• Management & Information Systems Review
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• v.7
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• pp.187-210
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• 2001

The purpose of this study is to develop an framework for the marketing performance evaluation of POS system by the theoretical model for evaluation of information system and the empirical data of Super-chains. This study were empirically examined an effect of POS system and its information quality on POS information use and another effect of POS information use on marketing performances of firms using POS system. Also, in order to achieve this purpose, a literature survey on MIS and marketing was conducted. In this study, the structural equation model(SEM) was established for verifying the relationship of above variables. And, several hypothesis were established and examined empirically from that SEM. In conclusion POS system and its information quality influenced positively on POS information use, and that information use influenced on marketing performance as the effectiveness of POS system operations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.305-318
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• 2022

Abusive behaviors have become a common issue in many online social media platforms. Profanity is common form of abusive behavior in online. Social media platforms operate the filtering system using popular profanity words lists, but this method has drawbacks that it can be bypassed using an altered form and it can detect normal sentences as profanity. Especially in Korean language, the syllable is composed of graphemes and words are composed of multiple syllables, it can be decomposed into graphemes without impairing the transmission of meaning, and the form of a profane word can be seen as a different meaning in a sentence. This work focuses on the problem of filtering system mis-detecting normal phrases with profane phrases. For that, we proposed the deep learning-based framework including grapheme and syllable separation-based word embedding and appropriate CNN structure. The proposed model was evaluated on the chatting contents from the one of the famous online games in South Korea and generated 90.4% accuracy.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.

• Information Systems Review
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• v.21 no.3
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• pp.87-110
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• 2019

The Fintech services provide innovation to financial services users using various mobile devices and computers in wired and wireless communication environments. In this study, we develope a theoretical research framework to explain the psychology of Fintech services users based on a cognitive, affective, and conative framework. Using this framework, this study analyzes the relationships between the cognitive characteristics (i.e., innovation, relative advantage, ease of use, and usefulness), emotional characteristic (i.e., attitude), and behavioral characteristic (i.e., actual use) toward Fintech services users. This study conducted an online survey of people who have experienced using Fintech services. And the data of the collected Fintech services users was analyzed using structural equation model software (i.e., SMART PLS 2.0 M3). The results of the empirical analysis show the relationships between innovation, relative advantage, perceived usefulness, perceived ease of use, attitude, and actual use of Fintech service users. The results of this study provide useful information to improve the practical use of Fintech services users in the Internet of Things (IoT) environment.

• Information Systems Review
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• v.5 no.2
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• pp.23-36
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• 2003

This study proposed the approaches to business process redesign and business network redesign in e-business environment through the case analysis of e-Transformation performed in Kolon Gloteck, Inc. which has been managed and operated in a traditional mode. The proposed e-Transformation approaches of the traditional firm in this study were as following: 1) Network-focused business process redesign; 2) Introducing ERP(Enterprise Resource Planning) as e-Business backbone; 3) Establishing transparence of business functions through using shared database; 4) Understanding customer and constructing IT(Information Technology) infrastructure for customer satisfaction; 5) Performing data and process standardization; 6) Applying the best suitable IT(Information Technology) such as JSP(Java Server Pages) and VPN(Virtual Private Network). The findings of this case study are thought to be useful as a practical guideline in carrying, out e-Transformation of the typical traditional firm and to provide significant basis for constructing the theoretical framework of e-Transformation methodology.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.57-66
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• 2023

In this study, we propose a novel research framework for the recommendation system that can estimate the user's emotional state and reflect it in the recommendation process by applying deep learning techniques and emotion AI (artificial intelligence). To this end, we build an emotion classification model that classifies each of the seven emotions of angry, disgust, fear, happy, sad, surprise, and neutral, respectively, and propose a model that can reflect this result in the recommendation process. However, in the general emotion classification data, the difference in distribution ratio between each label is large, so it may be difficult to expect generalized classification results. In this study, since the number of emotion data such as disgust in emotion image data is often insufficient, correction is made through augmentation. Lastly, we propose a method to reflect the emotion prediction model based on data through image augmentation in the recommendation systems.

• Journal of Digital Convergence
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• v.17 no.6
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• pp.133-144
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• 2019

The purpose of this study is to empirically examine the ICT legal system and the ICT performance by new technology's investment for government organizational changes. I will show the impact of government ICT investment interest, competency, convergence and process change, and then present policy direction. A research method used the structural equations. As a result of analysis, ICT investment interest and operational competency showed the negative impact the ICT legal system and the role change of ICT process and convergence of new technologies showed the positive impact. The Framework Act on National Information showed the positive impact on organizational performance, but the E-Government Act showed the negative impact. The contribution in the study expanded organization research from MIS perspective, and each organization is required the conflict resolve by ICT investment. A future study will require longitudinal study of ICT capabilities from previous to present government.

• Journal of the Economic Geographical Society of Korea
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• v.13 no.2
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• pp.219-233
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• 2010

In this study, the possibility of the application of knowledge management concept in a unit of regional industrial cluster is explored based on diverse case studies. For this purpose, a new framework for knowledge management strategies in an industrial cluster was developed and a model of Knowledge Hub was suggested for the support of integrated knowledge management in an industrial cluster. Additionally, characteristics of Knowledge Hub that should be considered in the design of the Hub are discussed. The concept of Knowledge Hub in this study could be particularly useful for the promotion of innovations in linking clusters and provincial industrial clusters.

• Journal of Biomedical Engineering Research
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• v.31 no.2
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• pp.134-140
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• 2010

Readily available high resolution brain MRI scans allow detailed visualization of the brain structures. Researchers have focused on developing methods to quantify shape differences specific to diseased scans. We have developed a novel method to quantify shape information for a specific population based on Multidimensional scaling(MDS). MDS is a well known tool in statistics and here we apply this classical tool to quantify shape change. Distance measures are required in MDS which are computed from pair-wise image registrations of the training set. Registration step establishes spatial correspondence among scans so that they can be compared in the same spatial framework. One benefit of our method is that it is quite robust to errors in registrations. Applying our method to 13 brain MRI showed clear separation between normal and diseased (Cushing's syndrome). Intentionally perturbing the image registration results did not significantly affect the separability of two clusters. We have developed a novel method to quantify shape based on MDS, which is robust to image mis-registration.

• Proceedings of the Korea Association of Information Systems Conference
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• 2004.11a
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• pp.339-348
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• 2004

There were few empirical researches on the ERP system in spite of increasing interest on ERP system recently Therefore, the purpose of this study is to develop an framework for the marketing performance based on evaluation of ERP system used by the theoretical model for evaluation of information system and the empirical data of Korean firms. This study was empirically examined an effect of ERP system and its information quality on the use of information from ERP system. And another effect of information use from ERP system on marketing performances survey on MIS was conducted for the marketing strategy. In this study, the structural equation model was established for verifying the relationship of above variables. And several hypotheses were established and examined empirically from that structural equation model. In conclusion, the effectiveness of ERP system must be evaluated within structural relationship among ERP system and its information quality, the use of information from ERP, and marketing performances. Additionally, the result of this research has founded the fact that ERP system and its information quality are important as elements of successful ERP system.