• The KIPS Transactions:PartD
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• v.18D no.2
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• pp.133-142
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• 2011

To ensure the successful employment of semantic web services, it is essential that they rely on the use of high quality ontologies. However, building such ontologies is difficult and costly, thus hampering web service deployment. This study automatically builds ontologies from WSDL documents and their underlying semantics, and presents discovery methods of similar web service operations using these ontologies. The key ingredient is techniques that cluster parameters in the collection of web services into semantically meaningful concepts, and capture the hierarchical relationships between the words contained in the tag. We implement an operation retrieval system for web services. This system finds out a ranked set of similar operations using a novel similarity measurement method, and selects the most optimal operation which satisfies user's requirements. It can be directly used for the web services composition.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.6
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• pp.346-353
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• 2002

The explosive increase of World Wide Web usage has incurred significant amount of network traffic and server load. To overcome these problems, web proxy caching replicates frequently requested documents in the web proxy closer to the users. Cache utilization depends on the replacement policy which tries to store frequently requested documents in near future. Temporal locality and Zipf frequency distribution, which are commonly observed in web proxy workloads, are considered as the important properties to predict the popularity of documents. In this paper, we propose a novel cache replacement policy, called Adaptive LFU (ALFU), which incorporates 1) Zipf frequency distribution by utilizing LFU and 2) temporal locality adaptively by measuring the amount of the popularity reduction of documents as time passed efficiently. We evaluate the performance of ALFU by comparing it to other policies via trace-driven simulation. Experimental results show that ALFU outperforms other policies.

• Cartoon and Animation Studies
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• s.45
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• pp.259-282
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• 2016

Documentaries are being recreated into a new genre and the Web Documentary is the typical case. Web Documentaries are the documentaries those comprise creators and users and they are the novel type of text that the interaction with users is absolute. In this research, two Web Documentaries and are analyzed for examining how Web utilizes its features as expressive media inducing users to experience reality. Web Documentaries have dual and spatial structure that allows user interaction and make users to face with various information and knowledge about reality by its encyclopedic characteristics. Also, Web Documentaries give the role of progressing documentary and expanding text to users and that is, they stimulate users' consciousness reminding that they are the ones who explore through reality. In this process, users of Web Documentaries get potentiality of critically examining the reality suggested by documentaries and grasping the meanings beneath it. These features make Web Documentaries special contrast to traditional documentaries not only with their way of pursuing the reality but also with their meanings. This makes the innovative position of Web Documentaries phenomenon clear, issuing the necessity of the discussion about Web Documentaries more strongly. Web Documentaries are not just new media technological phenomenon, and they have their significance as a fundamental challenge toward traditional documentaries.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.1
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• pp.71-79
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• 2007

Recent advances in the internet infrastructure have resulted in a large number of huge Web sites and portals worldwide. These Web sites are being visited by various types of users in many different ways. Among all the web page access sequences from different users, some of them occur so frequently that may need an attention from those who are interested. We call them frequent access patterns and access sequences that can be frequent the candidate patterns. Since these candidate patterns play an important role in the incremental Web mining, it is important to efficiently generate, add, delete, and search for them. This thesis presents a novel tree structure that can efficiently store the candidate patterns and a related set of algorithms for generating the tree structure, adding new patterns, deleting unnecessary patterns, and searching for the needed ones. The proposed tree structure has a kind of the 3 dimensional link structure and its nodes are layered.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.141-148
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• 2013

Automated classification systems which utilize machine learning develops classification models through learning process, and then classify unknown data into predefined set of categories according to the model. The performance of machine learning-based classification systems relies greatly upon the quality of features composing classification models. For textual data, we can use their word terms and structure information in order to generate the set of features. Particularly, in order to extract feature from Web documents, we need to analyze tag and hyperlink information. Recent studies on Web document classification focus on feature engineering technology other than machine learning algorithms themselves. Thus this paper proposes a novel method of incorporating feature selection and weighting which can improves classification models effectively. Through extensive experiments using Web-KB document collections, the proposed method outperforms conventional ones.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.607-610
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• 2008

Phishing is a form of online identity theft that aims to steal sensitive information such as online banking passwords and credit card information from users. Phishing scams have been receiving extensive press coverage because such attacks have been escalating in number and sophistication. According to a study by Gartner, Many Internet users have identified the receipt of e-mail linked to phishing scams and about 2 million of them are estimated to have been tricked into giving away sensitive information. This paper presents a novel browser extension, AntiPhish, that aims to protect users against spoofed web site-based phishing attack.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.4
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• pp.270-276
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• 2012

With the increase in amount of data and information available on the web, there have been high demands on personalized information retrieval services to provide context-aware services for the web users. This paper proposes a novel dynamic multi-agent context-awareness user profile construction method based on ontology to incorporate concepts and properties to model the user profile. This method comprehensively considers the frequency and the specific of the concept in one document and its corresponding domain ontology to construct the user profile, based on which, a fuzzy c-means clustering method is adopted to cluster the user's interest domain, and a dynamic update policy is adopted to continuously consider the change of the users' interest. The simulation result shows that along with the gradual perfection of the our user profile, our proposed system is better than traditional semantic based retrieval system in terms of the Recall Ratio and Precision Ratio.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1373-1387
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• 2012

Contextual advertising is an important revenue source for major service providers on the Web. Ads classification is one of main tasks in contextual advertising, and it is used to retrieve semantically relevant ads with respect to the content of web pages. However, it is difficult for traditional text classification methods to achieve satisfactory performance in ads classification due to scarce term features in ads. In this paper, we propose a novel ads classification method that handles the lack of term features for classifying ads with short text. The proposed method utilizes a vocabulary expansion technique using semantic associations among terms learned from large-scale search query logs. The evaluation results show that our methodology achieves 4.0% ~ 9.7% improvements in terms of the hierarchical f-measure over the baseline classifiers without vocabulary expansion.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.215-217
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• 2016

With the popularity of smart device, mobile applications are playing more and more important role in people's daily life, these applications stores various information which greatly facilitate the user's daily life. However due to the frequent transmission of data in the network also increases the risk of data leakage, more and more developers began to focus on how to protect user data. Current mainstream development models include Native development, Web development and Hybrid development. Hybrid development is based on JavaScript and HTML5, it has a cross platform advantages similar to Web Apps and a good user experience similar to Native Apps. In this paper according to the features of Hybrid applications, we proposed a security scheme in Hybrid development model implements message-level data encryption to protect user information. And through the performance evaluation we found that in some scenario the proposed security scheme has a better performance.

• Journal of the Korea Society of Computer and Information
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• v.22 no.6
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• pp.57-68
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• 2017

The Linked Open Data(LOD) cloud is quickly becoming one of the largest collections of interlinked datasets and the de facto standard for publishing, sharing and connecting pieces of data on the Web. Data publishers from diverse domains publish their data using Resource Description Framework(RDF) data model and provide SPARQL endpoints to enable querying their data, which enables creating a global, distributed and interconnected dataspace on the LOD cloud. Although it is possible to extract structured data as query results by using SPARQL, users have very poor in analysis and visualization of RDF data from SPARQL query results. Therefore, to tackle this issue, based on Formal Concept Analysis, we propose a novel approach for analyzing and visualizing useful information from the LOD cloud. The RDF data analysis and visualization technique proposed in this paper can be utilized in the field of semantic web data mining by extracting and analyzing the information and knowledge inherent in LOD and supporting classification and visualization.