Spatio-temporal Keywords Queries in HBase

  • Received: 01 May 2015 Published: 01 January 2016
  • With the amount of data accumulated to tens of billions of scale, HBase, a distributed key-value database, plays a significant role in providing effective and high-throughput data service and management. However, for the applications involving spatio-temporal data, there is no good solution, due to inefficient query processing in HBase. In this paper, we propose spatiotemporal keyword searching problem for HBase, which is a meaningful issue in real life and a new challenge in this platform. To solve this problem, a novel access model for HBase is designed, containing row keys for indexing spatiotemporal dimensions and Bloom filters for fast detecting the existence of query keywords. And then, two algorithms for spatio-temporal keyword queries are developed, one is suitable for the queries with ordinary selectivity, the other is a parallel algorithm based on MapReduce aiming for the large range queries. We evaluate our algorithms on a real dataset, and the empirical results show that they are capable to handle spatio-temporal keyword queries efficiently.

    Citation: Xiaoying Chen, Chong Zhang, Zonglin Shi, Weidong Xiao. Spatio-temporal Keywords Queries in HBase[J]. Big Data and Information Analytics, 2016, 1(1): 81-91. doi: 10.3934/bdia.2016.1.81

    Related Papers:

  • With the amount of data accumulated to tens of billions of scale, HBase, a distributed key-value database, plays a significant role in providing effective and high-throughput data service and management. However, for the applications involving spatio-temporal data, there is no good solution, due to inefficient query processing in HBase. In this paper, we propose spatiotemporal keyword searching problem for HBase, which is a meaningful issue in real life and a new challenge in this platform. To solve this problem, a novel access model for HBase is designed, containing row keys for indexing spatiotemporal dimensions and Bloom filters for fast detecting the existence of query keywords. And then, two algorithms for spatio-temporal keyword queries are developed, one is suitable for the queries with ordinary selectivity, the other is a parallel algorithm based on MapReduce aiming for the large range queries. We evaluate our algorithms on a real dataset, and the empirical results show that they are capable to handle spatio-temporal keyword queries efficiently.
    加载中
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    © 2016 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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