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

• International Journal of Computer Science & Network Security
• /
• v.21 no.4
• /
• pp.209-213
• /
• 2021

Data mining is the application of specific algorithms for extracting patterns from data and KDD is the automated or convenient extraction of patterns representing knowledge implicitly stored or captured in large databases, data warehouses, the Web, other massive information repositories or data streams. Data mining can be used for decision making in educational system. But educational institution does not use any knowledge discovery process approach on these data; this knowledge can be used to increase the quality of education. The problem was happening in the educational management system, but to make education system more flexible and discover knowledge from it huge data, we will use data mining techniques to solve problem.

• The KIPS Transactions:PartC
• /
• v.10C no.5
• /
• pp.647-656
• /
• 2003

In Webspace, mining traversal patterns is to understand user's path traversal patterns. On this mining, it has a unique characteristic which objects (for example, URLs) may be visited due to their positions rather than contents, because users move to other objects according to providing information services. As a consequence, it becomes very complex to extract meaningful information from these data. Recently discovering traversal patterns has been an important problem in data mining because there has been an increasing amount of research activity on various aspects of improving the quality of information services. This paper presents a Dynamic Link Recommendation (DLR) algorithm that recommends link sets on a Web site through mining frequent traversal patterns. It can be employed to any Web site with massive amounts of data. Our experimentation with two real Weblog data clearly validate that our method outperforms traditional method.

• Journal of Information Processing Systems
• /
• v.19 no.4
• /
• pp.474-482
• /
• 2023

Data mining is a technique for extracting valuable information from vast amounts of data by analyzing statistical and mathematical operations, rules, and relationships. In this study, we employed data mining technology to analyze the data concerning the painting poetry of Maehwado (plum blossom paintings) from the early Joseon Dynasty. The data was extracted from the Hanguk Munjip Chonggan (Korean Literary Collections in Classical Chinese) in the Hanguk Gojeon Jonghap database (Korea Classics DB). Using computer information processing techniques, we carried out web scraping and classification of the painting poetry from the Hanguk Munjip Chonggan. Subsequently, we narrowed down our focus to the painting poetry specifically related to Maehwado in the early Joseon Dynasty. Based on this, refined dataset, we conducted an in-depth analysis and interpretation of the text data at the syllable corpus level. As a result, we found a direct correlation between the corpus statistics for each syllable in Maehwado painting poetry and the symbolic meaning of plum blossoms.

• Journal of Korea Multimedia Society
• /
• v.11 no.11
• /
• pp.1601-1614
• /
• 2008

The number of web service users has been increased rapidly as existing services are changed into the web-based internet applications. Therefore, it is necessary for us to use web log pre-processing technique to detect attacks on diverse web service transactions and it is also possible to extract web mining information. However, existing mechanisms did not provide efficient pre-processing procedures for a huge volume of web log data. In this paper, we proposed both a field based parsing and a high-speed log indexing mechanism based on the suggested B-tree Index Vector structure for performance enhancement. In experiments, the proposed mechanism provides an efficient web log pre-processing and search functions with a session classification. Therefore it is useful to enhance web attack detection function.

• The Journal of Society for e-Business Studies
• /
• v.5 no.2
• /
• pp.113-125
• /
• 2000

There are many web-based applications which need graphs and charts to be generated from data stored in the database. This paper does a comparative study on graph processing techniques for web-based applications through a case study of building a stock information system. The result of this paper can be used for building effective web applications with graphs in areas of EC(electronic commerce), EIS(executive information system), and DM(data mining).

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.11
• /
• pp.6856-6862
• /
• 2014

Web usage mining is a method to extract meaningful patterns based on the web users' log data. Most existing patterns of web usage mining, however, do not consider the users' diverse inclination but create general models. Web users' keywords can have a variety of meanings regarding their tendency and background knowledge. This study evaluated the extraction web-user's pattern after collecting and analyzing the web usage information on the users' keywords of interest. Web-user's pattern can supply a web page network with various inclination information based on the users' keywords of interest. In addition, the Web-user's pattern can be used to recommend the most appropriate web pages and the suggested method of this experiment was confirmed to be useful.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.4
• /
• pp.55-62
• /
• 2016

In this paper, we propose the INDEM(Internet News Data Extraction Middleware) system for the removal of the unnecessary data in internet news. Although data on the internet can be used in various fields such as source of data of IR(Information Retrieval), Data mining and knowledge information service, it contains a lot of unnecessary information. The removal of the unnecessary data is a problem to be solved prior to the study of the knowledge-based information service that is based on the data of the web page. The INDEM system parses html and explores the XPath, and it is to perform the analysis. The user simply utilize INDEM by implementing an abstract class that provides INDEM, and can obtain the analysis information. INDEM System through this process delivers the analysis information including the main contents of news site to the users. In this paper, the INDEM system was adapted in a stand-alone and web service system and it was evaluated on the basis of 16 news site. As a result, performance of the INDEM system is affected in html source data size and complexity of used html grammar than the main news data size.

• The Journal of Information Technology
• /
• v.12 no.3
• /
• pp.7-17
• /
• 2009

This research is about the design of an opinion drawing and analysis system through statistical based Web Mining of web contents, where data of opinions are automatically drawn and analyzed concerning web documents that are scattered around in various web sites that exist within the internet. Furthermore, provides a search service that can easily classify positive/negative opinions and also provide searching and statistical information. Users, who want to search for opinions, can input a specific keyword to observe opinions of others easily. In addition, there is a merit in materializing the monitoring system.

• Journal of Korea Society of Industrial Information Systems
• /
• v.18 no.2
• /
• pp.47-57
• /
• 2013

Today a lot of functional areas of a firm are operated on the Web. Online shopping malls analyze web log recording customers' activities on the web to connect them to business outcomes. Not only commercial websites, but online news sites also need to collect and analyze web logs to understand their news readers' interest. However, little research has been performed yet. In this research we mined the web access log of an online news website and conduct Market Basket Analysis to uncover the association rules among the categories of news articles. The research is composed of two stages: 1) Identifying the individual session of a visitor; 2) Mining association rule from news articles read by each session. We gather 7-day access logs two times. The results of log mining and meanings of association rules are suggested with managerial implications in conclusion section.