• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.759-762
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• 2006

Historically, internet is the fastest growing media together with the ICT development and the key of the development is contents. Digital contents indicate information which covers voice, DB, game, publications and music etc. and the areas have been creating new technology business opportunities. The value chain of digital contents consists of production, collection, processing, services, connection and navigation and is expected to be reorganized around business players of production and distribution areas. This paper presents on those changes occurring in business environment and examples of business models, and further provides industries and academias with technology commercialization strategies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2781-2800
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• 2016

Emerging attacks aim to access proprietary assets and steal data for business or political motives, such as Operation Aurora and Operation Shady RAT. Skilled Intruders would likely remove their traces on targeted hosts, but their network movements, which are continuously recorded by network devices, cannot be easily eliminated by themselves. However, without complete knowledge about both inbound/outbound and internal traffic, it is difficult for security team to unveil hidden traces of intruders. In this paper, we propose an autonomous anomaly detection system based on behavior profiling and relation mining. The single-hop access profiling model employ a novel linear grouping algorithm PSOLGA to create behavior profiles for each individual server application discovered automatically in historical flow analysis. Besides that, the double-hop access relation model utilizes in-memory graph to mine time-sequenced access relations between different server applications. Using the behavior profiles and relation rules, this approach is able to detect possible anomalies and violations in real-time detection. Finally, the experimental results demonstrate that the designed models are promising in terms of accuracy and computational efficiency.

• The Journal of Information Systems
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• v.13 no.1
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• pp.121-134
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• 2004

As Internet usage widely grows, online content services such as newspaper, magazine, music, game and movie are provided with a fee-based subscription. Many content services providers consider charging a usage fee into its service provisions as one of the Internet business models for increasing revenue. There are customer resistances to adopting the fee-based service provision on the Web. Previous research in information systems (IS)has focused on the analysis of adoption of information technology or systems in the individual ororganization level. No principle research has been carried out on the user adoption behavior of online content services provisions. As users actively access content services on the Web, it needs to explore user adoption behavior in different settings. Many IS researcher have employedquantitative approaches, even though they deal with the process of user behavior regarding the information technology or system. In this study, we attempt to discover how customers adopt the fee-based provision of online content services by employing grounded theory, one of the principal qualitative research methods.

• Asia pacific journal of information systems
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• v.12 no.4
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• pp.173-192
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• 2002

Internet-based on-line firms have focused on the development of new business models with an assumption that this new model would create their competitiveness. At the same time, off-line firms have opened new marketing channels on the internet in order to defend their position against on-line firms. Based on Porter's well-known generic strategy, this study compares between on-line firms and on-offline firms i) whether these two types of firms take different strategies among cost reduction, marketing differentiation, innovation differentiation, and focus and ii) how the performance of these two types of firms is affected by different strategy types. The result shows that on-offline firms prefer the strategy of marketing differentiation and innovative differentiation while the strategy of cost reduction and focus are taken without significant difference between online firms and on-offline firms. Also it is found that even though the strategy of marketing differentiation and innovation differentiation are more preferred by on-offline firms than on-line firms, these two strategy types have a significant influence on the on-line firms' performance while the focus strategy has a significant influence on the on-offline firms' performance. Other managerial implications are discussed.

• Asia pacific journal of information systems
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• v.8 no.2
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• pp.53-67
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• 1998

The explosive growth of the Web is providing end-users access to ever-increasing volumes of information. The resources of legacy systems and relational databases have also been made available to the Web browser, which has become an essential business tool. Recently, model management on the Internet/Web is also proposed with its conceptual design or prototypical system like DecisionNet and DSS Web. However, they are also suffering from the same symptoms as the Web, Although we can identify the elements of a page with HTML tags and (declare) the relationships among the various document elements, they are semantically opaque to computer systems and have no domain-specific meaning. However, HTML is not extensible, so developers are forced to invent convoluted, non-standard solutions for embedding and parsing data. Extensible Markup Language (XML) is a simplified subset of SGML that has many benefits for folks who want to improve structure, maintainability, searchability, presentation, and other aspects of their document management. This paper proposes a structured markup language for model representation and management on the Web as an XML application. The language is based on a conceptual modeling framework, Object-Oriented Structured Modeling (OOSM), which is an extension of the structured modeling.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.2
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• pp.25-30
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• 2018

This paper is about wine quality classification with multilayer perceptron using the deep neural network. Wine complexity is an issue when predicting the quality. And the deep neural network is considered when using complex dataset. Wine Producers always aim high to get the highest possible quality. They are working on how to achieve the best results with minimum cost and efforts. Deep learning is the possible solution for them. It can help them to understand the pattern and predictions. Although there have been past researchers, which shows how artificial neural network or data mining can be used with different techniques, in this paper, rather not focusing on various techniques, we evaluate how a deep learning model predicts for the quality using two different activation functions. It will help wine producers to decide, how to lead their business with deep learning. Prediction performance could change tremendously with different models and techniques used. There are many factors, which, impact the quality of the wine. Therefore, it is a good idea to use best features for prediction. However, it could also be a good idea to test this dataset without separating these features. It means we use all features so that the system can consider all the feature. In the experiment, due to the limited data set and limited features provided, it was not possible for a system to choose the effective features.

• International Commerce and Information Review
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• v.6 no.2
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• pp.193-216
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• 2004

The management strategies of each company has been changed fundamentally owing to the emergence of digital economics by using internet. Therefore the efficient management of global e-port causes the main issues not only effect the survival and growth of ports but also exert new opportunities and fatal threats on them. Under the circumstances of these change, the Kwangyang port have to introduce e-biz for the purpose of increasing the level of their competition. The focus of this article is to suggest some strategies on the implementation of Kwangyang Global e-port in Korea. To analyse the e-port realities, I first reviewed the trends of e-marketing and e-biz. I chose and analysed website of four ports like Hongkong, Singapore, Antwerp and Hamburg as successful global e-port models. This article is focused to suggest the theoretical background by analysing the strategic points of Kwangyang global e-port which are divided in 6C: Contents, Communities, Connection, Commerce, Communication and Customization. This paper analyses many problems of Gwangyang port and presents various develpment ways to activate Gwangyang port. In order to be a global e-port, first of all Gwangyang port must improve global web-site by 6C. And also Gwangyang port have to constitute logistics hub site, create infrastructure needed to run electronic business more easily over the internet, establish nationwide network of industries, build up marine and port logistics information system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.540-557
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• 2021

Privacy vulnerability of social networks is one of the major concerns for social science research and business analysis. Most existing studies which mainly focus on un-weighted network graph, have designed various privacy models similar to k-anonymity to prevent data disclosure of vertex attributes or relationships, but they may be suffered from serious problems of huge information loss and significant modification of key properties of the network structure. Furthermore, there still lacks further considerations of privacy protection for important sensitive edges in weighted social networks. To address this problem, this paper proposes a privacy preserving method to protect sensitive weighted edges. Firstly, the sensitive edges are differentiated from weighted edges according to the edge betweenness centrality, which evaluates the importance of entities in social network. Then, the perturbation operations are used to preserve the privacy of weighted social network by adding some pseudo-edges or modifying specific edge weights, so that the bottleneck problem of information flow can be well resolved in key area of the social network. Experimental results show that the proposed method can not only effectively preserve the sensitive edges with lower computation cost, but also maintain the stability of the network structures. Further, the capability of defending against malicious attacks to important sensitive edges has been greatly improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4108-4122
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• 2019

Workflow process mining is becoming a more and more valuable activity in workflow-supported enterprises, and through which it is possible to achieve the high levels of qualitative business goals in terms of improving the effectiveness and efficiency of the workflow-supported information systems, increasing their operational performances, reducing their completion times with minimizing redundancy times, and saving their managerial costs. One of the critical challenges in the workflow process mining activity is to devise a reasonable approach to discover and recognize the bottleneck points of workflow process models from their enactment event histories. We have intuitively realized the fact that the iterative process pattern of redo-activities ought to have the high possibility of becoming a bottleneck point of a workflow process model. Hence, we, in this paper, propose an algorithmic approach and its implementation to discover the redo-activities and their performers' involvements patterns from workflow process enactment event logs. Additionally, we carry out a series of experimental analyses by applying the implemented algorithm to four datasets of workflow process enactment event logs released from the BPI Challenges. Finally, those discovered redo-activities and their performers' involvements patterns are visualized in a graphical form of information control nets as well as a tabular form of the involvement percentages, respectively.