• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.119-131
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• 2018

The increase of high-resolution spatial data and methodological developments in recent years has enabled a detailed analysis of individual experiences in space and over time. However, despite the increasing availability of data and technological advances, such individual-level analysis is not always possible in practice because of its computing requirements. To overcome this limitation, there has been a considerable amount of research on the use of high-performance, public cloud computing platforms for spatial analysis and simulation. The purpose of this paper is to empirically evaluate the efficiency and effectiveness of spatial analysis in cloud computing platforms. We compare the computing speed for calculating the measure of spatial autocorrelation and performing geographically weighted regression analysis between a local machine and spot instances on clouds. The results indicate that there could be significant improvements in terms of computing time when the analysis is performed parallel on clouds.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.651-653
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• 2003

As a result of the project 'Development of core technology for open LBS', we developed several solutions such as personal navigation system, mobile game, and emergency system. In this paper, we explain the approach and architecture of these solutions and consider their meaning on spatial information bases and expectation in the future computing environments. The final goal of this suggested solution is to test the efficiency of our open LBS system and verify the applicability and usability in real application environments. Our approaches will be headed to the future ubiquitous computing environments.

• Information Systems Review
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• v.26 no.2
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• pp.205-221
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• 2024

In June 2023, the term "spatial computing" began gaining recognition among the public with Apple's Vision Pro announcement, and interest surged exponentially after its official release in February 2024. With the market opening up, there's a need to analyze public perception for sustainable growth of Spatial Computing and provide evidence-based strategies for industry and government response. This study explores domestic public perception of Spatial Computing using various text mining techniques and seeks strategic directions for successful market penetration based on the analysis. Significantly, the study contributes by leading research on Spatial Computing, proposing new research methodologies, and offering strategic and policy directions for stakeholders.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.2
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• pp.207-215
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• 2016

The demand for spatial data service technologies is increasing lately with the development of realistic 3D spatial information services and ICT (Information and Communication Technology). Research is being conducted on the real-time provision of spatial data services through a variety of mobile and Web-based contents. Big data or cloud computing can be presented as alternatives to the construction of spatial data for the effective use of large volumes of data. In this paper, the process of building HLM (Hyper Live Map) using multi-source data to acquire stereo CCTV and other various data is presented and a big data service architecture design is proposed for the use of flexible and scalable cloud computing to handle big data created by users through such media as social network services and black boxes. The provision of spatial data services in real time using big data and cloud computing will enable us to implement navigation systems, vehicle augmented reality, real-time 3D spatial information, and single picture based positioning above the single GPS level using low-cost image-based position recognition technology in the future. Furthermore, Big Data and Cloud Computing are also used for data collection and provision in U-City and Smart-City environment as well, and the big data service architecture will provide users with information in real time.

• Journal of Korea Spatial Information System Society
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• v.7 no.1 s.13
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• pp.25-37
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• 2005

Recently, with the development of wireless communications and mobile computing, interest about mobile computing is rising. Mobile computing can be regarded as an environment where a user carries mobile devices, such as a PDA or a notebook, and shares resources with a server computer via wireless communications. A mobile database refers to a database which is used in these mobile devices. The mobile database can be used in the fields of insurance business, banking business, medical treatment, and so on. Especially, LBS(Location Based Service) which utilizes location information of users becomes an essential field of mobile computing. In order to support LBS in the mobile environment, there must be an Embedded Spatial MMDBMS(Main-Memory Database Management System) that can efficiently manage large spatial data in spatial mobile devices. Therefore, in this paper, we designed and implemented the Embedded Spatial MMDBMS, extended from the HSQLDB which is an existing MMDBMS for PC, to manage spatial data efficiently in spatial mobile devices. The Embedded Spatial MMDBMS adopted the spatial data model proposed by ISO(International Organization for Standardization), provided the arithmetic coding method that is suitable for spatial data, and supported the efficient spatial index which uses the MBR compression and hashing method suitable for spatial mobile devices. In addition, the system offered the spatial data display capability in low-performance processors of spatial mobile devices and supported the data caching and synchronization capability for performance improvement of spatial data import/export between the Embedded Spatial MMDBMS and the GIS server.

• Korean Journal of Remote Sensing
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• v.28 no.3
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• pp.337-346
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• 2012

In the view from most application system developers and users, cloud computing becomes popular in recent years and is still evolving. But in fact it is not easy to reach at the level of actual operations. Despite, it is known that the cloud in the practical stage provides a new pattern for deploying a geo-spatial application. However, domestically geo-spatial application implementation and operation based on this concept or scheme is on the beginning stage. It is the motivation of this works. Although this study is an introductory level, a simple and practical processed result was presented. This study was carried out on Amazon web services platform, as infrastructure as a service in the geo-spatial areas. Under this environment, cloud instance, a web and mobile system being previously implemented in the multi-layered structure for geo-spatial open sources of database and application server, was generated. Judging from this example, it is highly possible that cloud services with the functions of geo-processing service and large volume data handling are the crucial point, leading a new business model for civilian remote sensing application and geo-spatial enterprise industry. The further works to extend geo-spatial applications in cloud computing paradigm are left.

• 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.

• Spatial Information Research
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• v.22 no.5
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• pp.1-8
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• 2014

Globally, mobile, cloud computing or big data are the recent marketable key terms. These trend technologies or paradigm in the ICT (Information Communication Technology) fields exert large influence on the most application fields including geo-spatial applications. Among them, cloud computing, though the early stage in Korea now, plays a important role as a platform for other trend technologies uses. Especially, mobile cloud, an integrated platform with mobile device and cloud computing can be considered as a good solution to overcome well known limitations of mobile applications and to provide more information processing functionalities to mobile users. This work is a case study to design and implement the mobile application system for geo-spatial image filtering processing operated on mobile cloud platform built using OpenStack and various open sources. Filtering processing is carried out using R environment, recently being recognized as one of big data analysis technologies. This approach is expected to be an element linking geo-spatial information for new service model development and the geo-spatial analysis service development using R.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.354-356
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• 2003

The study of pedestrian behavior covers wide topics, including way finding, choice and decision make, as well as spatial cognition and environmental perception. To address the problem, simulation is now put forward as suitable technique and method for analyzing human spatial behavior. In the paper we present a development architecture for simulating tracking pedestrian in a distributed environment. We introduce and explore the potential of using mobile agent-enabled distributed implementation model as a tool for development and implementation of the simulation. Three kinds of mobile agents are designed for implementation of managing and querying data of pedestrian. Finally, simulation result of JR 10,000 passengers’ movement is developed and implemented as a case study.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.9-16
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• 2009

In ubiquitous spatial computing environments, GeoSensor generates sensor data streams including spatial information as well as various conventional sensor data from RFID, WSN, Web CAM, Digital Camera, CCTV, and Telematics units. This GeoSensor enables the revitalization of various ubiquitous USN technologies and services on geographic information. In order to service the u-GIS applications based on GeoSensors, it is indispensable to efficiently process sensor data streams from GeoSensors of a wide area. In this paper, we propose a GeoSensor data stream processing system for u-GIS computing over real-time stream data from GeoSensors with geographic information. The proposed system provides efficient gathering, storing, and continuous query processing of GeoSensor data stream, and also makes it possible to develop diverse u-GIS applications meet each user requirements effectively.