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2018 | OriginalPaper | Chapter

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

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

Published in: Trends and Applications in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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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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previous chapter HOC-Tree: A Novel Index for Efficient Spatio-Temporal Range Search
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Metadata
Title
A Hybrid Index Model for Efficient Spatio-Temporal Search in HBase
Authors
Chengyuan Zhang
Lei Zhu
Jun Long
Shuangqiao Lin
Zhan Yang
Wenti Huang
Copyright Year
2018
Book
Trends and Applications in Knowledge Discovery and Data Mining

Print ISBN: 978-3-030-04502-9

Electronic ISBN: 978-3-030-04503-6

Copyright Year: 2018

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

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