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Data-independent vantage point selection for range queries

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Abstract

Vantage point-based indexing is a popular technique for implementing range queries in main memory database. Vantage points are reference points that are used to improve the performance of range queries. In the past, vantage points have been derived from the data points in the database by using various heuristics. These approaches are, therefore, data dependent and not able to handle dynamic databases (allowing insertions and deletions) easily. Further, the amount of time needed for deriving vantage points for these approaches is very high for larger databases. We propose a data-independent technique for creating vantage points. Constraint of our approach is that values in each dimension of the feature vectors have to be bounded. Extensive experiments with real and synthetic data show that the proposed technique is superior to existing methods.

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Acknowledgements

This research was partially supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (Grant No. 2012R1A1A2042552).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Alok Watve & Sakti Pramanik

  2. Department of Computer Science and Engineering, Sogang University, Seoul, Korea

    Sungwon Jung

  3. Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA

    Chae Yong Lim

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  1. Alok Watve
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Correspondence to Sungwon Jung.

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Watve, A., Pramanik, S., Jung, S. et al. Data-independent vantage point selection for range queries. J Supercomput 75, 7952–7978 (2019). https://doi.org/10.1007/s11227-018-2384-8

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