American Institute of Mathematical Sciences

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January  2016, 1(1): 81-91. doi: 10.3934/bdia.2016.1.81

Spatio-temporal keywords queries in HBase

Xiaoying Chen 1, , Chong Zhang 1, , Zonglin Shi 1, and  Weidong Xiao 1,

1. 

Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China, China, China, China

Received  May 2015 Revised  August 2015 Published  September 2015

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 spatio-temporal 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 spatio-temporal 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.
Keywords: Spatio-temporal keyword query, bloom filter, Hilbert curve, MapReduce., HBase.
Mathematics Subject Classification: Primary: 68W15; Secondary: 68P2.
Citation: Xiaoying Chen, Chong Zhang, Zonglin Shi, Weidong Xiao. Spatio-temporal keywords queries in HBase. Big Data & Information Analytics, 2016, 1 (1) : 81-91. doi: 10.3934/bdia.2016.1.81
References:
[1]

, HBase, 2015. Available from: http://hbase.apache.org.

[2]

, Hadoop, 2015. Available from: http://hadoop.apache.org.

[3]

J. Blustein and A. El-Maazawi, Bloom filters. a tutorial, analysis, and survey, Halifax, NS: Dalhousie University, (2002), 1-31.

[4]

C. Cheng, C. Sun, X. Xu and D. Zhang, A multi-dimensional index structure based on improved VA-file and CAN in the cloud, International Journal of Automation and Computing, 11 (2014), 109-117. doi: 10.1007/s11633-014-0772-y.

[5]

G. Cong, C. S. Jensen and D. Wu, Efficient retrieval of the top k most relevant spatial web objects, VLDB Endowment, 2 (2009), 337-348. doi: 10.14778/1687627.1687666.

[6]

I. D. Felipe, V. Hristidis and N. Rishe, Keyword search on spatial databases, In ICDE, (2008), 656-665. doi: 10.1109/ICDE.2008.4497474.

[7]

C. S. Jensen, D. Lin and B. C. Ooi, Query and update efficient B$^+$-tree based indexing of moving objects, VLDB Endowment, 30 (2004), 768-779. doi: 10.1016/B978-012088469-8.50068-1.

[8]

B. Moon, H. V. Jagadish, C. Faloutsos and J. H. Saltz, Analysis of the clustering properties of the Hilbert space-filling curve, IEEE Transactions on Knowledge and Data Engineering, 13 (2001), 124-141. doi: 10.1109/69.908985.

[9]

S. Nishimura, S. Das, D. Agrawal and A. E. Abbadi, MD-HBase: A Scalable Multi-dimensional Data Infrastructure for Location Aware Services, In MDM, 1 (2011), 7-16. doi: 10.1109/MDM.2011.41.

[10]

W. Zhou, J. Lu, Z. Luan, S. Wang, G. Xue and S. Yao, SNB-index: A SkipNet and B+ tree based auxiliary Cloud index, Cluster Computing, 17 (2014), 453-462. doi: 10.1007/s10586-013-0246-y.

show all references

References:
[1]

, HBase, 2015. Available from: http://hbase.apache.org.

[2]

, Hadoop, 2015. Available from: http://hadoop.apache.org.

[3]

J. Blustein and A. El-Maazawi, Bloom filters. a tutorial, analysis, and survey, Halifax, NS: Dalhousie University, (2002), 1-31.

[4]

C. Cheng, C. Sun, X. Xu and D. Zhang, A multi-dimensional index structure based on improved VA-file and CAN in the cloud, International Journal of Automation and Computing, 11 (2014), 109-117. doi: 10.1007/s11633-014-0772-y.

[5]

G. Cong, C. S. Jensen and D. Wu, Efficient retrieval of the top k most relevant spatial web objects, VLDB Endowment, 2 (2009), 337-348. doi: 10.14778/1687627.1687666.

[6]

I. D. Felipe, V. Hristidis and N. Rishe, Keyword search on spatial databases, In ICDE, (2008), 656-665. doi: 10.1109/ICDE.2008.4497474.

[7]

C. S. Jensen, D. Lin and B. C. Ooi, Query and update efficient B$^+$-tree based indexing of moving objects, VLDB Endowment, 30 (2004), 768-779. doi: 10.1016/B978-012088469-8.50068-1.

[8]

B. Moon, H. V. Jagadish, C. Faloutsos and J. H. Saltz, Analysis of the clustering properties of the Hilbert space-filling curve, IEEE Transactions on Knowledge and Data Engineering, 13 (2001), 124-141. doi: 10.1109/69.908985.

[9]

S. Nishimura, S. Das, D. Agrawal and A. E. Abbadi, MD-HBase: A Scalable Multi-dimensional Data Infrastructure for Location Aware Services, In MDM, 1 (2011), 7-16. doi: 10.1109/MDM.2011.41.

[10]

W. Zhou, J. Lu, Z. Luan, S. Wang, G. Xue and S. Yao, SNB-index: A SkipNet and B+ tree based auxiliary Cloud index, Cluster Computing, 17 (2014), 453-462. doi: 10.1007/s10586-013-0246-y.

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