institute of Science and Technology (KAIST)DaejeonSouth Korea

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