• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.397-404
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• 2016

Illegal and harmful contents on the Internet has been an issue and been increased in Korea. They are often posted on the billboard and website of small enterprise and government office. Those illegal and harmful contents can relate to crime and suspicious activity, so, we need a detection system. However, to date the detection itself has been conducted manually by a person. In this paper, we develop an automated URL detection scheme for detecting a drug trafficking by using Google. This system works by analyzing the frequently used keywords in a drug trafficking and generate a keyword dictionary to store words for future search. The suspected drug trafficking URL are automatically collected based on the keyword dictionary by using Google search engine. The suspicious URL can be detected by classifying and numbering each domain from the collection of the suspected URL. This proposed automated URL detection can be an effective solution for detecting a drug trafficking, also reducing time and effort consumed by human-based URL detection.

• Knowledge Management Research
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• v.22 no.2
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• pp.55-76
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• 2021

The real estate market is a representative imperfectly competitive market. Real estate information is characterized by being collected and utilized in a closed environment, and market participants have to pay a lot of time, effort, and costs to acquire such information. Korea's real estate public data is increasing year by year, but it is scattered by relevant ministries. So it is difficult to search and analyze, and the level of development of the industry using it is low. In the recent 4th industrial revolution, the proptech industry has developed as an industry to efficiently provide necessary information to the real estate market. In this study, based on the case of major companies in the real estate platform field among proptech companies, we looked at the types of information provided to users, and on the contrary, explored ways to utilize the data collected from users. The results of this study are expected to provide theoretical and practical implications for ways to reduce information asymmetry in the real estate market and contribute to the development of the real estate industry.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.5
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• pp.789-809
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• 2020

Growth in online channels has created fierce competition; consequently, retailers have to invest an increasing amount of effort into attracting consumers. In this study, eye-tracking technology examined consumers' visual behavior to gain an understanding of information searching behavior in exploring product information for fashion products. Product attribute information was classified into two image-based elements (model image information and detail image information) and two text-based elements (basic text information, detail text information), after which consumers' visual behavior for each information element was analyzed. Furthermore, whether involvement affects consumers' information search behavior was investigated. The results demonstrated that model image information attracted visual attention the quickest, while detail text information and model image information received the most visual attention. Additionally, high-involvement consumers tended to pay more attention to detailed information while low-involvement consumers tended to pay more attention to image-based and basic information. This study is expected to help broaden the understanding of consumer behavior and provide implications for establishing strategies on how to efficiently organize product information for online fashion stores.

• Journal of the Korean Society for Library and Information Science
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• v.32 no.4
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• pp.53-67
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• 1998

This article investigates the potentialities of using empirical variables and their associated statistical relationships in document representation and retrieval. To this end, a newly devised empirical fact retrieval system was evaluated in comparison to a simulated traditional retrieval system involving a set of predetermined empirical queries. Results indicate that the EFRS generally outperformed the TRS in terms of the precision, search effort, and measures of user satisfaction.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.767-772
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• 2005

The Internet is a packet switched network which offers best-effort service, but current IP network provide enhanced services such Quality of Services, Virtual Private Network (VPN) services, Distribute Firewall and IP Security Gateways. All such services need packet classification for determining the flow. The problem is performing scalable packet classification at wire speeds even as rule databases increase in size. Therefore, this research offer packet classification algorithm that increase classifier performance when working with enlarge rules database by rearrange rule structure into Bitmap Intersection Lookup (BIL) tables. It will use packet's header field for looking up BIL tables and take the result with intersection operation by logical AND. This approach will use simple algorithm and rule structure, it make classifier have high search speed and fast updates.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.51-69
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• 2002

In the supply chain environment on the web, collaborative problem solving and case-based modeling has been getting more important, because it is difficult to cope with diverse problem requirements and inefficient to manage many models as well. Hence, the approach on case-based modeling is required. This paper provides a framework that generates a goal model based on multiple cases, modeling knowledge, and forward chaining and it also develops a search algorithm through sensitivity analysis to reduce the modeling effort.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.2
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• pp.27-34
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• 2017

Highly informative admissible heuristics can help to conduct more efficient search for optimal solutions. However, in general, more informative ones of heuristics from planning problems requires lots of computational effort. To address this problem, we propose an Delete Relaxation based Action Costs-based Planning Graph(ACPG) and Action Costs-based Heuristics for solving optimal planning problems more efficiently. The ACPG is an extended one to be applied to can find action costs between subgoal & goal conditions from the Relaxed Planning Graph(RPG) which is a common means to get heuristics for solving the planning problems, Action Costs-based Heuristics utilizing ACPG can find action costs difference between subgoal & goal conditions in an effective way, and then consider them to estimate the goal distance. In this paper, we present the heuristics algorithm to compute Action Costs-based Heuristics, and then explain experimental analysis to investigate the efficiency and the accuracy of the Action Costs-based Heuristics.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.189-195
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• 2018

With the advent of the digital age, production and distribution of web pages has been exploding. Internet users frequently need to extract specific information they want from these vast web pages. However, it takes lots of time and effort for users to find a specific information in many web pages. While search engines that are commonly used provide users with web pages containing the information they are looking for on the Internet, additional time and efforts are required to find the specific information among extensive search results. Therefore, it is necessary to develop algorithms that can automatically extract specific information in web pages. Every year, thousands of international conference are held all over the world. Each international conference has a website and provides general information for the conference such as the date of the event, the venue, greeting, the abstract submission deadline for a paper, the date of the registration, etc. It is not easy for researchers to catch the abstract submission deadline quickly because it is displayed in various formats from conference to conference and frequently updated. This study focuses on the issue of extracting abstract submission deadlines from International conference websites. In this study, we use three machine learning models such as SVM, decision trees, and artificial neural network to develop algorithms to extract an abstract submission deadline in an international conference website. Performances of the suggested algorithms are evaluated using 2,200 conference websites.

• Journal of Information Science Theory and Practice
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• v.2 no.4
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• pp.61-76
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• 2014

The exponential growth of information on the web far exceeds the capacity of present day information retrieval systems and search engines, making information integration on the web difficult. In order to overcome this, semantic web technologies were proposed by the World Wide Web Consortium (W3C) to achieve a higher degree of automation and precision in information retrieval systems. Semantic web, with its promise to deliver machine understanding to the traditional web, has attracted a significant amount of research from academia as well as from industries. Semantic web is an extension of the current web in which data can be shared and reused across the internet. RDF and ontology are two essential components of the semantic web architecture which support a common framework for data storage and representation of data semantics, respectively. Ontologies being the backbone of semantic web applications, it is more relevant to study various approaches in their application, usage, and integration into web services. In this article, an effort has been made to review the research work being undertaken in the area of design and development of ontology supported information systems. This paper also briefly explains the emerging semantic web technologies and standards.

• Journal of Information Science Theory and Practice
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• v.1 no.3
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• pp.33-46
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• 2013

Despite limited success, today's information retrieval (IR) systems are not intelligent or reliable. IR systems return poor search results when users formulate their information needs into incomplete or ambiguous queries (i.e., weak queries). Therefore, one of the main challenges in modern IR research is to provide consistent results across all queries by improving the performance on weak queries. However, existing IR approaches such as query expansion are not overly effective because they make little effort to analyze and exploit the meanings of the queries. Furthermore, word sense disambiguation approaches, which rely on textual context, are ineffective against weak queries that are typically short. Motivated by the demand for a robust IR system that can consistently provide highly accurate results, the proposed study implemented a novel topic detection that leveraged both the language model and structural knowledge of Wikipedia and systematically evaluated the effect of query disambiguation and topic-based retrieval approaches on TREC collections. The results not only confirm the effectiveness of the proposed topic detection and topic-based retrieval approaches but also demonstrate that query disambiguation does not improve IR as expected.