• Journal of Korean Library and Information Science Society
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• v.11
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• pp.69-99
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• 1984

A dramatic development in the new technology of copying materials has presented us with massive problems on reconciling the conflicts between copyright owners and potential users of copyrighted materials. The adaptation to this changing condition led some countries to revise their copyright laws such as in the U. S. in 1976 and in Korea in 1984 for merging with the international or universal copyright conventions in the future. Copyright defined as exclusive rights given to copyright owners aims to secure a fair return for an author's creative labor and to stimulate artistic creativity for the general public good. The exclusive rights on copyrightable matters, generally for reproduction, preparation of derivative works, public distribution, public performance, and public display, are limited by fair use for scholarship and criticism and by library reproduction for its preservation and interlibrary loan. These limitations on the exclusive rights are concerned with all aspects of library services and cause a great burden on librarian's daily duty to provide balance between the rights of creators and the needs of library patrons. The fair use as one of the limitations on it has been coupled with enormous growth of a new technology and extended from xerography to online database systems. The implementation of the fair use and library reprography in Korean law to the local practices is examined on the basis of the new American copyright act of 1976. Under the draft revision of Korean law, librarians will face many potential problems as summarized below. 1. Because the new provision of 'life time plus 50 years' will tie up substantial bodies of material longer than the old law, until that date librarians would need permissions from the owners and should pay attention to the author's death date. 2. Because the copyright can be sold, distributed, given to the heirs, donated, as a whole or a part, librarians should chase down the heirs and other second owners. In case of a derivative work, this is a real problem. 3. Since a work has its protection from the moment of its creation, the coverage of copyrightable matter would be extended to the published or the unpublished works and librarian's work load would be heavier. Without copyright registration, no one can be certain that a work is in the public domain. Therefore, librarians will need to check with an authority. 4. For implementation of limitations on exclusive rights, fair use and library reproduction for interlibrary loan, there can be no substantial aggregate use and there can be no systematic distribution of multicopies. Therefore, librarians should not substitute reproductions for subscriptions or purchases. 5. For the interlibrary loan by photocopying, librarians should understand the procedure of royalty payment. 6. Compulsory licenses should be understood by librarians. 7. Because the draft revision of Korean law is a reciprocal treaty, librarians should take care of other countries' copyright law to protect foreign authors from Korean law. In order to solve the above problems, some suggestions are presented below. 1. That copyright clearinghouse or central agency as a centralized royalty payment mechanism be established. 2. That the Korean Library Association establish a committee on copyright. 3. That the Korean Library Association propose guidelines for each occasion, e.g. for interlibrary loan, books and periodicals and music, etc. 4. That the Korean government establish a copyright office or an official organization for copyright control other than the copyright committee already organized by the government. 5. That the Korean Library Association establish educational programs on copyright for librarians through seminars or articles written in its magazines. 6. That individual libraries provide librarian's copyright kits. 7. That school libraries distribute subject bibliographies on copyright law to teachers. However, librarians should keep in mind that limitations on exclusive rights are not for an exemption from library reprography but as a convenient access to library resources.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.109-122
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• 2016

Recently, big data analysis has developed into a field of interest to individuals and non-experts as well as companies and professionals. Accordingly, it is utilized for marketing and social problem solving by analyzing the data currently opened or collected directly. In Korea, various companies and individuals are challenging big data analysis, but it is difficult from the initial stage of analysis due to limitation of big data disclosure and collection difficulties. Nowadays, the system improvement for big data activation and big data disclosure services are variously carried out in Korea and abroad, and services for opening public data such as domestic government 3.0 (data.go.kr) are mainly implemented. In addition to the efforts made by the government, services that share data held by corporations or individuals are running, but it is difficult to find useful data because of the lack of shared data. In addition, big data traffic problems can occur because it is necessary to download and examine the entire data in order to grasp the attributes and simple information about the shared data. Therefore, We need for a new system for big data processing and utilization. First, big data pre-analysis technology is needed as a way to solve big data sharing problem. Pre-analysis is a concept proposed in this paper in order to solve the problem of sharing big data, and it means to provide users with the results generated by pre-analyzing the data in advance. Through preliminary analysis, it is possible to improve the usability of big data by providing information that can grasp the properties and characteristics of big data when the data user searches for big data. In addition, by sharing the summary data or sample data generated through the pre-analysis, it is possible to solve the security problem that may occur when the original data is disclosed, thereby enabling the big data sharing between the data provider and the data user. Second, it is necessary to quickly generate appropriate preprocessing results according to the level of disclosure or network status of raw data and to provide the results to users through big data distribution processing using spark. Third, in order to solve the problem of big traffic, the system monitors the traffic of the network in real time. When preprocessing the data requested by the user, preprocessing to a size available in the current network and transmitting it to the user is required so that no big traffic occurs. In this paper, we present various data sizes according to the level of disclosure through pre - analysis. This method is expected to show a low traffic volume when compared with the conventional method of sharing only raw data in a large number of systems. In this paper, we describe how to solve problems that occur when big data is released and used, and to help facilitate sharing and analysis. The client-server model uses SPARK for fast analysis and processing of user requests. Server Agent and a Client Agent, each of which is deployed on the Server and Client side. The Server Agent is a necessary agent for the data provider and performs preliminary analysis of big data to generate Data Descriptor with information of Sample Data, Summary Data, and Raw Data. In addition, it performs fast and efficient big data preprocessing through big data distribution processing and continuously monitors network traffic. The Client Agent is an agent placed on the data user side. It can search the big data through the Data Descriptor which is the result of the pre-analysis and can quickly search the data. The desired data can be requested from the server to download the big data according to the level of disclosure. It separates the Server Agent and the client agent when the data provider publishes the data for data to be used by the user. In particular, we focus on the Big Data Sharing, Distributed Big Data Processing, Big Traffic problem, and construct the detailed module of the client - server model and present the design method of each module. The system designed on the basis of the proposed model, the user who acquires the data analyzes the data in the desired direction or preprocesses the new data. By analyzing the newly processed data through the server agent, the data user changes its role as the data provider. The data provider can also obtain useful statistical information from the Data Descriptor of the data it discloses and become a data user to perform new analysis using the sample data. In this way, raw data is processed and processed big data is utilized by the user, thereby forming a natural shared environment. The role of data provider and data user is not distinguished, and provides an ideal shared service that enables everyone to be a provider and a user. The client-server model solves the problem of sharing big data and provides a free sharing environment to securely big data disclosure and provides an ideal shared service to easily find big data.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.