• Journal of Korea Society of Industrial Information Systems
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• v.15 no.4
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• pp.17-27
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• 2010

Major IT vendors expect that cloud computing technology makes it possible to reduce the contents service cycle, speed up application deployment and skip the installation process, reducing operational costs, proactive management etc. However, cloud computing environment for massive content service solutions requires high-performance data processing to reduce the time of data processing and analysis. In this study, Efficient_Cloud_Processing_Scheme(ECPS) is proposed for allocation of resources for massive content services. For high-performance services, optimized resource allocation plan is presented using MapReduce programming techniques and association rules that is used to detect hidden patterns in data mining, based on levels of Hadoop platform(Infrastructure as a service). The proposed ECPS has brought more than 20% improvement in performance and speed compared to the traditional methods.

• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.76-85
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• 2012

Through opinion mining, we can analyze the degree of positive or negative sentiments that customers feel about important entities or attributes in online customer reviews. But, the limit of the opinion mining techniques is to provide only simple functions in analyzing the reviews. In this paper, we proposed novel techniques that can analyze the online customer reviews multi-dimensionally. The novel technique is to modify the existing OLAP techniques so that they can be applied to text data. The novel technique, that is, multi-dimensional analytic model consists of noun, adjective and document axes which are converted into four relational tables in relational database. The multi-dimensional analysis model would be new framework which can converge the existing opinion mining, information summarization and clustering algorithms. In this paper, we implemented the multi-dimensional analysis model and algorithms. we recognized that the system would enable us to analyze the online customer reviews more complexly.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.101-107
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• 2018

Recently, Korea's defense industry has advanced highly, and defense R&D budget is gradually increasing in defense budget. However, without objective analysis of defense industry technology, effective defense R&D activities are limited and defense budgets can be used inefficiently. Therefore, in addition to analyzing the defense industry technology quantitatively reflecting the opinions of the experts, this paper aims to analyze the defense industry technology objectively by quantitative methods, and to make efficient use of the defense budget. In addition, we propose a patent analysis method to grasp the characteristics of the defense industry technology and the vacant technology objectively and systematically by applying the big data analysis method, which is one of the keywords of the 4th industrial revolution, to the defense industry technology. The proposed method is applied to the technology of the firepower industry among several defense industrial technologies and the case analysis is conducted. In the process, the patents of 10 domestic companies related to firepower were collected through the Kipris in the defense industry companies' classification of the Korea Defense Industry Association(KDIA), and the data matrix was preprocessed to utilize IPC codes among them. And then, we Implemented association rule mining which can grasp the relation between each item in data mining technique using R program. The results of this study are suggested through interpretation of support, confidence lift index which were resulted from suggested approach. Therefore, this paper suggests that it can help the efficient use of massive national defense budget and enhance the competitiveness of defense industry technology.

• Information Systems Review
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• v.15 no.2
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• pp.111-127
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• 2013

This research introduces a case of NFC (Near Field Communication)-based exhibition support system for analyzing visitors' POB (Point of Behavior) data gathered from the system. The application of NFC technology to an exhibition space allows visitors new experiences of exposition and exhibitors to collect and analyze data about behaviors of visitors. The NFC-based exhibition support system is applied to the 'Korea Travel Expo 2013.' The visitors' behaviors are analyzed based on collected NFC tag touch data and association rules among booths are extracted. Furthermore, the tag touch data are integrated with the survey data for catching the demographics-based implications.

• CRM연구
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• v.4 no.2
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• pp.19-29
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• 2011

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems.

• Information Systems Review
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• v.10 no.2
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• pp.137-158
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• 2008

Rapid development of various information technologies creates new opportunities in online and offline markets. In this changing market environment, customers have various demands on new products and services. Therefore, their power and influence on the markets grow stronger each year. Companies have paid great attention to customer relationship management. Especially, personalized product recommendation systems, which recommend products and services based on customer's private information or purchasing behaviors in stores, is an important asset to most companies. CRM is one of the important business processes where reliable information is mined from customer database. Data mining techniques such as artificial intelligence are popular tools used to extract useful information and knowledge from these customer databases. In this research, we propose a recommendation system that predicts customer's purchase intention. Then, customer's purchasing intention of specific product is predicted by using data mining techniques using receipt data set. The performance of this suggested method is compared with that of other data mining technologies.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.6
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• pp.67-77
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• 2018

Currently, there are many problems due to the decline in school-age population. Moreover, Korea has the largest number of universities compared to the population, and the university enrollment rate is also the highest in the world. As a result, the minimum student retention rate required for the survival of each university is becoming increasingly important. The purpose of this study was to examine the effects of reducing the number of graduates of education and the social climate that prioritizes employment. And to determine what the basic direction is for students to manage the student retention rate, which can be maintained from admission to graduation, to determine the optimal input variables, Based on the input parameters, we will make associative analysis using apriori algorithm to collect training data that is most suitable for maintenance rate management and make base data for development of the most efficient Deep Learning module based on it. The accuracy of Deep Learning was 75%, which is a measure of graduation using decision trees. In decision tree, factors that determine whether to graduate are graduated from general high school and students who are female and high in residence in urban area have high probability of graduation. As a result, the Deep Learning module developed rather than the decision tree was identified as a model for evaluating the graduation of students more efficiently.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.59-68
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• 2011

As the role of soccer game analyst who analyzes soccer games and creates soccer wining strategies is emphasized, it is required to have high-level analysis beyond the procedural ones such as main event detection in the context of IT based broadcasting soccer game research community. In this paper, we propose a novel approach to generate the high-level in-depth analysis results via real-time text based soccer Webcast and text mining. Proposed method creates a metadata such as attribute, action and event, build index, and then generate available knowledges via text mining techniques such as association rule mining, event growth index, and pathfinder network analysis using Webcast and domain knowledges. We carried out a feasibility experiment on the proposed technique with the Webcast text about Spain team's 2010 World Cup games.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.279-300
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• 2001

Data mining plays an important role in knowledge discovery process and usually various existing algorithms are selected for the specific purpose of the mining. Currently, data mining techniques are actively to the statistics, business, electronic commerce, biology, and medical area and currently numerous algorithms are being researched and developed for these applications. However, in a long run, only a few algorithms, which are well-suited to specific applications with excellent performance in large database, will survive. So it is reasonable to focus our effort on those selected algorithms in the future. This paper classifies about 30 existing algorithms into 7 categories - association rule, clustering, neural network, decision tree, genetic algorithm, memory-based reasoning, and bayesian network. First of all, this work analyzes systematic hierarchy and characteristics of algorithms and we present 14 criteria for classifying the algorithms and the results based on this criteria. Finally, we propose the best algorithms among some comparable algorithms with different features and performances. The result of this paper can be used as a guideline for data mining researches as well as field applications of data mining.

• Journal of the Korea Society of Computer and Information
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• v.10 no.2 s.34
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• pp.133-141
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• 2005

We need to study on simulation method of user's mobility Patterns for the performance analysis of the location management in wireless mobile networks. For this purpose ,this paper presents a user mobility model of wireless mobile networks with regular Patterns Sometimes mobile users randomly move , but they show the movement characteristics that regularly change their locations in some patterns in given time slots. So, we suggest the mobility model that can describe user's time related movement patterns. This model consists of some elements-positions, transitions , transition Probabilities which are variable, and some geographical paths for each transitions. We describe the simulation method for users' mobilities with random movements and regular movements , too. The simulation results by the model show that the suggested model can generate movement scenarios having regular patterns related with location and time.