• Title/Summary/Keyword: research data platform

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Developing the Core Modules of for Viz-Platform for Supporting Public Service in the City (도시의 공공서비스 제공을 위한 시각화 플랫폼의 핵심모듈 개발)

  • Kim, Mi-Yun
    • Journal of Korea Multimedia Society
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    • v.18 no.9
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    • pp.1131-1139
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    • 2015
  • The purpose of this research is creating the visualization platform of the user interface to make able to be provide the demanded service while users communication with surrounding space in a smart city environment. This comes from the latest enhanced interface technology and social media, and it uses the shares information to support the proper interface environment according to a life style and spatial properties. "EzCity" which is the user interface platform suggested in this research, can control the enormous amount of public data for the smart city. The core module of user platform is made up with Public data module, Interface module, Visualization module and Service module. The role of this platform is to be provide "Geo-Intelligent Interface Service" for space users to access the data in easier and more practical interface environment. This reinforces the visualization process for data collecting, systematization, visualization and providing service. Also this will be expected to be the base to solve the problem which complexity and rapidly increasing amount of data.

CDOWatcher: Systematic, Data-driven Platform for Early Detection of Contagious Diseases Outbreaks

  • Albarrak, Abdullah M.
    • International Journal of Computer Science & Network Security
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    • v.22 no.11
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    • pp.77-86
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    • 2022
  • The destructive impact of contagious diseases outbreaks on all life facets necessitates developing effective solutions to control these diseases outbreaks. This research proposes an end-to-end, data-driven platform which consists of multiple modules that are working in harmony to achieve a concrete goal: early detection of contagious diseases outbreaks (i.e., epidemic diseases detection). Achieving that goal enables decision makers and people in power to act promptly, resulting in robust prevention management of contagious diseases. It must be clear that the goal of this proposed platform is not to predict or forecast the spread of contagious diseases, rather, its goal is to promptly detect contagious diseases outbreaks as they happen. The front end of the proposed platform is a web-based dashboard that visualizes diseases outbreaks in real-time on a real map. These outbreaks are detected via another component of the platform which utilizes data mining techniques and algorithms on gathered datasets. Those gathered datasets are managed by yet another component. Specifically, a mobile application will be the main source of data to the platform. Being a vital component of the platform, the datasets are managed by a DBMS that is specifically tailored for this platform. Preliminary results are presented to showcase the performance of a prototype of the proposed platform.

Development of Evaluation Perspective and Criteria for the DataON Platform

  • Kim, Suntae
    • Journal of Information Science Theory and Practice
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    • v.8 no.2
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    • pp.68-78
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    • 2020
  • This study is a preliminary study to develop an evaluation framework necessary for evaluating the DataON platform. The first objective is to examine expert perceptions of the level of DataON platform construction. The second objective is to evaluate the importance, stability, and usability of DataON platform features over OpenAIRE features. The third objective is to derive weights from the evaluation perspective for future DataON platform evaluation. The fourth objective is to examine the preferences of experts in each evaluation perspective and to derive unbiased evaluation criteria. This study used a survey method for potential stakeholders of the DataON platform. The survey included 12 professionals with at least 10 years of experience in the field. The 57 overall functions and services were measured at 3.1 out of 5 for importance. Stability was -0.07 point and usability was measured as -0.05 point. The 42 features and services scored 3.04 points in importance. Stability was -0.58 points and usability was -0.51 points. In particular, the stability and usability scores of the 42 functions and services provided as of 2018 were higher than the total functions were, which is attributed to the stable and user-friendly improvement after development. In terms of the weight of the evaluation point, the collection quality has the highest weight of 27%. Interface usability is then weighted 22%. Subsequently, service quality is weighted 19%, and finally system performance efficiency and user feedback solicitation are equally weighted 16%.

Big Data Platform Based on Hadoop and Application to Weight Estimation of FPSO Topside

  • Kim, Seong-Hoon;Roh, Myung-Il;Kim, Ki-Su;Oh, Min-Jae
    • 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.

An Evaluation of the Suitability of Data Mining Algorithms for Smart-Home Intelligent-Service Platforms (스마트홈 지능형 서비스 플랫폼을 위한 데이터 마이닝 기법에 대한 적합도 평가)

  • Kim, Kilhwan;Keum, Changsup;Chung, Ki-Sook
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.40 no.2
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    • pp.68-77
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    • 2017
  • In order to implement the smart home environment, we need an intelligence service platform that learns the user's life style and behavioral patterns, and recommends appropriate services to the user. The intelligence service platform should embed a couple of effective and efficient data mining algorithms for learning from the data that is gathered from the smart home environment. In this study, we evaluate the suitability of data mining algorithms for smart home intelligent service platforms. In order to do this, we first develop an intelligent service scenario for smart home environment, which is utilized to derive functional and technical requirements for data mining algorithms that is equipped in the smart home intelligent service platform. We then evaluate the suitability of several data mining algorithms by employing the analytic hierarchy process technique. Applying the analytical hierarchy process technique, we first score the importance of functional and technical requirements through a hierarchical structure of pairwise comparisons made by experts, and then assess the suitability of data mining algorithms for each functional and technical requirements. There are several studies for smart home service and platforms, but most of the study have focused on a certain smart home service or a certain service platform implementation. In this study, we focus on the general requirements and suitability of data mining algorithms themselves that are equipped in smart home intelligent service platform. As a result, we provide a general guideline to choose appropriate data mining techniques when building a smart home intelligent service platform.

Fishery R&D Big Data Platform and Metadata Management Strategy (수산과학 빅데이터 플랫폼 구축과 메타 데이터 관리방안)

  • Kim, Jae-Sung;Choi, Youngjin;Han, Myeong-Soo;Hwang, Jae-Dong;Cho, Wan-Sup
    • The Journal of Bigdata
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    • v.4 no.2
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    • pp.93-103
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    • 2019
  • In this paper, we introduce a big data platform and a metadata management technique for fishery science R & D information. The big data platform collects and integrates various types of fisheries science R & D information and suggests how to build it in the form of a data lake. In addition to existing data collected and accumulated in the field of fisheries science, we also propose to build a big data platform that supports diverse analysis by collecting unstructured big data such as satellite image data, research reports, and research data. Next, by collecting and managing metadata during data extraction, preprocessing and storage, systematic management of fisheries science big data is possible. By establishing metadata in a standard form along with the construction of a big data platform, it is meaningful to suggest a systematic and continuous big data management method throughout the data lifecycle such as data collection, storage, utilization and distribution.

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A Data-driven Approach for Computational Simulation: Trend, Requirement and Technology

  • Lee, Sunghee;Ahn, Sunil;Joo, Wonkyun;Yang, Myungseok;Yu, Eunji
    • Journal of Internet Computing and Services
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    • v.19 no.1
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    • pp.123-130
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    • 2018
  • With the emergence of a new paradigm called Open Science and Big Data, the need for data sharing and collaboration is also emerging in the computational science field. This paper, we analyzed data-driven research cases for computational science by field; material design, bioinformatics, high energy physics. We also studied the characteristics of the computational science data and the data management issues. To manage computational science data effectively it is required to have data quality management, increased data reliability, flexibility to support a variety of data types, and tools for analysis and linkage to the computing infrastructure. In addition, we analyzed trends of platform technology for efficient sharing and management of computational science data. The main contribution of this paper is to review the various computational science data repositories and related platform technologies to analyze the characteristics of computational science data and the problems of data management, and to present design considerations for building a future computational science data platform.

Development of Bioinformatics Capacity in Support of the KOICA-UPLB-IRRI Agricultural Genomics Research Center

  • Ramil P. Mauleon;Lord Hendrix Barboza;Frances Nikki Borja;Dmytro Chebotarov;Jeffrey Detras;Venice Juanillas;Riza Pasco;Kenneth L. McNally
    • Proceedings of the Korean Society of Crop Science Conference
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    • 2022.10a
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    • pp.34-34
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    • 2022
  • Capacity building for bioinformatics could be achieved with the systematic training of research staff and higher degree students in the current best practices for analysis of data from 'omic-type experiments. It is anticipated that the KOICA-University of the Philippines Los Baños - International Rice Research Insitute Agricultural Genomics Research Center activities will focus on the use of next generation sequencing technology for genome sequencing and annotation, genome variant discovery for use in GWAS and QTL mapping, and transcriptome analysis of organisms important to agriculture and food security. Such activities require that researchers have high levels of knowledge and skills in bioinformatics in order to gain insights from the results of the experiments performed. In this talk the bioinformatic tools/solutions and online training materials already available will be presented, as well the upcoming resources under development in support of the project.

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Development of a Real-Time Active Safety Management Platform and Data Collection Device for the Safety of Radiation Workers (방사선 작업종사자 안전을 위한 실시간 능동형 안전관리 플랫폼과 데이터 수집 디바이스 개발 연구)

  • Kilsoon Park;Kihun Bae;Yongkwon Kim;Won Ki Seo
    • Journal of Radiation Industry
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    • v.18 no.3
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    • pp.209-215
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    • 2024
  • Radiation work always carries the risk of radiation exposure, so regulatory agencies manage it through licensing when high exposure is expected. However, due to passive management methods using TLD, etc., there are cases where risk management is done after an incident occurs or the incident is covered up. In this study, we developed a system to manage the location of radiation work and the risk of workers in real time through a safety management platform and a location-based personal dosimeter. The safety platform server receives data from the developed personal dosimeter in real time and manages risks in three steps for each worker using location and dose rate, and can predict risks and generate alarms in real time. The personal dosimeter transmits the location and dose rate of the worker in real time using GPS and LTE communication. The developed safety management platform and personal dosimeter were verified through a field test to receive real-time data of the location and dose rate data of the worker, and the risk management function according to the individual dose rate was verified.

A Study on Big Data Platform Based on Hadoop for the Applications in Ship and Offshore Industry (조선 해양 산업에서의 응용을 위한 하둡 기반의 빅데이터 플랫폼 연구)

  • Kim, Seong-Hoon;Roh, Myung-Il;Kim, Ki-Su
    • Korean Journal of Computational Design and Engineering
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    • v.21 no.3
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    • pp.334-340
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    • 2016
  • As Information Technology (IT) is developed constantly, big data is becoming important in various industries, including ship and offshore industry where a lot of data are being generated. However, it is difficult to apply big data to ship and offshore industry because there is no generalized platform for its application. Therefore, this study presents a big data platform based on the Hadoop for applications in ship and offshore industry. The Hadoop is one of the most popular big data technologies. The presented platform includes existing data of shipyard and is possible to manage and process the data. To check the applicability of the platform, it is applied to estimate the weight of offshore plant topsides. The result shows that the platform can be one of alternatives to use effectively big data in ship and offshore industry.