• 한국전자거래학회:학술대회논문집
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• 한국전자거래학회 2005년도 e-Biz World Conference 2005
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• pp.241-246
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• 2005

• 한국중재학회지:중재연구
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• 제20권2호
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• pp.67-90
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• 2010

Electronic discovery refers to the discovery of electronically stored information. The differences between producing paper documents and electronic information can be categorized into seven groups: massive volume, persistence, dynamic and changeable contents, metadata, environment-dependence, dispersion and searchability. Since these differences make the discovery more expensive and less expeditious, it is necessary to limit the scope of discovery. Accordingly, a number of arbitration institutions have already introduced rules, guidelines or protocols on electronic discovery. ICDR guidelines take a minimal approach and address only the proper form of electronic document. CIArb Protocol is intended to act as a checklist for discovery of electronic data. CPR Protocol offers four modes of discovery of electronic documents ranging from minimal to extensive among which the parties may choose the way of electronic discovery. IBA Rules on Evidence and ICC Rules are silent on the issue of electronic discovery, however, working parties of the ICC are considering updates to the rules to deal with electronic discovery. It is disputed whether rules, guidelines or protocols on electronic discovery is necessary or appropriate. Although some have suggested that existing rules can make adequate provision for electronic discovery, it is more desirable to prepare new rules, guidelines or protocols to make arbitrators and counsels be familiar with electronic discovery process, to provide an adequate standard for electronic discovery and to limit the time and cost of electronic discovery. Such rules on electronic discovery should include provisions regarding the form of electronic document production, conference between parties regarding electronic discovery, keyword search, bearing the expenses to reduce disputes over electronic discovery.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.172-178
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• 2023

When the mobile device moves from the coverage of one access point to the radio coverage of another access point it needs to maintain its connection with the current access point before it successfully discovers the new access point, this process is known as handoff. During handoff the acceptable delay a voice over IP application can bear is of 50ms whereas the delay on medium access control layer is high enough that goes up to 350-500ms. This research provides a suitable methodology on medium access control layer of the IEEE 802.11 network. The medium access control layer comprises of three phases, namely discovery, reauthentication and re-association. The discovery phase on medium access control layer takes up to 90% of the total handoff latency. The objective is to effectively reduce the delay for discovery phase to ensure a seamless handoff. The research proposes a scheme that reduces the handoff latency effectively by scanning channels prior to the actual handoff process starts and scans only the neighboring access points. Further, the proposed scheme enables the mobile device to scan first the channel on which it is currently operating so that the mobile device has to perform minimum number of channel switches. The results show that the mobile device finds out the new potential access point prior to the handoff execution hence the delay during discovery of a new access point is minimized effectively.

• 한국IT서비스학회지
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• 제10권2호
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• pp.95-117
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• 2011

Today, IT governance has also become a subject of attention along with recent technologies such as ITSM (IT Service Management), PPM (Project Portfolio Management) and Compliance. At the national level, the market is fairly recent. and therefore, lacks detailed research in the field. Models specifically related to EIM has not yet been presented to this day, hence, firms that are considering EIM as a potential part of their information management system may fall into a state of disorder in the process of its implementation. To this end, this research attempts to construct an IT governance model for EIM based on existing models, surveys and interviews. In particular, E-discovery has been applied as means of protecting information assets and its use as evidence. In addition, by applying the research model to a particular global firm and then assessing its documentation management system, the overall feasibility of the research model has been tested.

• 한국전자거래학회:학술대회논문집
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• 한국전자거래학회 2004년도 e-Biz World Conference
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• pp.112-116
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• 2004

· Dynamic Pricing vs. Fixed Pricing Auctions make both buyers and sellers engage in the price discovery process, Auctions of various kinds will replace the fixed pricing model that now pervades much of the web(pmitted)

• 지능정보연구
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• 제29권2호
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• pp.285-301
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• 2023

비대면 교육의 중요성 및 필요에 따른 수요가 증가함에 따라 국내외 온라인 교육 오픈 플랫폼이 활성화되고 있다. 본 플랫폼은 대학 등 교육 전문기관과 달리 학습자의 자율성이 높은 특징을 가지며 이에 따라 개인화된 학습 도구를 지원하기 위한 학습 행동 데이터의 분석 연구가 중요시 되고 있다. 실제적인 학습 행동을 이해하고 패턴을 도출하기 위하여 프로세스 마이닝이 다수 활용되었지만 온라인 교육 플랫폼과 같이 자기 관리형(Self-regulated) 환경에서의 학습 로그를 기반한 사례는 부족하다. 또한, 대부분 프로세스 모델 도출 등의 모델 관점에서의 접근이며 분석 결과의 실제적인 적용을 위한 개별 패턴 및 인스턴스 관점에서의 방법 제시는 미흡하다. 본 연구에서는 온라인 교육 오픈 플랫폼 내 학습 패턴을 파악하기 위하여 프로세스 마이닝을 활용한 분석 방법을 제시한다. 학습 패턴을 다각도로 분석하기 위하여 모델, 패턴, 인스턴스 관점에서의 분석 방법을 제시하며, 프로세스 모델 발견, 적합도 검사, 군집화 기법, 예측 알고리즘 등 다양한 기법을 활용한다. 본 방법은 국내 오픈 교육 플랫폼 내 기계학습 관련 강좌의 학습 로그를 추출하여 분석하였다. 분석 결과 온라인 강의의 특성에 맞게 비구조화된 프로세스 모델을 도출할 수 있었으며 구체적으로 한 개의 표준 학습 패턴과 세 개의 이상 학습 패턴으로 세분화할 수 있었다. 또한, 인스턴스별 패턴 분류 예측 모델을 도출한 결과 전체 흐름 중 초기 30%의 흐름을 바탕으로 예측하였을 때 0.86의 분류 정확도를 보였다. 본 연구는 프로세스 마이닝을 활용하여 학습자의 패턴을 체계적으로 분석한다는 점에서 기여점을 가진다.

• Journal of Crop Science and Biotechnology
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• 제10권4호
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• pp.237-242
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• 2007

Genome duplication(i.e. polyploidy) is a common phenomenon in the evolution of plants. The objective of this study was to achieve a comprehensive understanding of genome duplication for SNP discovery by Thymine/Adenine(TA) cloning for confirmation. Primer pairs were designed from 793 EST contigs expressed in the roots of a supernodulating soybean mutant and screened between 'Pureunkong' and 'Jinpumkong 2' by direct sequencing. Almost 27% of the primer sets were failed to obtain sequence data due to multiple bands on agarose gel or poor quality sequence data from a single band. TA cloning was able to identify duplicate genes and the paralogous sequences were coincident with the nonspecific peaks in direct sequencing. Our study confirmed that heterogeneous products by the co-amplification of a gene family member were the main cause of obtaining multiple bands or poor quality sequence data in direct sequencing. Counts of amplified bands on agarose gel and peaks of sequencing trace suggested that almost 27% of nonrepetitive soybean sequences were present in as many as four copies with an average of 2.33 duplications per segment. Copy numbers would be underestimated because of the presence of long intron between primer binding sites or mutation on priming site. Also, the copy numbers were not accurately estimated due to deletion or tandem duplication in the entire soybean genome.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1997년도 추계학술대회발표논문집; 홍익대학교, 서울; 1 Nov. 1997
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• pp.181-184
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• 1997

Practical applications of learning systems usually involve complex domains exhibiting nonlinear behavior and dilution by noise. Consequently, an intelligent system must be able to adapt to nonlinear processes as well as probabilistic phenomena. An important class of application for a knowledge based systems in prediction: forecasting the future trajectory of a process as well as the consequences of any decision made by e system. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes in the form of chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a Henon process in the presence of various patterns of noise.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2014년도 추계학술발표대회
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• pp.849-852
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• 2014

Reverse engineering of gene regulatory network is a challenging task in computational biology. To detect a regulatory relationship among genes from time series data is called reverse engineering. Reverse engineering helps to discover the architecture of the underlying gene regulatory network. Besides, it insights into the disease process, biological process and drug discovery. There are many statistical approaches available for reverse engineering of gene regulatory network. In our paper, we propose pairwise mutual information for the reverse engineering of a gene regulatory network from time series data. Firstly, we create random boolean networks by the well-known $Erd{\ddot{o}}s-R{\acute{e}}nyi$ model. Secondly, we generate artificial time series data from that network. Then, we calculate pairwise mutual information for predicting the network. We implement of our system on java platform. To visualize the random boolean network graphically we use cytoscape plugins 2.8.0.

• 한국IT서비스학회지
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• 제16권3호
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.