• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.4
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• pp.195-200
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• 2013

This paper proposes an SNS and Web Data Analytics System which can utilize a business marketing strategy by analyzing negative SNS and Web Data that can do great damage to a business image. It consists of the Data Collection Module collecting SNS and Web Data, the Hbase Module storing the collected data, the Data Analysis Module estimating and classifying the meaning of data after an semantic analysis of the collected data, and the PHS Module accomplishing an optimized Map Reduce by using SNS and Web data involved a Businesse. This paper can utilize this analysis result for a business marketing strategy by efficiently managing SNS and Web data with these modules.

• Journal of Information Technology Services
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• v.13 no.4
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• pp.231-243
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• 2014

Of the various data that corporations can approach, web log data are important data that correspond to data analysis to implement customer relations management strategies. As the volume of approachable data has increased exponentially due to the Internet and popularization of smart phone, web log data have also increased a lot. As a result, it has become difficult to expand storage to process large amounts of web logs data flexibly and extremely hard to implement a system capable of categorizing, analyzing, and processing web log data accumulated over a long period of time. This study thus set out to apply Hadoop, a distributed processing system that had recently come into the spotlight for its capacity of processing large volumes of data, and propose an efficient analysis plan for large amounts of web log. The study checked the forms of web log by the effective web log collection methods and the web log levels by using Hadoop and proposed analysis techniques and Hadoop organization designs accordingly. The present study resolved the difficulty with processing large amounts of web log data and proposed the activity patterns of users through web log analysis, thus demonstrating its advantages as a new means of marketing.

• Korean Journal of Computational Design and Engineering
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• v.13 no.3
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• pp.217-226
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• 2008

Web Services for CAD (WSC) aims at interoperation with CAD systems based on Web Services. This paper introduces one part of WSC which enables remote users to retrieve assembly model data using Web Services. However, retrieving assembly model data takes long time. To resolve this problem, this paper proposes using parallel Web Services. As assembly models comprise a set of part models, it is easy to separate the problem domain into smaller problems. In addition, Web Services inherently supports distributed computing. This characteristic makes the parallel processing of Web Services easy. Firstly, the implementation of WSC which retrieves assembly model data based parallel Web Services is shown. And then, for the comparison, the experiments on the retrieval of assembly model data based on single Web Services and parallel Web Services are shown.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.204-209
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• 2002

The interest of data mining in artificial intelligence with fuzzy logic has been increased. Data mining is a process of extracting desirable knowledge and interesting pattern ken large data set. Because of expansion of WWW, web data is more and more huge. Besides mining web contents and web structures, another important task for web mining is web usage mining which mines web log data to discover user access pattern. The goal of web usage mining in this paper is to find interesting user pattern in the web with user feedback. It is very important to find user's characteristic fer e-business environment. In Customer Relationship Management, recommending product and sending e-mail to user by extracted users characteristics are needed. Using our method, we extract user profile from the result of web usage mining. In this research, we concentrate on finding association rules and verify validity of them. The proposed procedure can integrate fuzzy set concept and association rule. Fuzzy association rule uses given server log file and performs several preprocessing tasks. Extracted transaction files are used to find rules by fuzzy web usage mining. To verify the validity of user's feedback, the web log data from our laboratory web server.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.95-102
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• 1999

The web-based group collaborative systems are emerging as enterprise-wide information systems. Since data in group collaborative systems are apt to be shared among multiple concurrent users and modified simutaneously by them, the web-based group collaborative systems must support synchronous collaboration in order to provide users with synchronized and consistent views of shared data. This Paper proposes an active web server which can facilitate synchronous collaboration in web-based group collaborative systems. To accomplish such a goal, the active web server manages dependency relationships between shared data and web browsers referencing them and actively propagates changing details of the shared data to all web browsers referencing them. And, this paper examines usefullness and effectiveness of the active web server to apply it to the ball-bearing design example of concurrent engineering design systems. The prototype system of the active web server is developed on a commercial Object-oriented Database Management System (ODBMS) called OBJECTSTORE using the C＋＋ programming language.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.575-584
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• 2013

Data types used for big-data analysis are very widely, such as news, blog, SNS, papers, patents, sensed data, and etc. Particularly, the utilization of web documents offering reliable data in real time is increasing gradually. And web crawlers that collect web documents automatically have grown in importance because big-data is being used in many different fields and web data are growing exponentially every year. However, existing web crawlers can't collect whole web documents in a web site because existing web crawlers collect web documents with only URLs included in web documents collected in some web sites. Also, existing web crawlers can collect web documents collected by other web crawlers already because information about web documents collected in each web crawler isn't efficiently managed between web crawlers. Therefore, this paper proposed a distributed web crawler. To resolve the problems of existing web crawler, the proposed web crawler collects web documents by RSS of each web site and Google search API. And the web crawler provides fast crawling performance by a client-server model based on RMI and NIO that minimize network traffic. Furthermore, the web crawler extracts core content from a web document by a keyword similarity comparison on tags included in a web documents. Finally, to verify the superiority of our web crawler, we compare our web crawler with existing web crawlers in various experiments.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.281-289
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• 2001

The continuous growth in the use of the World Wide Web is creating the data with very large scale and different types. Analyzing such data can help to determine the life time value of users, evaluate the effectiveness of web sites, and design marketing strategies and services. In this paper, we propose some analysis methods for web data and present an example of a prototypical web data analysis.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.366-370
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• 2003

In this research, we suggest a hybrid intelligent Web recommendation systems based on Web data mining and case-based reasoning (CBR). One of the important research topics in the field of Internet business is blending artificial intelligence (AI) techniques with knowledge discovering in database (KDD) or data mining (DM). Data mining is used as an efficient mechanism in reasoning for association knowledge between goods and customers＇ preference. In the field of data mining, the features, called attributes, are often selected primary for mining the association knowledge between related products. Therefore, most of researches, in the arena of Web data mining, used association rules extraction mechanism. However, association rules extraction mechanism has a potential limitation in flexibility of reasoning. If there are some goods, which were not retrieved by association rules-based reasoning, we can＇t present more information to customer. To overcome this limitation case, we combined CBR with Web data mining. CBR is one of the AI techniques and used in problems for which it is difficult to solve with logical (association) rules. A Web-log data gathered in real-world Web shopping mall was given to illustrate the quality of the proposed hybrid recommendation mechanism. This Web shopping mall deals with remote-controlled plastic models such as remote-controlled car, yacht, airplane, and helicopter. The experimental results showed that our hybrid recommendation mechanism could reflect both association knowledge and implicit human knowledge extracted from cases in Web databases.

• The Journal of Society for e-Business Studies
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• v.7 no.3
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• pp.93-103
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• 2002

The combination of data warehousing and Internet technology produces a new concept - web warehousing. Due to the availability of web technologies and the need to make prompt decisions with timely information, web warehousing is emerging as a key strategic business weapon. Yet despite the many promising benefits of web warehousing, researchers have also identified several challenges, including scalability and availability. With the rise of the Internet and data centric computing applications, the use of new Storage Area Network (SAN) technology has been spotlighted for the possibility of a new data repository for web warehousing. In this article, the two new concepts of web warehousing and storage area networks are introduced. In particular, a SAN is discussed in detail as an alternative data repository to overcome the current limitations of web warehousing.

• Journal of the Korean Data and Information Science Society
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• v.16 no.1
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• pp.157-163
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• 2005

In the Web, the data is generally gathered automatically by Web servers and collected in server or access logs. However, as users access larger and larger amounts of data, query response times to extract information inevitably get slower. A method to resolve this issue is the use of summary tables. In this short note, we design a prototype of summary tables that can efficiently extract information from Web log data. We also present the relative performance of the summary tables against a sampling technique and a method that uses raw data.