• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.137-147
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• 2022

Diagnosis and management of skin condition is a very basic and important function in performing its role for workers in the beauty industry and cosmetics industry. For accurate skin condition diagnosis and management, it is necessary to understand the skin condition and needs of customers. In this paper, we developed SCIS, a big data-based skin care information system that supports skin condition diagnosis and management using social media big data for skin condition diagnosis and management. By using the developed system, it is possible to analyze and extract core information for skin condition diagnosis and management based on text information. The skin care information system SCIS developed in this paper consists of big data collection stage, text preprocessing stage, image preprocessing stage, and text word analysis stage. SCIS collected big data necessary for skin diagnosis and management, and extracted key words and topics from text information through simple frequency analysis, relative frequency analysis, co-occurrence analysis, and correlation analysis of key words. In addition, by analyzing the extracted key words and information and performing various visualization processes such as scatter plot, NetworkX, t-SNE, and clustering, it can be used efficiently in diagnosing and managing skin conditions.

• Journal of Digital Convergence
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• v.13 no.2
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• pp.43-50
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• 2015

Since the popularity of social network services (SNS) rise, the data produced from them is rapidly increased. The SNS data includes personal propensity or interest and propagates rapidly so there are many requests on analyzing the data for applying the analytic results to various fields. New technologies and services for processing and analyzing big data in the real-time are introduced but it is hard to apply them in a short time and low coast. In this paper, an efficient method to build a tweet analysis system without inducing new technologies or service platforms for handling big data is proposed. The proposed method was verified through building a prototype monitoring system to collect and analyze tweets using the MySQL database and the PHP scripts.

• The Journal of Industrial Distribution & Business
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• v.11 no.12
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• pp.7-15
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• 2020

Purpose: This study aims to explore the feasibility of expanding complex wards to provide efficient hospital management and high-quality medical services to local residents of Gangneung Medical Center (GMC). Research Design, Data and Methodology: There are four research designs to achieve the research objectives. We analyzed Big Data for 3 months on Social Network Services (SNS). A questionnaire survey conducted on 219 patients visiting the GMC. Surveys of 20 employees of the GMC applied. The feasibility to expand the GMC ward measured through Focus Group Interview by 12 internal and external experts. Data analysis methods derived from various surveys applied with data mining technique, frequency analysis, and Importance-Performance Analysis methods, and IBM SPSS statistical package program applied for data processing. Results: In the result of the big data analysis, the GMC's recognition on SNS is high. 95.9% of the residents and 100.0% of the employees required the need for the complex ward extension. In the analysis of expert opinion, in the future functions of GMC, specialized care (△3.3) and public medicine (△1.4) increased significantly. Conclusion: GMC's complex ward extension is an urgent and indispensable project to provide efficient hospital management and service quality.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.107-115
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• 2022

Recently, big data applications need another database different from the Relation database. NoSQL databases are used to save and handle massive amounts of data. NoSQL databases have many advantages over traditional databases like flexibility, efficiently processing data, scalability, and dynamic schemas. Most of the current applications are based on the web, and the size of data is in increasing. NoSQL databases are expected to be used on a more and large scale in the future. However, NoSQL suffers from many security issues, and one of them is access control. Many recent applications need Fine-Grained Access control (FGAC). The integration of the NoSQL databases with FGAC will increase their usability in various fields. It will offer customized data protection levels and enhance security in NoSQL databases. There are different NoSQL database models, and a document-based database is one type of them. In this research, we choose the CouchDB NoSQL document database and develop an access control mechanism that works at a fain-grained level. The proposed mechanism uses role-based access control of CouchDB and restricts read access to work at the document level. The experiment shows that our mechanism effectively works at the document level in CouchDB with good execution time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.2-5
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• 2021

With the recent development of the Internet of Things (IoT) technology, various terminals are being connected to the Internet. As a result, the amount of IoT data is also increasing, and an index key that can efficient analyze the large-scale IoT data is proposed. Existing index keys have only time and space information, so if data stored in index tables and instance tables were queried using repetition or join operation, IoT data was embedded in the index key of the proposal to create OLAP-based big tables to minimize the number of repetitions or join times.

• Journal of the Korean Society for Heat Treatment
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• v.36 no.6
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• pp.374-381
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• 2023

This study develops and evaluates a deep learning model for predicting oxide and nitride layers based on plasma process data. We introduce a novel deep learning-based Varying Coefficient Regressor (VCR) by adapting the VCR, which previously relied on an existing unique function. This model is employed to forecast the oxide and nitride layers within the plasma. Through comparative experiments, the proposed VCR-based model exhibits superior performance compared to Long Short-Term Memory, Random Forest, and other methods, showcasing its excellence in predicting time series data. This study indicates the potential for advancing prediction models through deep learning in the domain of plasma processing and highlights its application prospects in industrial settings.

• Annual Conference of KIPS
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• 2019.10a
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• pp.612-614
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• 2019

본 논문은 손실이 큰 IoT 빅데이터 처리 고속화 SW의 Queue를 In-memory DataBase로 이용하여 전처리 프레임워크 기술에 대하여 제안하였다.

• Annual Conference of KIPS
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• 2013.05a
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• pp.179-182
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• 2013

We develop Framework architecture of Online Shopping Services based on M2M and IoT for Handheld Devices in Cloud Computing. MapReduce model will be used as a method to simplify large scale data processing when user search for purchasing products online which provide efficient, and fast respond time. Therefore, providing user with a enhanced Quality of Experience (QoE) as well as Quality of Service (QoS) when purchasing/searching products Online from big data.

• Annual Conference of KIPS
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• 2013.11a
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• pp.1596-1598
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• 2013

2000년대 초반 등장한 시맨틱 웹 기술은 최근 재조명을 받고 있다. 이는 초기에 구축된 시맨틱 데이터와 최근에 구축하는 시맨틱 데이터의 양적 비교를 통해서도 알 수 있다. 그러나 기존의 시맨틱웹 기술은 대용량 데이터를 처리하는데 어려움이 많아, 이를 처리하기 위한 기술이 중요한 문제로 대두되고 있다. 본 논문에서는 앞에서 말한 바와 같이, 기존 RDF Repository의 대안으로, 다양한 데이터 베이스를 복합적으로 사용하였다. RDF 데이터를 효율적으로 처리하기 위해, NoSQL DB와 메모리 기반 관계형 DB를 활용하여 시스템을 구성하였다. 또한, 사용자가 이에 대한 별도의 지식 없이 기존의 SPARQL 질의를 그대로 사용하여, 원하는 결과를 얻을 수 있는 시스템을 제안한다.

• Food Science of Animal Resources
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• v.43 no.2
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• pp.374-381
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• 2023

In a previous study, 'response surface methodology (RSM) using a fullest balanced model' was proposed to improve the optimization of food processing when a standard second-order model has a significant lack of fit. However, that methodology can be used when each factor of the experimental design has five levels. In response surface experiments for optimization, not only five-level designs, but also three-level designs are used. Therefore, the present study aimed to improve the optimization of food processing when the experimental factors have three levels through a new approach to RSM. This approach employs three-step modeling based on a second-order model, a balanced higher-order model, and a balanced highest-order model. The dataset from the experimental data in a three-level, two-factor central composite design in a previous research was used to illustrate three-step modeling and the subsequent optimization. The proposed approach to RSM predicted improved results of optimization, which are different from the predicted optimization results in the previous research.