A k-d tree-based algorithm to parallelize Kriging interpolation of big spatial data

Wei, Haitao; Du, Yunyan; Liang, Fuyuan; Zhou, Chenghu; Liu, Zhang; Yi, Jiawei; Xu, Kun; Wu, Di

doi:10.1080/15481603.2014.1002379

Cited by 21 publications

(21 citation statements)

References 28 publications

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“…The experimental fusion of sub-models in VI-B has shown the general feasibility of this approach to cope with massive observational data streams. In contrast to other approaches like spatial segmentation [20] or fixed sized windows [21], it follows the principle of continuity by applying a gradual or fuzzy update method in a spatio-temporal context. Applications like real-time monitoring or dynamic animations can thus be generated easily.…”

Section: Discussionmentioning

confidence: 99%

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Environmental monitoring of continuous phenomena by sensor data streams: A system approach based on Kriging

Lorkowski¹,

Brinkhoff²

2015

Advances in Computer Science Research

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Environmental monitoring as technological endeavour has to deal with limitations in respect to transmission capacities, computational resources and storage space. Despite the progress in ICT, those limitations remain significant because of rising demands like real-time monitoring on the one hand and increased amount of observations on the other. The geostatistic method of kriging, originally developed to manage spatial uncertainty, provides elaborate means for spatio-temporal interpolation and is therefore the method of choice wherever continuous phenomena are to be monitored by discrete observations. With its associated confidence estimation, the method can also be exploited to solve other problems like fusion of sub-models for continuous real-time updates or filtering massive sensor streams. In this work, we suggest a system, substantially based on kriging, to filter, process, interpolate, monitor and archive sensor data streams. We incorporate several algorithmic and technical solutions into this system and evaluate them by simulated and real data.

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Section: Discussionmentioning

confidence: 99%

“…Wei et al [20] use a k-d tree-based method to partition big datasets into child data groups which can be processed in parallel by kriging. This method is particularly suitable to cope with big datasets, but does not consider the temporal dimension.…”

Section: Related Workmentioning

confidence: 99%

Environmental monitoring of continuous phenomena by sensor data streams: A system approach based on Kriging

Lorkowski¹,

Brinkhoff²

2015

Advances in Computer Science Research

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“…Traditional attribute data partition methods, such as ID division or random partition, are not ideal for dividing spatial data . For the spatial data, a good spatial data partition strategy should ensure both optimal performance of spatial operation and data balance in the cluster (Wei et al, 2015). The spatial partition methods can be summarized into three categories (Eldawy, Alarabi, & Mokbel, 2015;, namely space partition, data partition, and spatial filling curve partition.…”

Section: Spatial Indexmentioning

confidence: 99%

“…The spatial partition methods can be summarized into three categories (Eldawy, Alarabi, & Mokbel, 2015;, namely space partition, data partition, and spatial filling curve partition. Based on the above partition methods, the corresponding spatial indexes are built for big spatial vector data in clusters, such as the k-d tree (Wei et al, 2015), Grid, G-tree (Zhong, Li, Tan, Zhou, & Gong, 2015), HQ-tree (Feng, Tang, Wei, & Xu, 2014)…”

Section: Spatial Indexmentioning

confidence: 99%

Big spatial vector data management: a review

Yao

2018

Big Earth Data

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Spatial vector data with high-precision and wide-coverage has exploded globally, such as land cover, social media, and other datasets, which provides a good opportunity to enhance the national macroscopic decision-making, social supervision, public services, and emergency capabilities. Simultaneously, it also brings great challenges in management technology for big spatial vector data (BSVD). In recent years, a large number of new concepts, parallel algorithms, processing tools, platforms, and applications have been proposed and developed to improve the value of BSVD from both academia and industry. To better understand BSVD and take advantage of its value effectively, this paper presents a review that surveys recent studies and research work in the data management field for BSVD. In this paper, we discuss and itemize this topic from three aspects according to different information technical levels of big spatial vector data management. It aims to help interested readers to learn about the latest research advances and choose the most suitable big data technologies and approaches depending on their system architectures. To support them more fully, firstly, we identify new concepts and ideas from numerous scholars about geographic information system to focus on BSVD scope in the big data era. Then, we conclude systematically not only the most recent published literatures but also a global view of main spatial technologies of BSVD, including data storage and organization, spatial index, processing methods, and spatial analysis. Finally, based on the above commentary and related work, several opportunities and challenges are listed as the future research interests and directions for reference.

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“…Liu [21] used the computation power of modern programmable graphics hardware (GPU) for 3D visualization in a reservoir modeling system. In terms of algorithms and model improvements, Liu [22] proposed an algorithm based on the k-d tree method to address the unevenly distributed spatial data. Hu et al [23] proposed an fast Fourier transform (FFT)-based parallel algorithm to accelerate regression kriging interpolation, which was computed on a GPU device.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Parallel GPU-Based Space-Time Kriging Framework

Zhang

Zheng

Wang

et al. 2018

IJGI

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In the study of spatiotemporal geographical phenomena, the space-time interpolation method is widely applied, and the demands for computing speed and accuracy are increasing. For nonprofessional modelers, utilizing the space-time interpolation method quickly is a challenge. To solve this problem, the classical ordinary kriging algorithm was selected and expanded to a spatiotemporal kriging algorithm. Using the OpenCL framework to integrate central processing unit (CPU) and graphic processing unit (GPU) computing resources, a parallel spatiotemporal kriging algorithm was implemented, and three experiments were conducted in this work to verify the results. The results indicated the following: (1) when the size of the prediction point dataset is consistent, the performance of the method is robust with the increasing size of the observation point dataset; (2) the acceleration effect of the parallel method increases with an increased number of predicted points. Compared with the original sequential program, the implementation of the improved parallel framework showed a 3.23 speedup, which obviously shortens the interpolation time; (3) when cross-validating the temperature data in the Beijing Tianjin Hebei region, the space-time acceleration model provides a better fit than traditional pure space interpolation.

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A k-d tree-based algorithm to parallelize Kriging interpolation of big spatial data

Cited by 21 publications

References 28 publications

Environmental monitoring of continuous phenomena by sensor data streams: A system approach based on Kriging

Environmental monitoring of continuous phenomena by sensor data streams: A system approach based on Kriging

Big spatial vector data management: a review

Implementation of a Parallel GPU-Based Space-Time Kriging Framework

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