• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.1
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• pp.111-120
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• 2015

The christian communities have been maintained and spreaded based on the great foundation of the holy bible. The printing and publishing technology made the holy bible popular at the end of the middle age. Namely, because of this remarkable development, the Bible have been became the best seller for centuries. Since the smart phone users have been growing, the number of people use the Bible Application have been increased. In this circumstance, the interface becomes a crucial element in the Age of Digital Media. Thus, developers have to regard the user's convenient. The user's characteristic is the important point to develop applications. This study is focusing on a particular using of the Bible, and how the using characteristic affects on application's UI. For this study, five bible applications reflecting particular using characteristics are chosen and compared in terms of function of services and elements of UI. Form this comparison and analysis, this study deduces the pattern of UI considering particular using characteristics such as patterns of reading, recording, sharing, and searching the bible. This study would be useful data for publications formed into applications and setting the UI which reflected the using characteristics be better user-centered.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.77-83
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• 2015

Frequent pattern mining, which is one of the major areas actively studied in data mining, is a method for extracting useful pattern information hidden from large data sets or databases. Moreover, frequent pattern mining approaches have been actively employed in a variety of application fields because the results obtained from them can allow us to analyze various, important characteristics within databases more easily and automatically. However, traditional frequent pattern mining methods, which simply extract all of the possible frequent patterns such that each of their support values is not smaller than a user-given minimum support threshold, have the following problems. First, traditional approaches have to generate a numerous number of patterns according to the features of a given database and the degree of threshold settings, and the number can also increase in geometrical progression. In addition, such works also cause waste of runtime and memory resources. Furthermore, the pattern results excessively generated from the methods also lead to troubles of pattern analysis for the mining results. In order to solve such issues of previous traditional frequent pattern mining approaches, the concept of representative pattern mining and its various related works have been proposed. In contrast to the traditional ones that find all the possible frequent patterns from databases, representative pattern mining approaches selectively extract a smaller number of patterns that represent general frequent patterns. In this paper, we describe details and characteristics of pattern condensing techniques that consider the maximality or closure property of generated frequent patterns, and conduct comparison and analysis for the techniques. Given a frequent pattern, satisfying the maximality for the pattern signifies that all of the possible super sets of the pattern must have smaller support values than a user-specific minimum support threshold; meanwhile, satisfying the closure property for the pattern means that there is no superset of which the support is equal to that of the pattern with respect to all the possible super sets. By mining maximal frequent patterns or closed frequent ones, we can achieve effective pattern compression and also perform mining operations with much smaller time and space resources. In addition, compressed patterns can be converted into the original frequent pattern forms again if necessary; especially, the closed frequent pattern notation has the ability to convert representative patterns into the original ones again without any information loss. That is, we can obtain a complete set of original frequent patterns from closed frequent ones. Although the maximal frequent pattern notation does not guarantee a complete recovery rate in the process of pattern conversion, it has an advantage that can extract a smaller number of representative patterns more quickly compared to the closed frequent pattern notation. In this paper, we show the performance results and characteristics of the aforementioned techniques in terms of pattern generation, runtime, and memory usage by conducting performance evaluation with respect to various real data sets collected from the real world. For more exact comparison, we also employ the algorithms implementing these techniques on the same platform and Implementation level.

• Journal of Korean Library and Information Science Society
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• v.43 no.1
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• pp.221-239
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• 2012

The information that has been created according to the complex environment and usage pattern of library user can provide context-aware information service through knowledge structuralization on whether it is a suitable situation for user. Accordingly, the development of a context model for defining the various contexts of library user and for the structuralization of interrelated context information is an essential requirement. This study examined the context concept and context modeling, and utilizing the concept of Activity Theory by Engestrom, the activity model of library user was designed as 1) subject, 2) object, 3) tools, 4) divison of labor, 5) community, and 6) rules. In addition, for the purpose of analyzing the context of library user, activity information was tracked to utilize the Shadow Tracking for observing and recording their forms, and the methodology of CAbAT (Context Analysis based on Activity Theory) was utilized for the collected activity information to analyze the user context model.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.12
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• pp.1264-1268
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• 2010

In this paper, we propose a behavior pattern analysis method for users tasking on hands-on security exercise missions. By analysing and evaluating the observed user behavior data, the proposed method discovers some significant patterns able to contribute mission successes or fails. A Markov chain modeling approach and algorithm is used to automate the whole analysis process. How to apply and understand our proposed method is briefly shown through a case study, "network service configurations for secure web service operation".

• International Journal of Contents
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• v.13 no.2
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• pp.21-28
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• 2017

Abnormal data in the manufacturing process makes it difficult to find useful information that can be applied in data management for the manufacturing industry. It causes various problems in the daily process of production. An issue from the abnormal data can be handled by our method that uses big data and visualization. Visualization is a new technology that transforms data representation into a two-dimensional representation. Nowadays, many newly developed technologies provide data analysis, algorithm, optimization, and high efficiency, and they meet user requirements. We propose combined production of the data visualization approach that uses integrative visualization of sources of abnormal pattern analysis results. The perceived idea of the proposed approach can solve the problem as it also works for big data. It can also improve the performance and understanding by using visualization and solving issues that occur in the manufacturing process with a calendar heat map.

• Journal of the Korean Society for information Management
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• v.27 no.4
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• pp.109-130
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• 2010

The purpose of this study is to identify th patterns in the news reporters' information seeking behaviors by observing their web activities. For this purpose, transaction logs collected from 23 news reporters were analyzed. Web tracking software was installed to collect the data from their PCs, and a total of 39,860 web logs were collected in two weeks. Start and end pattern of sessions, transitional pattern by step, sequence rule model was analyzed and the pattern of Internet use was compared with the general public. the analysis of pattern derived a web information seeking behavior modes that consists of four types of behaviors: fact-checking browsing, fact-checking search, investigative browsing and investigative search.

• Journal of applied mathematics & informatics
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• v.28 no.1_2
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• pp.439-450
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• 2010

In this paper, we propose a new idea to evaluate the prediction accuracy of user's preference generated by memory-based collaborative filtering algorithm before prediction process in the recommender system. Our analysis results show the possibility of a pre-evaluation before the prediction process of users' preference of item's transaction on the web. Classification functions proposed in this study generate a user's rating pattern under certain conditions. In this research, we test whether classification functions select users who have lower prediction or higher prediction performance under collaborative filtering recommendation approach. The statistical test results will be based on the differences of the prediction accuracy of each user group which are classified by classification functions using the generative probability of specific rating. The characteristics of rating patterns of classified users will also be presented.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.2
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• pp.369-383
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• 2020

This study proposes a method to effectively teaching technic for pattern development and virtual garment manufacturing by adopting the K-MOOC platform for the Apparel Pattern CAD curriculum. According to K-MOOC guidelines, Apparel Pattern CAD curriculum were developed and presented through the K-MOOC platform. A questionnaire survey was utilized to evaluate K-MOOC platform features in terms of learner satisfaction when adopting the 5-point Likert scale. Questionnaire survey participants included 52 college students. The result of the survey found that most of the attributes of the K-MOOC platform were highly rated in terms of interaction and learning effectiveness. The user interface of the K-MOOC platform were shown to be satisfactory in terms of usability. Participants gave a positive assessment of the benefits of online lectures when comparing online and offline lectures. In particular, the preference for online lectures in computer-related courses such as CAD was higher than the offline. It was concluded that the Apparel Pattern CAD curriculum based on the K-MOOC platform was effective and satisfactory for learners in various aspects.

• International Journal of Highway Engineering
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• v.18 no.3
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• pp.47-57
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• 2016

PURPOSES : This study aims to examine the differences between the existing traffic demand forecasting method and the traffic demand forecasting method considering future regional development plans and new road construction and expansion plans using a four-step traffic demand forecast for a more objective and sophisticated national highway maintenance. This study ultimately aims to present future pavement deterioration and budget forecasting planning based on the examination. METHODS : This study used the latest data offered by the Korea Transport Data Base (KTDB) as the basic data for demand forecast. The analysis scope was set using the Daejeon Metropolitan City's O/D and network data. This study used a traffic demand program called TransCad, and performed a traffic assignment by vehicle type through the application of a user equilibrium-based multi-class assignment technique. This study forecasted future traffic demand by verifying whether or not a realistic traffic pattern was expressed similarly by undertaking a calibration process. This study performed a life cycle cost analysis based on traffic using the forecasted future demand or existing past pattern, or by assuming the constant traffic demand. The maintenance criteria were decided according to equivalent single axle loads (ESAL). The maintenance period in the concerned section was calculated in this study. This study also computed the maintenance costs using a construction method by applying the maintenance criteria considering the ESAL. The road user costs were calculated by using the user cost calculation logic applied to the Korean Pavement Management System, which is the existing study outcome. RESULTS : This study ascertained that the increase and decrease of traffic occurred in the concerned section according to the future development plans. Furthermore, there were differences from demand forecasting that did not consider the development plans. Realistic and accurate demand forecasting supported an optimized decision making that efficiently assigns maintenance costs, and can be used as very important basic information for maintenance decision making. CONCLUSIONS : Therefore, decision making for a more efficient and sophisticated road management than the method assuming future traffic can be expected to be the same as the existing pattern or steady traffic demand. The reflection of a reliable forecasting of the future traffic demand to life cycle cost analysis (LCCA) can be a very vital factor because many studies are generally performed without considering the future traffic demand or with an analysis through setting a scenario upon LCCA within a pavement management system.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.3
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• pp.59-74
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• 2002

This study investigates two basic operations of mobility management of PCNs (Personal Communication Networks), i.e., the location update and the paging of the mobile terminal. From the realistic consideration that a user either moves through several cells consecutively or stays in a cell with long time, we model the mobility pattern by introducing two types of CRT (Cell Residence Time). Mobility patterns of the mobile terminal are classified Into various ways by using the ratios of two types of CRT. Cost analysis is performed for distance-based and movement-based location update schemes combined with blanket polling paging and selective paging scheme. It is demonstrated that in a certain condition of mobility pattern and call arrival pattern, 2-state CRT model produces different optimal threshold and so, is more effective than IID ( Independently-Identically-Distributed) CRT model. An analytical model for the new CRT model is compact and easily extendable to the other location update schemes.