An Efficient Indexing Approach for Continuous Spatial Approximate Keyword Queries over Geo-Textual Streaming Data
School of Computer Science, China University of Geosciences, Wuhan 430074, China
Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China
School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia
Author to whom correspondence should be addressed.
Received: 17 November 2018 / Revised: 22 January 2019 / Accepted: 24 January 2019 / Published: 28 January 2019
Current social-network-based and location-based-service applications need to handle continuous spatial approximate keyword queries over geo-textual streaming data of high density. The continuous query is a well-known expensive operation. The optimization of continuous query processing is still an open issue. For geo-textual streaming data, the performance issue is more serious since both location information and textual description need to be matched for each incoming streaming data tuple. The state-of-the-art continuous spatial-keyword query indexing approaches generally lack both support for approximate keyword matching and high-performance processing for geo-textual streaming data. Aiming to tackle this problem, this paper first proposes an indexing approach for efficient supporting of continuous spatial approximate keyword queries by integrating
signatures into an AP-tree, namely AP-tree
utilizes the one-permutation
hashing method to achieve a much lower signature maintenance costs compared with the traditional
hashing method because it only employs one hashing function instead of dozens. Towards providing a more efficient indexing approach, this paper has explored the feasibility of parallelizing AP-tree
by employing a Graphic Processing Unit (GPU). We mapped the AP-tree
data structure into the GPU’s memory with a variety of one-dimensional arrays to form the GPU-aided AP-tree
. Furthermore, a
parallel hashing algorithm with a scheme of data parallel and a GPU-CPU data communication method based on a four-stage pipeline way have been used to optimize the performance of the GPU-aided AP-tree
. The experimental results indicate that (1) AP-tree
can reduce the space cost by about 11% compared with MHR-tree, (2) AP-tree
can hold a comparable recall and 5.64× query performance gain compared with MHR-tree while saving 41.66% maintenance cost on average, (3) the GPU-aided AP-tree
can attain an average speedup of 5.76× compared to AP-tree
, and (4) the GPU-CPU data communication scheme can further improve the query performance of the GPU-aided AP-tree
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MDPI and ACS Style
Deng, Z.; Wang, M.; Wang, L.; Huang, X.; Han, W.; Chu, J.; Zomaya, A.Y. An Efficient Indexing Approach for Continuous Spatial Approximate Keyword Queries over Geo-Textual Streaming Data. ISPRS Int. J. Geo-Inf. 2019, 8, 57.
Deng Z, Wang M, Wang L, Huang X, Han W, Chu J, Zomaya AY. An Efficient Indexing Approach for Continuous Spatial Approximate Keyword Queries over Geo-Textual Streaming Data. ISPRS International Journal of Geo-Information. 2019; 8(2):57.
Deng, Ze; Wang, Meng; Wang, Lizhe; Huang, Xiaohui; Han, Wei; Chu, Junde; Zomaya, Albert Y. 2019. "An Efficient Indexing Approach for Continuous Spatial Approximate Keyword Queries over Geo-Textual Streaming Data." ISPRS Int. J. Geo-Inf. 8, no. 2: 57.
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