• Journal of Intelligence and Information Systems
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• 제21권1호
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• pp.143-159
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• 2015

Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• 제35권4호
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• pp.1-13
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• 2017

This thesis aims to research the original landscape of an area in Chirwon by comparing and interpreting the current and the former landscape of the time around 1860, in which was created. Inside the painting, MuGi-YeonDang(舞沂蓮塘), its nearby garden facilities and landscape are described. The conclusions of the research are as follows. Around the inner garden(內園) called Gukdam(菊潭), core spaces of Mugi-YeonDang were Hahwanjeong(何換亭) and Pungyongnu(風浴樓). After 1971, Chunghyosa(忠孝祠), Yeongjeonggak(影幀閣) which was built as inheritance of Giyangseowon (沂陽書院), and other buildings were added. On the opposite of Hahwanjeong, there is a designed pond inside Gukdam, in which three pieces of bizarre stones and colorful flower plants and trees reside. In the middle of the pond, there is an center island(中島), constructed with Bonghwangseok(鳳凰石) and Napduseok(衲頭石). Two different kinds of trees are settled on top of the stones, and one of them is identified as a maple. In the external scenery(外境), Jakdaesan Mountain and Cheonjusan Mountain are located at the upper part of Mugi-Yeondang, and Bibo Forest(裨補林), which does not exist anymore, was located at the lower part of it. A notable achievement in own research was to discover the Ju family's graveyard(朱氏墓群) consisted of more than 10 murals at the lower part of Mt. Jakdaesan in the outer space and the remains of 'Byeoreop(別業) Yuhoejeong(有懷亭)' at the nearby Sanjeong-ri(山亭里) area, which was the Ju family's gravesite(Seonsan, 先山). The discovered remains showed the presence of a square pond(方塘) and an island in the center of it(中島) in the form of Seokgasan(an artificial mountain made with stones), a stone monument called Mangchudae(望楸臺), etc. The Seokgasan was consisted of processed natural stones, and layers of the stones were piled up for it. On the side of the layered stones, 'Gyeongam(敬嵒)' and 'Sesim(洗心)' are engraved. Especially, Gyeongja(letter Gyeong, 敬字) is a copy of the Gyeongja Rock(敬字岩) of Sosu-Seowon(紹修書院), which is a symbolic garden language mutually used in the signboards of Pungyongnu and Musansa(武山祠), a place built to enshrine Ju, Sebung(周世鵬). Through the written names of the building found in , it can be assumed that the name of the square pond with Seokgasan was Taehwaji or Jeongwudang, and the name of the Seokgasan was Sogeumgang(小金剛) or Sobangjang(小方丈). The names correspond to the names of the Seokgasan of Gukdam, which was Yangsimdae(養心臺) and Bongnaesan(蓬萊山). By means of the corresponding names, it can be inferred that the relations between the spaces were intended. was originally created as 'a manor painting(莊園圖)', led by the 15th generation of the Ju family who moved into Chirwon-ri, Haman. The painting describes not only the back garden but also the external scenery, thus it provides important evidences for understanding Mugi-YeonDang and its nearby landscape, and is helpful to its maintenance and restoration.

• Journal of Science Education
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• 제45권3호
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• pp.292-303
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• 2021

This study identifies the terms frequently used together with energy in online science news articles and topics of the news reports to find out how the term energy is used in everyday life and to draw implications for science curriculum and instruction about energy. A total of 2,171 online news articles in science category published by 11 major newspaper companies in Korea for one year from March 1, 2018 were selected by using energy as a search term. As a result of natural language processing, a total of 51,224 sentences consisting of 507,901 words were compiled for analysis. Using the R program, term frequency analysis, semantic network analysis, and structural topic modeling were performed. The results show that the terms with exceptionally high frequencies were technology, research, and development, which reflected the characteristics of news articles that report new findings. On the other hand, terms used more than once per two articles were industry-related terms (industry, product, system, production, market) and terms that were sufficiently expected as energy-related terms such as 'electricity' and 'environment.' Meanwhile, 'sun', 'heat', 'temperature', and 'power generation', which are frequently used in energy-related science classes, also appeared as terms belonging to the highest frequency. From a network analysis, two clusters were found including terms related to industry and technology and terms related to basic science and research. From the analysis of terms paired with energy, it was also found that terms related to the use of energy such as 'energy efficiency,' 'energy saving,' and 'energy consumption' were the most frequently used. Out of 16 topics found, four contexts of energy were drawn including 'high-tech industry,' 'industry,' 'basic science,' and 'environment and health.' The results suggest that the introduction of the concept of energy degradation as a starting point for energy classes can be effective. It also shows the need to introduce high-tech industries or the context of environment and health into energy learning.

• Journal of Intelligence and Information Systems
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• 제20권2호
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.