• Journal of Distribution Science
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• v.19 no.3
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• pp.49-60
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• 2021

Purpose: This study aims to utilize geographic data to analyze how various retail formats of large-scale stores around the traditional market affect the performance of the traditional market in Seoul, Korea. Research design, data, and methodology: The two types of catchment areas were demarcated (circle of 1km radius and Thiessen polygon) for each traditional market, and the large-scale stores located within each catchment area were identified for 153 traditional markets in Seoul, Korea. Additionally, multiple regression analysis was utilized. Results: The results revealed that the influence on the performance of the traditional markets were different depending on the retail format of the large-scale stores. Large discount stores were found to have a negative effect on the sales and the visitors of traditional markets, whereas complex shopping malls and department stores had a positive effect on the traditional markets. Conclusions: As a result of the differences in the retail format such as product categories and leisure functions, the impact of some large-scale stores on the traditional market may have a greater agglomeration effect than the consumer churn effect. Therefore, it is suggested that in the regulation of these large-scale stores, the differences in retail format should be considered for the future.

• Journal of KIISE:Information Networking
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• v.37 no.3
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• pp.217-227
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• 2010

To apply active RFID technology in the various kinds of industry, it needs to quickly transmit a large amount of data. ISO/IEC 18000-7 standard uses the 433.92MHz as single channel system and its transmit rate is just 27.8kbps, that is insufficient for a large amount of data transmission. To solve this problem, we designed a new data transmission protocol using 2.4GHz band. The feature of designed protocol is not only making over 255bytes data messages using the Burst Read UDB but also efficiently transmitting it. To implement this protocol, we use Texas Instruments's SmartRF04 develop kit and CC2500 transceiver as RF module. As an evaluation of 63.75kbytes data transmission, we demonstrate that transmission time of Burst Read UDB has improved as 17.95% faster than that of Read UDB in the ISO/IEC 18000-7.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.3 s.30
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• pp.61-67
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• 2004

Satellite and aerial images are effectively used to monitor ecological and environmental situation. More and more officials in the Ministry of Environment thus need to utilize these image data for various administrative affairs. However, it is difficult not only to deliver to the officials these image data mostly of large capacity through network but also for them to actively use the delivered data without specialized knowledge in remote sensing and image processing. Therefore, we established a large rapacity image data service system employing image compressive transmission and web-based image processing techniques. This system allows the officials to rapidly access all the associated image data and conveniently utilize the data using various functions implemented for remote sensing, image processing, GIS operations. Consequently, this system have been actively utilized for the decision making processes of the officials and hence accomplished a great reduction in the resources required for the data analysis for various administrative affairs.

• Journal of the Korean Society for information Management
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• v.40 no.3
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• pp.77-98
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• 2023

Large Language Models (LLMs) are becoming the major trend in the natural language processing field. These models were built based on research data, but information such as types, limitations, and risks of using research data are unknown. This research would present how to analyze and evaluate the LLMs that were built with research data: LLaMA or LLaMA base models such as Alpaca of Stanford, Vicuna of the large model systems organization, and ChatGPT from OpenAI from the perspective of research data. This quality evaluation focuses on the validity, functionality, and reliability of Data Quality Management (DQM). Furthermore, we adopted the Holistic Evaluation of Language Models (HELM) to understand its evaluation criteria and then discussed its limitations. This study presents quality evaluation criteria for LLMs using research data and future development directions.

• Journal of Advanced Research in Ocean Engineering
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• v.3 no.1
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• pp.32-40
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• 2017

Recently, the amount of data to be processed and the complexity thereof have been increasing due to the development of information and communication technology, and industry's interest in such big data is increasing day by day. In the shipbuilding and offshore industry also, there is growing interest in the effective utilization of data, since various and vast amounts of data are being generated in the process of design, production, and operation. In order to effectively utilize big data in the shipbuilding and offshore industry, it is necessary to store and process large amounts of data. In this study, it was considered efficient to apply Hadoop and R, which are mostly used in big data related research. Hadoop is a framework for storing and processing big data. It provides the Hadoop Distributed File System (HDFS) for storing big data, and the MapReduce function for processing. Meanwhile, R provides various data analysis techniques through the language and environment for statistical calculation and graphics. While Hadoop makes it is easy to handle big data, it is difficult to finely process data; and although R has advanced analysis capability, it is difficult to use to process large data. This study proposes a big data platform based on Hadoop for applications in the shipbuilding and offshore industry. The proposed platform includes the existing data of the shipyard, and makes it possible to manage and process the data. To check the applicability of the platform, it is applied to estimate the weights of offshore structure topsides. In this study, we store data of existing FPSOs in Hadoop-based Hortonworks Data Platform (HDP), and perform regression analysis using RHadoop. We evaluate the effectiveness of large data processing by RHadoop by comparing the results of regression analysis and the processing time, with the results of using the conventional weight estimation program.

• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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• pp.529-538
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• 2005

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are association rules, decision tree, clustering, neural network and so on. Association rule mining searches for interesting relationships among items in a riven large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. We analyze Gyeongnam social indicator survey data using association rule technique for environmental information discovery. We can use to environmental preservation and environmental improvement by association rule outputs.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.2
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• pp.19-27
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• 2010

Historical archived traffic data management system enables a long term time-series analysis and provides data necessary to acquire the constantly changing traffic conditions and to evaluate and analyze various traffic related strategies and policies. Such features are provided by maintaining highly reliable traffic data through scientific and systematic management. Now, the management systems for massive traffic data have a several problems such as, the storing and management methods of a large volume of archive data. In this paper, we describe how to storing and management for the massive traffic data and, we propose methodology for logical and physical architecture, collecting and storing, database design and implementation, process design of massive traffic data.

• Journal of the Korean Data and Information Science Society
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• v.16 no.1
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• pp.59-69
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• 2005

Data mining is the method to find useful information for large amounts of data in database It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. We analyze Gyeongnam social indicator survey data by 2001 using association rule technique for environment information. Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. We can use to environmental preservation and environmental improvement by association rule outputs

• International Journal of Contents
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• v.17 no.1
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• pp.61-70
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• 2021

As the resolution of climate change scenario data applied with regional models increased, Earth System Grid Federation (ESGF) was established around major climate-related organizations to jointly operated and manage large-scale climate data. ESGF developed standard software to provide model output, observation data management, dissemination, and analysis using Peer to Peer (P2P) computing technology. Roles of each institution were divided into index and data nodes. Therefore, ESGF data node was established at APEC Climate Center in Korea on behalf of Asia to share data on climate change scenarios of CORDEX-East Asia (CORDEX-EA) to study climate changes in Eastern Asia. Climate researchers are expected to play a large role in researching causes of global warming and responding to climate change by providing CORDEX-EA regional model data to the world through ESGF data node.

• Journal of Platform Technology
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• v.9 no.1
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• pp.15-22
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• 2021

In a smart manufacturing environment, more and more devices are connected to the Internet so that a large volume of data can be obtained during all phases of the product life cycle. The large-scale industries, companies and organizations that have more operational units scattered among the various geographical locations face a huge resource consumption because of their unorganized structure of sharing resources among themselves that directly affects the supply chain of the corresponding concerns. Cloud-based smart manufacturing paradigm facilitates a new variety of applications and services to analyze a large volume of data and enable large-scale manufacturing collaboration. The manufacturing units include machinery that may be situated in different geological areas and process instances that are executed from different machinery data should be constantly managed by the super admin to coordinate the manufacturing process in the large-scale industries these environments make the manufacturing process a tedious work to maintain the efficiency of the production unit. The data from all these instances should be monitored to maintain the integrity of the manufacturing service system, all these data are computed in the cloud environment which leads to the latency in the performance of the smart manufacturing service system. Instead, validating data from the external device, we propose to validate the data at the front-end of each device. The validation process can be automated by script validation and then the processed data will be sent to the cloud processing and storing unit. Along with the end-device data validation we will implement the APM(Asset Performance Management) to enhance the productive functionality of the manufacturers. The manufacturing service system will be chunked into modules based on the functionalities of the machines and process instances corresponding to the time schedules of the respective machines. On breaking the whole system into chunks of modules and further divisions as required we can reduce the data loss or data mismatch due to the processing of data from the instances that may be down for maintenance or malfunction ties of the machinery. This will help the admin to trace the individual domains of the smart manufacturing service system that needs attention for error recovery among the various process instances from different machines that operate on the various conditions. This helps in reducing the latency, which in turn increases the efficiency of the whole system