• Journal of Cadastre & Land InformatiX
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• v.45 no.1
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• pp.169-179
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• 2015

Recently, as the amount of data increases rapidly, the development of IT technology entered the 'Big Data' era, dealing with large-volume of data at once. In the spatial field, a spatial data service technology is required to use that various and big amount of data. In this study, firstly, we explained the technology of typical spatial information data services abroad, and then we have developed large KML data processing techniques those can be applied as KML format to VWorld desktop. The test was conducted using a large KML data in order to verify the development KML partitioned methods and tools. As a result, the index file and the divided files are produced and it was visible in VWorld desktop.

• Spatial Information Research
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• v.21 no.6
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• pp.1-9
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• 2013

Recently, Construction information is being changed from CAD to BIM, and GIS is extending from outdoor to indoor information. In these circumstances, the needs of continuous use of construction information linked with GIS are growing constantly in stages of maintenance, operation and service as well as planning, design and construction. To this end, it is essential element to represent 3-dimensional large object efficiently in establishing BIM-GIS interoperability platform by combination of construction and spatial information. In this study, we design spatial data LOD for making spatial object and texture by level, and develop weight lightening algorithm of large spatial object.

• Journal of Advanced Information Technology and Convergence
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• v.9 no.2
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• pp.89-97
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• 2019

Recently, there is an increasing demand for applications utilizing maps and locations such as autonomous vehicles and location-based services. Since these applications are developed based on spatial data, interest in spatial data processing is increasing and various studies are being conducted. In this paper, I propose a parallel mining algorithm using the CUDA library to efficiently analyze large spatial data. Spatial data includes both geometric (spatial) and non-spatial (aspatial) attributes. The proposed parallel spatial data mining algorithm analyzes both the geometric and non-spatial relationships between two layers. The experiment was performed on graphics cards containing CUDA cores based on TIGER/Line data, which is the actual spatial data for the US census. Experimental results show that the proposed parallel algorithm using CUDA greatly improves spatial data mining performance.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.495-504
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• 2015

The Gaussian geostatistical model has been widely used for modeling spatial data. However, this model suffers from a severe difficulty in computation because inference requires to invert a large covariance matrix in evaluating log-likelihood. In addressing this computational challenge, three strategies have been employed: likelihood approximation, lower dimensional space approximation, and Markov random field approximation. In this paper, we reviewed statistical approaches attacking the computational challenge. As an illustration, we also applied integrated nested Laplace approximation (INLA) technology, one of Markov approximation approach, to real data to provide an example of its use in practice dealing with large spatial data.

• Spatial Information Research
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• v.17 no.3
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• pp.371-379
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• 2009

Recently, GPU (Graphics Processing Unit) has been improved rapidly on the need of speed for gaming. As a result, GPU contains multiple ALU (Arithmetic Logic Unit) for parallel processing of a lot of graphics data, such as transform, ray tracing, etc. Therefore, this paper proposed a technique for parallel processing of spatial data using GPU. Spatial data consists of multiple coordinates, and each coordinate contains value of x and y axis. To display spatial data graphics operations have to be processed to large amount of coordinates. Because the graphics operation is identical and coordinates are multiple data, SIMD (Single Instruction Multiple Data) parallel processing of GPU can be used for processing of spatial data to improve performance. This paper implemented SIMD parallel processing of spatial data using two kinds of SDK (Software Development Kit). CUDA and ATI Stream are used for NVIDIA and ATI GPU respectively. Experiments that measure time of calculation for graphics operations are carried out to observe enhancement of performance. Experimental result is reported that proposed method can enhance performance up to 1,162% for graphics operations. The proposed method that uses parallel processing with GPU for spatial data can be generally used to enhance performance for applications which deal with large amount of spatial data.

• Spatial Information Research
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• v.23 no.2
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• pp.1-9
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• 2015

Graphical processing unit (GPU) contains many arithmetic logic units (ALUs). Because many ALUs can be exploited to process parallel processing, GPU provides efficient data processing. The spatial data require many geographic coordinates to represent the shape of them in a map. The coordinates are usually stored as geodetic longitude and latitude. To display a map in 2-dimensional Cartesian coordinate system, the geodetic longitude and latitude should be converted to the Universal Transverse Mercator (UTM) coordinate system. The conversion to the other coordinate system and the rendering process to represent the converted coordinates to screen use complex floating-point computations. In this paper, we propose a parallel processing technique that processes the conversion and the rendering using the GPU to improve the performance. Large spatial data is stored in the disk on files. To process the large amount of spatial data efficiently, we propose a technique that merges the spatial data files to a large file and access the file with the method of memory mapped file. We implement the proposed technique and perform the experiment with the 747,302,971 points of the TIGER/Line spatial data. The result of the experiment is that the conversion time for the coordinate systems with the GPU is 30.16 times faster than the CPU only method and the rendering time is 80.40 times faster than the CPU.

• Spatial Information Research
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• v.19 no.4
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• pp.55-71
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• 2011

Recently, interest in cloud computing which provides IT resources as service form in IT field is increasing. As a result, much research has been done on the distributed data processing that store and manage a large amount of data in many servers. Meanwhile, in order to effectively utilize the spatial data which is rapidly increasing day by day with the growth of GIS technology, distributed processing of spatial data using cloud computing is essential. Therefore, in this paper, we review the representative distributed data processing techniques and we analyze the optimization requirements for performance improvement of the distributed processing techniques for a large amount of data. In addition, we uses the Hadoop and we evaluate the performance of the distributed data processing techniques for their optimization requirements.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.32-48
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• 2022

This study presented a spatial data serialization technique that can efficiently store and transmit large amounts of spatial data for precision road maps was designed and implemented. For efficient serialization, a binary spatial data structure is defined, and a coordinate value encoding technique without loss of information is designed using the Zigzag-Z-order curve. The spatial data serialization technique designed for precision road maps was tested, and the data size and encoding/decoding speed after encoding were compared with Protocol buffer and Geobuff. As a result, it was confirmed that the designed serialization method was excellent in data weight reduction performance and encoding speed. However, the decoding speed was inferior to other serialization techniques in linestring and polygon type spatial data. Through this study, it was confirmed that spatial data can be efficiently encoded, stored, and transmitted using binary serialization techniques.

• Journal of Korea Multimedia Society
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• v.4 no.6
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• pp.477-488
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• 2001

As total system performance depends on spatial dta management in spatial database system, low cost method is required. However, spatial data have many characters that are different from multimedia data, data size is almost similar by layer and variable from few bytes to tera bytes. So large data manager of EXODUS and Starburst and BLOB etc, make problems that is many Disk I/O and Disk space waste. This paper proposes new storage method for spatial data considering spatial dta characteristics.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.261-270
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• 2002

Recently, the demand of the Intelligent Transportation System(ITS) has been increased to a large extent, and a real-time traffic information service based on the internet system became very important. When ITS companies carry out real-time traffic services, they find some traffic data missing, and use the conventional method of reconstructing missing values by calculating average time trend. However, the method is found unsatisfactory, so that we develop a new method based the spatial and spatial-temporal models. A cross-validation technique shows that the spatial-temporal model outperforms the others.