• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.239-245
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• 2000

This study is aimed to develop the historical data management system named "ACON"(Actual COst Network system) which can be used for collecting, analysis and modification of the historical data. ACON_Local is used for collecting the historical data at each spot place. It redueces the work potential of managers, spending time and cost. ACON_Analysis is to analyze the collection , modifies the data′s error and updates the historical database. ACON_Network is developed to transfer data from ACON to Web server or opposites. The proposed model, ACON, provide the easy way for the cost estimate system, "HiCOMS" to collect, analyze and modify the historical cost evaluated data, which is used to compose the historical data base.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1141-1155
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• 2019

Today, most approaches used in the recommendation system provide correct data prediction similar to the data that users need. The method that researchers are paying attention and apply as a model in the recommendation system is the communities' detection in the big social network. The outputted result of this approach is effective in improving the exactness. Therefore, in this paper, the personalized movie recommendation system that combines data mining for the k-clique method is proposed as the best exactness data to the users. The proposed approach was compared with the existing approaches like k-clique, collaborative filtering, and collaborative filtering using k-nearest neighbor. The outputted result guarantees that the proposed method gives significant exactness data compared to the existing approach. In the experiment, the MovieLens data were used as practice and test data.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.119-126
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• 2024

The volume of genomic data is constantly increasing in various modern industries and research fields. This growth presents new challenges and opportunities in terms of the quantity and diversity of genetic data. In this paper, we propose a distributed cloud system for integrating and managing large-scale gene databases. By introducing a distributed data storage and processing system based on the Hadoop Distributed File System (HDFS), various formats and sizes of genomic data can be efficiently integrated. Furthermore, by leveraging Spark on YARN, efficient management of distributed cloud computing tasks and optimal resource allocation are achieved. This establishes a foundation for the rapid processing and analysis of large-scale genomic data. Additionally, by utilizing BigQuery ML, machine learning models are developed to support genetic search and prediction, enabling researchers to more effectively utilize data. It is expected that this will contribute to driving innovative advancements in genetic research and applications.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1689-1694
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• 2011

There are increasing customer's needs for safe operation and comfort of trains for the passengers as trains are getting fast and developed with advanced components. To meet the needs we developed the system which is able to prevent possible risk elements by transverse vibrations during the train operation. System records the amplitude data of trains vibration and location data from GPS antenna in real time. The system is composed of 5 different functioning modules as following GCPU, GGWD, GIFD, GAID and GPSD. GAID module is detecting amplitude value of vibration which is sensed by bogie hunting sensor's. GFID gets location information where vibration data is detected with GPS Antenna. Once those data is prepared, GCPU testify it's amplitude and decides whether it send those data. When it sends those recorded data, those data are sent to control unit by MVB communication. we expect that this information will help operation and maintenance engineers improve the safety train services. This paper explains train transverse Vibrations data recording system and its major functions which can measure the amplitude of trains's data during the operation and introduces the configuration for the system setup.

• Spatial Information Research
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• v.12 no.1
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• pp.1-12
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• 2004

The 3-dimensional GIS data provider system manages and retrieves 2, 3-dimensional spatial and time data. This system provides the standard interfaces which help developers to omit additional works to do modify or convert a format of 2 or 3-dimensional spatial data, which has been already accumulated, into that dependent on a specified system. This system consists of a data provider and data access component. The former deals with the connections with some databases and manages the meta data, so that the various GIS or software can access to 3-dimensional spatial and time data via the same method, and the latter takes charge of index management and spatial operations on GIS data for consumers. The system offers the diverse spatial operations and analysis functions for 3-dimensional GIS.

• Journal of the Korean Institute of Landscape Architecture
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• v.21 no.3
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• pp.89-98
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• 1993

It is necessary for the park tree manager to have the current information about the status of trees, which can help him with right decisions. However, there are many problems in the existing management method such as huge amount of data, tedious work, and the difficult update work due to the lack of necessary data or the inappropriate data record and management method. The sole use of database management system(DBMS) cannot slove these problems because it cannot handle graphic data based on the locational information. So, it is imperative for the park manager to have locational data as well as attribute data of the park tree concerned. Therefore, the purpose of this study is to develop the personal computer-based, user friendly park tree management information system, which deals with attribute data(DBMS) and graphic data(using the CAD) together within the integrated environment. The park tree management information system developed in this study provides a complete operating environment for data input, update, query, delete, and retrieve. The major advantages of this system are as follows: 1) To search the location and distribution of trees. 2) To record, store, and manage data easily. 3) When the manager is changed, delivery of the park tree work is convenient. 4) The system can help the manager with the correct information for the efficient park tree management.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.164-167
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• 2009

The amount of acoustic data gathered from the acoustic data gathering system is increased dramatically as the acoustic data gathering system become various and highly effective. It is needed to transmit this acoustic data to analysis environment for precise analysis. In this gathering/analysis system, it is also needed the stable transmitting as well as highly perfect security. In this paper, I would like to propose a transmitting and management system sending a massive gathering acoustic data in the unsecurity and low speed networking environment. The implemented system is to transmit the acoustic data safely in low speed networking environment and secure the acoustic data from various threats.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.88-95
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• 2022

Detecting anomalies in human movement is an important task in industrial applications, such as monitoring industrial disasters or accidents and recognizing unauthorized factory intruders. In this paper, we propose an abnormal worker movement detection system based on data stream processing and hierarchical clustering. In the proposed system, Apache Spark is used for streaming the location data of people. A hierarchical clustering-based anomalous trajectory detection algorithm is designed for detecting anomalies in human movement. The algorithm is integrated into Apache Spark for detecting anomalies from location data. Specifically, the location information is streamed to Apache Spark using the message queuing telemetry transport protocol. Then, Apache Spark processes and stores location data in a data frame. When there is a request from a client, the processed data in the data frame is taken and put into the proposed algorithm for detecting anomalies. A real mobility trace of people is used to evaluate the proposed system. The obtained results show that the system has high performance and can be used for a wide range of industrial applications.

• Genomics & Informatics
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• v.4 no.1
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• pp.40-44
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• 2006

Toxicogenomics combines transcriptome, proteome and metabolome profiling with conventional toxicology to investigate the interaction between biological molecules and toxicant or environmental stress in disease caution. Toxicogenomics faces the problems of comparison and integration across different sources of data. Cause of unusual characteristics of toxicogenomic data, researcher should be assisted by data analysis and annotation for getting meaningful information. There are already existing repositories which claim to stand for toxicogenomics database. However, those just contain limited abilities for toxicogenomic research. For supporting toxicologist who comes up against toxicogenomic data flood, now we propose novel toxicogenomics knowledgebase system, XPERANTO-TOX. XPERANTO-TOX is an integrated system for toxicogenomic data management and analysis. It is composed of three distinct but closely connected parts. Firstly, Data Storage System is for reposit many kinds of '-omics' data and conventional toxicology data. Secondly, Data Analysis System consists of analytical modules for integrated toxicogenomics data. At last, Data Annotation System is for giving extensive insight of data to researcher.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1785-1792
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• 2017

Generally, big data contains sensitive information about individuals, and thus directly releasing it for public use may violate existing privacy requirements. Therefore, privacy-preserving data publishing (PPDP) has been actively researched to share big data containing personal information for public use, while protecting the privacy of individuals with minimal data modification. Recently, with increasing demand for big data sharing in various area, there is also a growing interest in the development of software which supports a privacy-preserving data publishing. Thus, in this paper, we develops the system which aims to effectively and efficiently support privacy-preserving data publishing. In particular, the system developed in this paper enables data owners to select the appropriate anonymization level by providing them the information loss matrix. Furthermore, the developed system is able to achieve a high performance in data anonymization by using distributed Hadoop clusters.