An Efficient Distributed Index for Geospatial Databases

Van, Le Hong; Takasu, Atsuhiro

doi:10.1007/978-3-319-22849-5_3

Cited by 12 publications

(12 citation statements)

References 12 publications

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“…A number of studies have been proposed to reduce the false positives in spatial query processing by using a secondary index on top of HBase. 35,36 Recently, quadrant-based minimum bounding rectangle (QbMBR) tree indexing technique is proposed to reduce the false positives in spatial query. 37 This method groups spatial data more precisely by using QbMBR, which provides more selective query processing and reduces the storage space required for indexing.…”

Section: Indexing and Querying For Historical Datamentioning

confidence: 99%

k‐nearest reliable neighbor search in crowdsourced LBSs

Jang

Kim

Jung

2019

Int J Communication

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Summary To improve the quality of spatial information in a location‐based services (LBS), crowdsourced LBS (cLBS) applications that receive additional information such as the visit time of static spatial objects from users have appeared. In this paper, we propose a new type of nearest neighbor (NN) query called the k‐nearest reliable neighbor (kNRN) query, which searches for objects that are likely to exist. Suppose that in cLBSs, the user wants to find a restaurant that is likely to exist and is close to the user. In such a case, a kNRN query is highly recommended. In this paper, we formally define a data model in cLBSs and define reliable objects and a kNRN problem. As a brute‐force approach to this problem in a massive dataset that has large computational and I/O costs, we propose a 3DR‐tree‐based baseline algorithm, 2DR‐tree‐based incremental algorithm, and an a3DR‐tree‐based branch‐and‐bound algorithm for kNRN queries. A performance study is conducted on both synthetic and real datasets. Our experimental results show the efficiency of our proposed methods.

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Section: Indexing and Querying For Historical Datamentioning

confidence: 99%

k‐nearest reliable neighbor search in crowdsourced LBSs

Jang

Kim

Jung

2019

Int J Communication

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“…The objects located within the same cells are grouped together and are indexed with the same leaf node. BGRP-tree [ 11 ] also uses an in-memory R-tree. In this method, objects are indexed by unit of Hbase region, and the additional index named BGRP-tree is maintained to manage the result of dynamic region splitting.…”

Section: Related Workmentioning

confidence: 99%

“…As the linearization techniques cannot completely guarantee the spatial proximity of data, the linearization-based approaches sometimes include false positives, as shown in Figure 1 . Therefore, a significant number of studies [ 10 , 11 , 12 , 13 , 14 , 15 ] have recently proposed to reduce the false positives of query processing by implementing a secondary index on top of HBase. These methods use the multi-dimensional index such as a kd-tree or an R-tree to determine more accurately the range of the row key.…”

Section: Introductionmentioning

confidence: 99%

Quadrant-Based Minimum Bounding Rectangle-Tree Indexing Method for Similarity Queries over Big Spatial Data in HBase

Jung

2018

Sensors

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With the rapid development of mobile devices and sensors, effective searching methods for big spatial data have recently received a significant amount of attention. Owing to their large size, many applications typically store recently generated spatial data in NoSQL databases such as HBase. As the index of HBase only supports a one-dimensional row keys, the spatial data is commonly enumerated using linearization techniques. However, the linearization techniques cannot completely guarantee the spatial proximity of data. Therefore, several studies have attempted to reduce false positives in spatial query processing by implementing a multi-dimensional indexing layer. In this paper, we propose a hierarchical indexing structure called a quadrant-based minimum bounding rectangle (QbMBR) tree for effective spatial query processing in HBase. In our method, spatial objects are grouped more precisely by using QbMBR and are indexed based on QbMBR. The QbMBR tree not only provides more selective query processing, but also reduces the storage space required for indexing. Based on the QbMBR tree index, two query-processing algorithms for range query and kNN query are also proposed in this paper. The algorithms significantly reduce query execution times by prefetching the necessary index nodes into memory while traversing the QbMBR tree. Experimental analysis demonstrates that our method significantly outperforms existing methods.

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“…Typical methods include Geohash proposed by Morton [5] and GeoSOT proposed by Song et al [6], Sun and Cheng [7]. There are many research about the application of geohash-based index and the rapid retrieval of big spatial data using the classical meshing and coding method like [28]- [32], [34]- [38]. There are also some research focus on improving the efficiency of geohash encoding, such as [39] implemented a method of encodeing lat/lon coordinate to Geohash code on FPGA architecture.…”

Section: Introductionmentioning

confidence: 99%

A Geographic Meshing and Coding Method Based on Adaptive Hilbert-Geohash

Guo

Xiong

et al. 2019

IEEE Access

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Geographic meshing system is an essential technology in the digital earth framework and has essential applications in the integration and organization of heterogeneous spatial data, along with corresponding coding method. But current meshing and coding methods show unsatisfying locality and performance. To make an improvement, an adaptive Hilbert-Geohash meshing and coding method called AHG is proposed, which could represent both the location and the approximate size of the coded object directly by the meshing hierarchy and the corresponding coding length. This unique feature helps to accelerate the spatial range query and neighbor query. By simple string operations, many candidate objects that do not meet the query criteria can be quickly filtered out without precise spatial calculation. In addition, AHG code can also support spatial size query by finding objects whose size is within a certain interval quickly, without calculating the precise size of each object, which brings great convenience to spatial size statistics of a massive spatial dataset. Demonstrated by experiments over different types of the spatial dataset in a common PC environment, AHG shows favorable stability and scalability besides its capability in accelerating spatial query. The method is now applied successfully in several spatial query tools in a high-performance geographic information system called HiGIS.

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An Efficient Distributed Index for Geospatial Databases

Cited by 12 publications

References 12 publications

k‐nearest reliable neighbor search in crowdsourced LBSs

k‐nearest reliable neighbor search in crowdsourced LBSs

Quadrant-Based Minimum Bounding Rectangle-Tree Indexing Method for Similarity Queries over Big Spatial Data in HBase

A Geographic Meshing and Coding Method Based on Adaptive Hilbert-Geohash

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