• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1301-1306
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• 2018

Artificial intelligence is one of the key technologies of the Fourth Industrial Revolution. This paper introduces the diverse kinds of approaches to subjects that tackle diverse kinds of research fields such as model-based MS approach, deep neural network model, image edge detection approach, cross-layer optimization model, LSSVM approach, screen design approach, CPU-GPU hybrid approach and so on. The research on Superintelligence and superconnection for IoT and big data is also described such as 'superintelligence-based systems and infrastructures', 'superconnection-based IoT and big data systems', 'analysis of IoT-based data and big data', 'infrastructure design for IoT and big data', 'artificial intelligence applications', and 'superconnection-based IoT devices'.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.716-722
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• 2016

In the recent rapidly changing IT environment, the amount of smart digital data is growing exponentially. As a result, in many areas, utilizing big data research and development services and related technologies is becoming more popular. In SMART learning, big data is used by students, teachers, parents, etc., from a perspective of the potential for many. In this paper, we describe big data and can utilize it to identify scenarios. Big data, obtained through customized learning services that can take advantage of the scheme, is proposed. To analyze educational big data processing technology for this purpose, we designed a system for big data processing. Education services offer the measures necessary to take advantage of educational big data. These measures were implemented on a test platform that operates in a cloud-based operations section for a pilot training program that can be applied properly. Teachers try using it directly, and in the interest of business and education, a survey was conducted based on enjoyment, the tools, and users' feelings (e.g., tense, worried, confident). We analyzed the results to lay the groundwork for educational use of big data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.54-63
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• 2016

As technology has developed and cost for data processing has reduced, big data market has grown bigger. Developed countries such as the United States have constantly invested in big data industry and achieved some remarkable results like improving advertisement effects and getting patents for customer service. Every company aims to achieve long-term survival and profit maximization, but it needs to establish a good strategy, considering current industrial conditions so that it can accomplish its goal in big data industry. However, since domestic big data industry is at its initial stage, local companies lack systematic method to establish competitive strategy. Therefore, this research aims to help local companies diagnose their big data capabilities through a reference model and big data capability assessment system. Big data reference model consists of five maturity levels such as Ad hoc, Repeatable, Defined, Managed and Optimizing and five key dimensions such as Organization, Resources, Infrastructure, People, and Analytics. Big data assessment system is planned based on the reference model's key factors. In the Organization area, there are 4 key diagnosis factors, big data leadership, big data strategy, analytical culture and data governance. In Resource area, there are 3 factors, data management, data integrity and data security/privacy. In Infrastructure area, there are 2 factors, big data platform and data management technology. In People area, there are 3 factors, training, big data skills and business-IT alignment. In Analytics area, there are 2 factors, data analysis and data visualization. These reference model and assessment system would be a useful guideline for local companies.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.645-657
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• 2021

Along with the rapid development of the economy, the urban scale has extended rapidly, leading to the formation of different types of urban function districts (UFDs), such as central business, residential and industrial districts. Recognizing the spatial distributions of these districts is of great significance to manage the evolving role of urban planning and further help in developing reliable urban planning programs. In this paper, we propose an automatic UFD division method based on big data analysis of point of interest (POI) data. Considering that the distribution of POI data is unbalanced in a geographic space, a dichotomy-based data retrieval method was used to improve the efficiency of the data crawling process. Further, a POI spatial feature analysis method based on the mean shift algorithm is proposed, where data points with similar attributive characteristics are clustered to form the function districts. The proposed method was thoroughly tested in an actual urban case scenario and the results show its superior performance. Further, the suitability of fit to practical situations reaches 88.4%, demonstrating a reasonable UFD division result.

• International Journal of Contents
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• v.15 no.3
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• pp.32-38
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• 2019

We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.109-122
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• 2016

Recently, big data analysis has developed into a field of interest to individuals and non-experts as well as companies and professionals. Accordingly, it is utilized for marketing and social problem solving by analyzing the data currently opened or collected directly. In Korea, various companies and individuals are challenging big data analysis, but it is difficult from the initial stage of analysis due to limitation of big data disclosure and collection difficulties. Nowadays, the system improvement for big data activation and big data disclosure services are variously carried out in Korea and abroad, and services for opening public data such as domestic government 3.0 (data.go.kr) are mainly implemented. In addition to the efforts made by the government, services that share data held by corporations or individuals are running, but it is difficult to find useful data because of the lack of shared data. In addition, big data traffic problems can occur because it is necessary to download and examine the entire data in order to grasp the attributes and simple information about the shared data. Therefore, We need for a new system for big data processing and utilization. First, big data pre-analysis technology is needed as a way to solve big data sharing problem. Pre-analysis is a concept proposed in this paper in order to solve the problem of sharing big data, and it means to provide users with the results generated by pre-analyzing the data in advance. Through preliminary analysis, it is possible to improve the usability of big data by providing information that can grasp the properties and characteristics of big data when the data user searches for big data. In addition, by sharing the summary data or sample data generated through the pre-analysis, it is possible to solve the security problem that may occur when the original data is disclosed, thereby enabling the big data sharing between the data provider and the data user. Second, it is necessary to quickly generate appropriate preprocessing results according to the level of disclosure or network status of raw data and to provide the results to users through big data distribution processing using spark. Third, in order to solve the problem of big traffic, the system monitors the traffic of the network in real time. When preprocessing the data requested by the user, preprocessing to a size available in the current network and transmitting it to the user is required so that no big traffic occurs. In this paper, we present various data sizes according to the level of disclosure through pre - analysis. This method is expected to show a low traffic volume when compared with the conventional method of sharing only raw data in a large number of systems. In this paper, we describe how to solve problems that occur when big data is released and used, and to help facilitate sharing and analysis. The client-server model uses SPARK for fast analysis and processing of user requests. Server Agent and a Client Agent, each of which is deployed on the Server and Client side. The Server Agent is a necessary agent for the data provider and performs preliminary analysis of big data to generate Data Descriptor with information of Sample Data, Summary Data, and Raw Data. In addition, it performs fast and efficient big data preprocessing through big data distribution processing and continuously monitors network traffic. The Client Agent is an agent placed on the data user side. It can search the big data through the Data Descriptor which is the result of the pre-analysis and can quickly search the data. The desired data can be requested from the server to download the big data according to the level of disclosure. It separates the Server Agent and the client agent when the data provider publishes the data for data to be used by the user. In particular, we focus on the Big Data Sharing, Distributed Big Data Processing, Big Traffic problem, and construct the detailed module of the client - server model and present the design method of each module. The system designed on the basis of the proposed model, the user who acquires the data analyzes the data in the desired direction or preprocesses the new data. By analyzing the newly processed data through the server agent, the data user changes its role as the data provider. The data provider can also obtain useful statistical information from the Data Descriptor of the data it discloses and become a data user to perform new analysis using the sample data. In this way, raw data is processed and processed big data is utilized by the user, thereby forming a natural shared environment. The role of data provider and data user is not distinguished, and provides an ideal shared service that enables everyone to be a provider and a user. The client-server model solves the problem of sharing big data and provides a free sharing environment to securely big data disclosure and provides an ideal shared service to easily find big data.

• Smart Media Journal
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• v.2 no.3
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• pp.47-53
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• 2013

Recently convergence of IT in logistics system as a typical application RFID/NFC technology is being used, such as, according to the distribution of the flow is generated by a lot of big data. The Hadoop distributed system to collect data items produced by the parallel processing capabilities of logistics information and logistics information for the record management can create. Hadoop system to support the design and development of prototypes were approaching the possibility of its utilization.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.351-354
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• 2014

The emergence of big data has brought many changes to the database management environment. the each amount of big data will increase, but each data size is smaller and simpler. This feature was required to a new data processing techniques. Accordingly, A variety database technology was provided to Specializing in big data processing. It is defined as NoSQL. NoSQL is how to use each different, according to the data characteristics. It is difficult to define one. In this paper, Classified according to the characteristics of each type of NoSQL Appropriate NoSQL is proposed.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.27-35
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• 2017

In near future, IoT data is expected to be a major portion of Big Data. Moreover, sensor data is expected to be major portion of IoT data, and its' research is actively carried out currently. However, processed results may not be trusted and used if outlier data is included in the processing of sensor data. Therefore, method for detection and deletion of those outlier data before processing is studied in this paper. Moreover, we used Spark which is memory based distributed processing environment for fast processing of big sensor data. The detection and deletion of outlier data consist of four stages, and each stage is implemented with Mapper and Reducer operation. The proposed method is compared in three different processing environments, and it is expected that the outlier detection and deletion performance is best in the distributed Spark environment as data volume is increasing.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.161-167
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• 2021

Laboratory Information Management System(LIMS) is a centralized database for storing, processing, retrieving, and analyzing laboratory data, and refers to a computer system or system specially designed for laboratories performing inspection, analysis, and testing tasks. In particular, LIMS is equipped with a function to support the operation of the laboratory, and it requires workflow management or data tracking support. In this paper, we collect data on websites and various channels using crawling technology, one of the automated big data collection technologies for the operation of the laboratory. Among the collected test methods and contents, useful test methods and contents useful that the tester can utilize are recommended. In addition, we implement a complementary LIMS platform capable of verifying the collection channel by managing the feedback.