• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.

• Journal of Korea Technology Innovation Society
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• v.14 no.spc
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• pp.1171-1186
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• 2011

Identifying Emerging Technology Trends is crucial for decision makers of nations and organizations in order to use limited resources, such as time, money, etc., efficiently. Many researchers have proposed emerging trend detection systems based on a popularity analysis of the document, but this still needs to be improved. In this paper, an emerging trend detection classifier is proposed which uses both academic and industrial data, SCOPUS and PATSTAT. Unlike most pre-vious research, our emerging technology trend classifi-er utilizes supervised, semi-automatic, machine learning techniques to improve the precision of the results. In addition, the citation information from among the SCOPUS data is analyzed to identify the early signals of emerging technology trends.

• Journal of Korean Society of Rural Planning
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• v.26 no.4
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• pp.53-65
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• 2020

This study aimed to provide a basic information for managing the water quality of national and regional 1st rivers in Gyeongnam by analyzing the emerging hot spot patterns in BOD, T-P, and TOC, and by grouping the changing trends into clusters. The emerging hot spot analysis for each water quality item was implemented in ArcGIS Desktop with monthly water quality data from 96 water environmental monitoring stations in Gyeongnam, and then four patterns of water quality change were classified by the K-mean cluster analysis. As for BOD, persistent cold spot pattern covered about 42.9% of target rivers, and T-P concentration tended to be low or be getting lower at over 70% of target rivers. While, for TOC, about 70% of target rivers resulted in oscillating hot spots. In addition, the cluster analysis showed that the downstream of Nakdong river had the top priority in terms of water quality management because of the increasing concentration for all the three water quality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.663-677
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• 2023

As the paradigm of future vehicles changes, the interest in automotive semiconductor, which plays a key role in realizing this, is increasing. Automotive semiconductors are the technology with very high entry barriers that require a lot of effort and time because it must secure technology readiness level and also consider safety and reliability. In this technology field, it is very important to develop new businesses and create opportunities through technology trend analysis. However, systematic analysis and application of automotive semiconductor technology trends are currently lacking. In this paper, U.S. registered patent documents related to automotive semiconductor were collected and investigated based on the patent's IPC. The main technology of automotive semiconductor was analyzed through topic modeling, and the technology path such as emerging technology was investigated through cosine similarity. We identified that those emerging technologies such as driving control for vehicle and AI service appeared. We observed that as time passed, both convergence and independence of automotive semiconductor technology proceeded simultaneously.

• Journal of Information Technology Services
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• v.19 no.4
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• pp.49-61
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• 2020

Prior studies on technology predictions attempted to predict the emergence and spread of emerging technologies through the analysis of correlations and changes between data using objective data such as patents and research papers. Most of the previous studies predicted future technologies only from the viewpoint of technology development. Therefore, this study intends to conduct technical forecasting from the perspective of the consumer by using keyword search frequency of search portals such as NAVER before and after the introduction of emerging technologies. In this study, we analyzed healthcare technologies into three types : measurement technology, platform technology, and remote service technology. And for the keyword analysis on the healthcare, we converted the classification of technology perspective into the keyword classification of consumer perspective. (Blood pressure and blood sugar, healthcare diagnosis, appointment and prescription, and remote diagnosis and prescription) Naver Trend is used to analyze keyword trends from a consumer perspective. We also used the ARIMA model as a technology prediction model. Analyzing the search frequency (Naver trend) over 44 months, the final ARIMA models that can predict three types of healthcare technology keyword trends were estimated as "ARIMA (1,2,1) (1,0,0)", "ARIMA (0,1,0) (1,0,0)", "ARIMA (1,1,0) (0,0,0)". In addition, it was confirmed that the values predicted by the time series prediction model and the actual values for 44 months were moving in almost similar patterns in all intervals. Therefore, we can confirm that this time series prediction model for healthcare technology is very suitable.

• Journal of KIISE:Software and Applications
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• v.36 no.3
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• pp.236-243
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• 2009

With advances in topic detection and tracking(TDT), automatic trend analysis from a collection of time-stamped documents, like patents, news papers, and blog pages, is a challenging research problem. Past research in this area has mainly focused on showing a trend line over time of a given concept by measuring the strength of trend-associated term frequency information. for detection of emerging trends, either a simple criterion such as frequency change was used, or an overall comparison was made against a training data. We note that in order to show most salient trends detected among many possibilities, it is critical to devise a ranking function. To this end, we define four properties(change, persistency, stability and volume) of trend lines drawn from frequency information, to quantify various aspects of trends, and propose a method by which trend lines can be ranked. The properties are examined individually and in combination in a series of experiments for their validity using the ranking algorithm. The results show that a judicious combination of the four properties is a better indicator for salient trends than any single criterion used in the past for ranking or detecting emerging trends.

• East Asian Economic Review
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• v.18 no.1
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• pp.55-81
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• 2014

This study aims to answer two questions using input-output decomposition analysis: 1) Have emerging Asian economies decoupled? 2) What are the sources of structural changes in gross outputs and value-added of emerging Asian economies related to the first question? The main findings of the study are as follows: First, since 1990, there has been a trend of increasing dependence on exports to extra-regions such as G3 and the ROW, indicating no sign of "decoupling", but rather an increasing integration of emerging Asian countries into global trade. Second, there is a contrasting feature in the sources of structural changes between non-China emerging Asia and China. Dependence of non-China emerging Asia on intra-regional trade has increased in line with strengthening economic integration in East Asia, whereas China has disintegrated from the region. Therefore, it can be said that China has contributed to no sign of decoupling of emerging Asia as a whole.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.2
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• pp.139-149
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• 2013

As the importance of technology forecasting while countries and companies manage the R&D project is growing bigger, the methodology of technology forecasting has been diversified. One of the forecasting method is patent analysis. This research proposes quick forecasting process of emerging technology based on keyword approach using text mining. The forecasting process is following: First, the term-document matrix is extracted from patent documents by using text mining. Second, emerging technology keyword are extracted by analyzing the importance of word from utilizing mean values and standard deviation values of the term and the emerging trend of word discovered from time series information of the term. Next, association between terms is measured by using cosine similarity. finally, the keyword of emerging technology is selected in consequence of the synthesized result and we forecast the emerging technology according to the results. The technology forecasting process described in this paper can be applied to developing computerized technology forecasting system integrated with various results of other patent analysis for decision maker of company and country.

• Korean Management Science Review
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• v.23 no.3
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• pp.77-93
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• 2006

In the highly competitive world, there has been a concomitant increase in the need for the research and planning methodology, which can perform an advanced assessment of technological opportunities and an early Perception of threats and possibilities of the emerging technology according to the nation's economic and social status. This research is aiming to provide indicators and visualization methods to measure the latest research trend and aspect underlying scientific and technological documents to researchers and policy planners using 'Co-word Analysis' Organic light emitting diodes(OLED) is an emerging technology in various fields of display and which has a highly prospective market value. In this paper, we presented an analysis on OLED. Co-word analysis was employed to reveal patterns and trends in the OLED fields by measuring the association strength of terms representatives of relevant publications or other texts produced in the OLED field. Data were collected from SCI and the critical keywords could De extracted from the author keywords. These extracted keywords were further standardized. In order to trace the dynamic changes in the OLED field, we presented a variety of technology mapping. The results showed that the OLED field has some established research theme and also rapidly transforms to embrace new themes.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.1
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• pp.111-122
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• 2014

This paper proposes a new strategy for assisting SMEs in S. Korea to grow by the use of free technologies, which include expired patents, patents that are effective in other countries but not in S. Korea, and technologies that were published without being patented. A key question answered in this paper is how to find valuable technologies developed by large companies that can be utilized by domestic SMEs. A procedure for identifying emerging free technologies was developed and is explained with an example. A quantitative analysis of technology trend was conducted on PCT's published patents that did not include S. Korea as a designated country, and then emerging IPC subclasses were identified. Among those emerging technology areas, the subclass areas where the number of Korean inventors is small were identified as key emerging free technology areas. Our method for identifying emerging free technologies can assist domestic SMEs to advance in the international market and guide the development of a national industrial strategy.