• Journal of Environmental Impact Assessment
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• v.25 no.4
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• pp.296-306
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• 2016

There is a growing interest in establishing a regional climate change adaptation policy as the climate change impact in the region and local scale increases. This study focused on the analysis of 32 regions on its characteristics of local climate change adaptation plans. First, statistic program R was used for conducting cluster analysis based on the frequency and budgets of adaptation plan. Further, we analyzed damage frequency from newspapers regarding climate change impacts in eight categories which were caused by extreme weather events on 2,565 cases for 24 years. Lastly, the characteristics of climate change adaptation plan was compared with damage frequency patterns for evaluating the adequacy of climate change adaptation plan on each cluster. Four different clusters were created by cluster analysis. Most clusters clearly have their own characteristics on certain sectors. There was a high frequency of damage in 'disaster' and 'health' sectors. Climate change adaptation plan and budget also invested a lot on those sectors. However, when comparing the relative rate among regional governments, there was a difference between types of damage and climate change adaptation plan. We assumed that the difference could come from that each region established their adaptation plans based on not only the frequency of damage, but vulnerability assessment, and expert opinions as well. The result of study could contribute to policy making of climate change adaptation plan.

• Journal of Environmental Impact Assessment
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• v.27 no.1
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• pp.92-104
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• 2018

The loss of biodiversity has become a global issue. In order to cope with this problem, national biodiversity strategies and action plan (NBSAP) at national level as well as local biodiversity strategies and action plan (LBSAP) at local level have been established in many countries. In this study, we analyzed 8 domestic LBSAPs and 41 foreign LBSAPs through semantic network analysis to investigate the characteristics of domestic and foreign LBSAPs. The results showed that conservation and management were the most used keywords in both domestic and foreign LBSAPs but the ranking of other keywords used in vision, goal, strategy, and action plan sector was different. Thus, it has been found that there is a difference between domestic and foreign practical approaches to conservation and management of biodiversity. Results of the network analysis showed that the domestic network has a more detailed distributed network, while the foreign network has a more comprehensive and integrally configured dense network. These differences may be due to differences of threats to biodiversity, problem recognition, or differences in local circumstances. These results are expected to help establish LBSAP in other region or to assess the local roles to achieve the strategic goals of the Convention on Biological Diversity.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.321-334
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• 2020

The US Federal Reserve (Fed) has decided to cut interest rates. When we look at the expression of the FOMC statements at the time of policy change period we can understand that Fed has been communicating with markets through a change of word selection. However, there is a criticism that the method of analyzing the expression of the decision sentence through the context can be subjective and limited in qualitative analysis. In this paper, we evaluate the signaling effect of FOMC statements based on previous research. We analyze decision making characteristics from the viewpoint of text mining and try to predict future policy trend changes by capturing changes in expressions between statements. For this purpose, a decision tree and neural network models are used. As a result of the analysis, it can be judged that the discrepancy indicators between statements could be used to predict the policy change in the future and that the US Federal Reserve has systematically implemented policy signaling through the policy statements.

• Journal of KIISE:Software and Applications
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• v.28 no.10
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• pp.720-731
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• 2001

Complexity metrics have been developed for the structured paradigm of software development are not suitable for use with the object-oriented(OO) paradigm, because they do not support key object-oriented concepts such as inheritance, polymorphism. message passing and encapsulation. There are many researches on OO software metrics such as program complexity or design metrics. But metrics measuring the complexity of classes at the OO analysis phase are needed because they provide earlier feedback to the development project. and earlier feedback means more effective developing and less costly maintenance. In this paper, we propose the new metrics to measure the complexity of analysis classes which draw out in the analysis based on RUP(Rational Unified Process). By the collaboration complexity, is denoted by CC, we mean the maximum number of the collaborations can be achieved with each of the collaborator and determine the potential complexity. And the interface complexity, is denoted by IC, shows the difficulty related to understand the interface of collaborators each other. We verify theoretically the suggested metrics for Weyuker's nine properties. Moreover, we show the computation results for analysis classes of the system which automatically respond to questions of the user using the text mining technique. As a result of the comparison of CC and CBO and WMC suggested by Chidamber and Kemerer, the class that have highly the proposed metric value maintain the high complexity at the design phase too. And the complexity can be represented by CC and IC more than CBO and WMC. We can expect that our metrics may provide us the earlier feedback and hence possible to predict the efforts, costs and time required to remainder processes. As a result, we expect to develop the cost-effective OO software by reviewing the complexity of analysis classes in the first stage of SDLC(Software Development Life Cycle).

• The Journal of Society for e-Business Studies
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• v.25 no.4
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• pp.61-75
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• 2020

The scope of data for understanding or predicting stock prices has been continuously widened from traditional structured format data to unstructured data. This study investigates whether commentary data collected from SNS may affect future stock prices. From "Stock Discussion Room" in Naver, we collect 20 stocks' commentary data for six months, and test whether this data have prediction power with respect to one-hour ahead price direction and price range. Deep neural network such as LSTM and CNN methods are employed to model the predictive relationship. Among the 20 stocks, we find that future price direction can be predicted with higher than the accuracy of 50% in 13 stocks. Also, the future price range can be predicted with higher than the accuracy of 50% in 16 stocks. This study validate that the investors' sentiment reflected in SNS community such as Naver's "Stock Discussion Room" may affect the demand and supply of stocks, thus driving the stock prices.

• Journal of Korean Library and Information Science Society
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• v.51 no.3
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• pp.103-121
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• 2020

This study aimed to analyze the research trends of school libraries from 1990 to July 2020. To this end, LDA topic modeling analysis was conducted to the domestic article abstracts related to school libraries. The total number of documents is 498 papers published by the four major domestic journals in Library and Information Science. The log-likelihood estimate criterion was used to determine the number of topics for topic modeling. As a result of the study, 27 topics were discovered, then, theory were categorized by eight subject areas: general, institutional system, building/equipment, operation/management, data organization, service, education, and others. The most popular research was library utilization classes (T27) and Information Utilization (T2). More than 20 studies were found in each evaluation index development (T13), school librarian placement (T24), learning information media utilization (T3), community public library (T7), library cooperation (T9), library use (T17), library research (T11), reading education (T4), collection development (T5), and education effects/teaching methods (T18).

• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.295-306
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• 2016

The purpose of this paper is both to analyze research trends and to predict industrial flows using the meta-data from the previous studies on data quality. There have been many attempts to analyze the research trends in various fields till lately. However, analysis of previous studies on data quality has produced poor results because of its vast scope and data. Therefore, in this paper, we used a text mining, social network analysis for time series network analysis to analyze the vast scope and data of data quality collected from a Web of Science index database of papers published in the international data quality-field journals for 10 years. The analysis results are as follows: Decreases in Mathematical & Computational Biology, Chemistry, Health Care Sciences & Services, Biochemistry & Molecular Biology, Biochemistry & Molecular Biology, and Medical Information Science. Increases, on the contrary, in Environmental Sciences, Water Resources, Geology, and Instruments & Instrumentation. In addition, the social network analysis results show that the subjects which have the high centrality are analysis, algorithm, and network, and also, image, model, sensor, and optimization are increasing subjects in the data quality field. Furthermore, the industrial connection analysis result on data quality shows that there is high correlation between technique, industry, health, infrastructure, and customer service. And it predicted that the Environmental Sciences, Biotechnology, and Health Industry will be continuously developed. This paper will be useful for people, not only who are in the data quality industry field, but also the researchers who analyze research patterns and find out the industry connection on data quality.

• The Mathematical Education
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• v.59 no.1
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• pp.63-80
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• 2020

This study analyzed 3,114 articles published in KCI journals and 1,636 articles published in SSCI journals from 2000 to 2019 in order to compare domestic and international research trends of mathematics education using a topic modeling method. Results indicated that there were 16 similar research topics in domestic and international mathematics education journals: algebra/algebraic thinking, fraction, function/representation, statistics, geometry, problem-solving, model/modeling, proof, achievement effect/difference, affective factor, preservice teacher, teaching practice, textbook/curriculum, task analysis, assessment, and theory. Also, there were 7 distinct research topics in domestic and international mathematics education journals. Topics such as affective/cognitive domain and research trends, mathematics concept, class activity, number/operation, creativity/STEAM, proportional reasoning, and college/technology were identified from the domestic journals, whereas discourse/interaction, professional development, identity/equity, child thinking, semiotics/embodied cognition, intervention effect, and design/technology were the topics identified from the international journals. The topic related to preservice teacher was the most frequently addressed topic in both domestic and international research. The topic related to in-service teachers' professional development was the second most popular topic in international research, whereas it was not identified in domestic research. Domestic research in mathematics education tended to pay attention to the topics concerned with the mathematical competency, but it focused more on problem-solving and creativity/STEAM than other mathematical competencies. Rather, international research highlighted the topic related to equity and social justice.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5227-5237
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• 2011

Technology roadmap is a powerful tool that considers relationships of technology, product and market and referred as a supporting technology strategy and planning. There are numerous studies that have attempted to develop technology roadmap and case studies on specific technology areas. However, a number of studies have been dependant on brainstorming and discussion of expert group, delphi technique as qualitative analysis rather than systemic and quantitative analysis. To overcome the limitation, patent analysis considered as quite quantitative analysis is employed in this paper. Therefore, this paper proposes new technology roadmapping based on patent citation network considering technology life cycle and suggests planning for undeveloped technology but considered as promising. At first, patent data and citation information are collected and patent citation network is developed on the basis of collected patent information. Secondly, we investigate a stage of technology in the life cycle by considering patent application year and the technology life cycle, and duration of technology development is estimated. In addition, subsequent technologies are grouped as nodes of a super-level technology to show the evolution of the technology for the period. Finally, a technology roadmap is drawn by linking these technology nodes in a technology layer and estimating the duration of development time. Based on technology roadmap, technology planning is conducted to identify undeveloped technology through text mining and this paper suggests characteristics of technology that needs to be developed in the future. In order to illustrate the process of the proposed approach, technology for hydrogen storage is selected in this paper.

• Journal of Information Management
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• v.41 no.4
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• pp.227-249
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• 2010

Scholarly information has increased tremendously according to the development of IT, especially the Internet. However, simultaneously, people have to spend more time and exert more effort because of information overload. There have been many research efforts in the field of expert systems, data mining, and information retrieval, concerning a system that recommends user-expected information items through presumption. Recently, the hybrid system combining a content-based recommendation system and collaborative filtering or combining recommendation systems in other domains has been developed. In this paper we resolved the problem of the current recommendation system and suggested a new system combining collaborative filtering and Naive Bayes Classification. In this way, we resolved the over-specialization problem through collaborative filtering and lack of assessment information or recommendation of new contents through Naive Bayes Classification. For verification, we applied the new model in NDSL's paper service of KISTI, especially papers from journals about Sitology and Electronics, and witnessed high satisfaction from 4 experimental participants.