An Efficient Indexing Approach for Continuous Spatial Approximate Keyword Queries over Geo-Textual Streaming Data

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 op...

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Vydané v:ISPRS international journal of geo-information Ročník 8; číslo 2; s. 57
Hlavní autori: Deng, Ze, Wang, Meng, Wang, Lizhe, Huang, Xiaohui, Han, Wei, Chu, Junde, Zomaya, Albert Y.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 28.01.2019
Predmet:
algorithms
Alliances
artificial intelligence
Central processing units
Communication
computer hardware
computer software
continuous query
CPUs
Data
data analysis
Data communication
Data processing
Data structures
Feasibility studies
Geographic information systems
geospatial data processing
GPU
Graphics processing units
Hash based algorithms
Indexing
indexing methods
Keywords
Location based services
Maintenance costs
Methods
Optimization
Parallel processing
Permutations
Queries
Query processing
Social networks
Social organization
spatial approximate keyword matching
Submarine pipelines
Trees
Workloads
ISSN:2220-9964, 2220-9964
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Shrnutí: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 m i n - w i s e signatures into an AP-tree, namely AP-tree + . AP-tree + utilizes the one-permutation m i n - w i s e hashing method to achieve a much lower signature maintenance costs compared with the traditional m i n - w i s e 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 m i n - w i s e 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 + by 39.4%.
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ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi8020057