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A Hybrid Index Model for Efficient Spatio-Temporal Search in HBase

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Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11154))

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Abstract

With advances in geo-positioning technologies and geo-locat-ion services, there are a rapidly growing massive amount of spatio-tempor-al data collected in many applications such as location-aware devices and wireless communication, in which an object is described by its spatial location and its timestamp. Consequently, the study of spatio-temporal search which explores both geo-location information and temporal information of the data has attracted significant concern from research organizations and commercial communities. This work study the problem of spatio-temporal k-nearest neighbors search (STkNNS), which is fundamental in the spatial temporal queries. Based on HBase, a novel index structure is proposed, called Hybrid Spatio-Temporal HBase Index (HSTI for short), which is carefully designed and takes both spatial and temporal information into consideration to effectively reduce the search space. Based on HSTI, an efficient algorithm is developed to deal with spatio-temporal k-nearest neighbors search. Comprehensive experiments on real and synthetic data clearly show that HSTI is three to five times faster than the state-of-the-art technique.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61702560, 61379110, 61472450), the Key Research Program of Hunan Province (2016JC2018), Natural Science Foundation of Hunan Province (2018JJ3691), and Science and Technology Plan of Hunan Province (2016JC2011).

Author information

Authors and Affiliations

  1. School of Information Science, Central South University, Changsha, People’s Republic of China

    Chengyuan Zhang, Lei Zhu, Jun Long, Shuangqiao Lin, Zhan Yang & Wenti Huang

  2. Big Data and Knowledge Engineering Institute, Central South University, Changsha, People’s Republic of China

    Chengyuan Zhang, Lei Zhu, Jun Long, Shuangqiao Lin, Zhan Yang & Wenti Huang

Authors
  1. Chengyuan Zhang
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  2. Lei Zhu
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  3. Jun Long
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  4. Shuangqiao Lin
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  5. Zhan Yang
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  6. Wenti Huang
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Corresponding author

Correspondence to Lei Zhu .

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Editors and Affiliations

  1. University of Melbourne, Melbourne, VIC, Australia

    Mohadeseh Ganji

  2. University of Melbourne, Melbourne, VIC, Australia

    Lida Rashidi

  3. McGill University, Montreal, QC, Canada

    Benjamin C. M. Fung

  4. Griffith University, Gold Coast, QLD, Australia

    Can Wang

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Zhang, C., Zhu, L., Long, J., Lin, S., Yang, Z., Huang, W. (2018). A Hybrid Index Model for Efficient Spatio-Temporal Search in HBase. In: Ganji, M., Rashidi, L., Fung, B., Wang, C. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 11154. Springer, Cham. https://doi.org/10.1007/978-3-030-04503-6_9

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  • DOI: https://doi.org/10.1007/978-3-030-04503-6_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04502-9

  • Online ISBN: 978-3-030-04503-6

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