Byoung-Yup Lee

Utilization of Social Media Analysis using Big Data

The analysis method using Big Data has evolved based on the Big data Management Technology. There are quite a few researching institutions anticipating new era in data analysis using Big Data and IT vendors has been sided with them launching standardized technologies for Big Data management technologies. Big Data is also affected by improvements of IT gadgets IT environment. Foreran by social media, analyzing method of unstructured data is being developed focusing on diversity of analyzing method, anticipation and optimization. In the past, data analyzing methods were confined to the optimization of structured data through data mining, OLAP, statics analysis. This data analysis was solely used for decision making for Chief Officers. In the new era of data analysis, however, are evolutions in various aspects of technologies; the diversity in analyzing method using new paradigm and the new data analysis experts and so forth. In addition, new patterns of data analysis will be found with the development of high performance computing environment and Big Data management techniques. Accordingly, this paper is dedicated to define the possible analyzing method of social media using Big Data. this paper is proposed practical use analysis for social media analysis through data mining analysis methodology.

Positioning sensor nodes and smart devices for multimedia data transmission in wireless sensor and mobile P2P networks

In recent, studies on multimedia data transmission have been progressing in a wireless sensor network that consist of a large number of sensor nodes and a mobile P2P network that consists of a large of number of smart devices such as smart phones and wearable devices. When multimedia data such as image, audio, and video are transmitted, it is important to figure out the positions of a sensor nodes and a smart device in the networks. However, existing positioning schemes do not consider network holes that can occur in real application areas, thereby causing many errors during positioning. In this paper, we propose a new positioning scheme considering holes in wireless sensor and mobile P2P network environments. In the proposed scheme, holes are detected based on neighboring node density when holes exist in a network, and the position of a sensor node or a smart device is estimated by conducting distance correction based on the cumulative number of hole-boundary nodes. Through the proposed scheme, highly accurate sensor positioning can be performed even in circumstances where holes are present over a network. To verify the good performance of the proposed scheme, a performance evaluation is conducted by comparing it with existing schemes. It is shown through experiments that the proposed positioning scheme transmits multimedia data to the destination node without data loss when the holes in the network exist.

Implementation of Data processing of the High Availability for Software Architecture of the Cloud Computing

These days, there are more and more IT research institutions which foresee cloud services as the predominant IT service in the near future and there, in fact, are actual cloud services provided by some IT leading vendors. Regardless of physical location of the service and environment of the system, cloud service can provide users with storage services, usage of data and software. On the other hand, cloud service has challenges as well. Even though cloud service has its edge in terms of the extent to which the IT resource can be freely utilized regardless of the confinement of hardware, the availability is another problem to be solved. Hence, this paper is dedicated to tackle the aforementioned issues; prerequisites of cloud computing for distributed file system, open source based Hadoop distributed file system, in-memory database technology and high availability database system. Also the author tries to body out the high availability mass distributed data management architecture in cloud service`s perspective using currently used distributed file system in cloud computing market.

Distributed File Systems Architectures of the Large Data for Cloud Data Services

In these day, some of IT venders already were going to cloud computing market, as well they are going to expand their territory for the cloud computing market through that based on their hardware and software technology, making collaboration between hardware and software vender. Distributed file system is very mainly technology for the cloud computing that must be protect performance and safety for high levels service requests as well data store. This paper introduced distributed file system for cloud computing and how to use this theory such as memory database, Hadoop file system, high availability database system. now In the market, this paper define a very large distributed processing architect as a reference by kind of distributed file systems through using technology in cloud computing market.

Materialize the Checklist through Type of Classification analysis for the Regulatory Compliance and Database Encryption, Access Control

Due to the rapid development of the Internet, many companies in a variety of applications to users open an unspecified number of the current business environment, security of personal information about recent issues are often mentioned in terms of its importance may be the company`s top priority. The government recently on personal information strengthening measures on information communications network law enacted into law which is applicable to various industries. Companies to protect the personal information of various measures to comply with these regulations, and arrange your personal information for internal management to enhance security fast security solution has been introduced. The number of used data is stored in the DBMS in terms of compliance with these regulations at the same time effectively to ensure data security and encryption measures, access control, audit, each separated by an implementation of the solution and how it compares with the best Database security plan allows you to explore as a this paper`s security checklist.

An efficient distributed caching for accessing small files in HDFS

In this paper, we propose a distributed caching scheme to efficiently access small files in Hadoop distributed file system. The proposed scheme reduces the volume of metadata to manage in the NameNode by combining and storing multiple small files in a block. In addition, it reduces unnecessary accesses by maintaining information on requested files using client cache and DataNode cache, and synchronizing metadata of the client cache. The client cache maintains small files requested by users and metadata, and each DataNode cache maintains small files frequently requested by users. Performance evaluation shows that the proposed distributed cache management scheme significantly outperforms existing schemes in small file access costs.

Materialized View Selection Scheme for enhancing RDF Query Performance

AbstractWith the development of the semantic web, a large amount of data being produced nowadays is in RDF format. RDF is represented by a triple. An RDF database consisting of triples requires the high cost of join query processing. Materialized view is known as a scheme to reduce the query processing cost by accessing materialized views without accessing the database. It is physically stored the results or the intermediate results of the query processing in a storage area. In this paper, we propose a materialized view selection scheme by using decision tree to solve such a problem. The decision tree considers the size and maintenance costs of the materialized view as well as the profit of query response times. It is shown through performance evaluation that the proposed scheme increases the number of materialized views in the limited storage space and decreases the update rates of the materialized views. ■ keyword : ∣RDF∣Decision Tree ∣Materialized View ∣Shortcut ∣ * 이 논문은 미래창조과학부 및 정보통신기술진흥센터의 대학ICT연구센터육성 지원사업(IITP-2015-H8501-15-1013), 교육부와 한국연구재단의 지역혁신인력양성사업(No.2013H1B8A2032298), 2013년도 정부(미래창조과학부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No.2013R1A2A2A01015710), 2014년도 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임(No.2014R1A1A2055778).접수일자 : 2015년 06월 25일 수정일자 : 2015년 07월 24일 심사완료일 : 2015년 07월 24일 교신저자 : 유재수, e-mail : yjs@chungbuk.ac.kr

Software Architecture for Implementing the Grid Computing of the High Availability Solution through Load Balancing

In these days, internet environment are very quickly development as well on-line service have been using a online for the mission critical business around the world. As the amount of information to be processed by computers has recently been increased there has been cluster computing systems developed by connecting workstations server using high speed networks for high availability. but cluster computing technology are limited for a lot of IT resources. So, grid computing is an expanded technology of distributed computing technology to use low-cost and high-performance computing power in various fields. Although the purpose of Grid computing focuses on large-scale resource sharing, innovative applications, and in some case, high-performance orientation, it has been used as conventional distributed computing environment like clustered computer until now because grid middleware does not have common sharable information system. In order to use grid computing environment efficiently which consists of various grid middleware, it is necessary to have application-independent information system which can share information description and services, and expand them easily. This paper proposed new database architecture and load balancing for high availability through Grid technology.

Buying Customer Classification in Automotive Corporation with Decision Tree

Generally, data mining is the process of analyzing data from different perspectives and summarizing it into useful information that can be used to increase revenue, cuts costs, or both. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Data mining is one of the fastest growing field in the computer industry. Because of According to computer technology has been improving, Massive customer data has stored in database. Using this massive data, decision maker can extract the useful information to make a valuable plan with data mining. Data mining offers service providers great opportunities to get closer to customer. Data mining doesn`t always require the latest technology, but it does require a magic eye that looks beyond the obvious to find and use the hidden knowledge to drive marketing strategies. Automotive market face an explosion of data arising from customer but a rate of increasing customer is getting lower. therefore, we need to determine which customer are profitable clients whom you wish to hold. This paper builds model of customer loyalty detection and analyzes customer buying patterns in automotive market with data mining using decision tree as a quinlan C4.5 and basic statics methods.

Project Management Methodology using Managing Data Dictionary

With the development of IT technologies, IT environment is making great change over life whole and is displacing business and business achievement systems of industry at the fast speed. Software development using project management tool is more important because of constructing the consistent and reliable system. So, design and implementation of the project management tool which support data standardization of project is proposed in this paper.