• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.38 no.4
• /
• pp.595-599
• /
• 2018

Sufficient understanding of oversea construction market status is crucial to get profitability in the international construction project. Plenty of researchers have been considering the news article as a fine data source for figuring out the market condition, since the data includes market information such as political, economic, and social issue. Since the text data exists in unstructured format with huge size, various text-mining techniques were studied to reduce the unnecessary manpower, time, and cost to summarize the data. However, there are some limitations to extract the needed information from the news article because of the existence of various topics in the data. This research is aimed to overcome the problems and contribute to summarization of market status by performing topic modeling with Latent Dirichlet Allocation. With assuming that 10 topics existed in the corpus, the topics included projects for user convenience (topic-2), private supports to solve poverty problems in Africa (topic-4), and so on. By grouping the topics in the news articles, the results could improve extracting useful information and summarizing the market status.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.03a
• /
• pp.335-345
• /
• 1999

오늘날 날로 증가하는 물류비는 개별 기업은 물론 국가 전체의 수출 경쟁력을 약화시키는 주요 원인으로 지적되고 있다. 그러나 그동안 우리나라에서는 물류비 절감을 위한 종합적이고 체계적인 대책이 이루어지지 못하였다. 특히 본 논문의 연구대상인 육상물류의 경우 그 비중이 전체 화물 운송의 60% 이상을 차지함에도 불구하고 심각한 교통체증 및 물류기반 시설의 미비 등으로 인하여 물류비가 계속 증가하는 양상을 보여 왔다. 따라서 본 논문에서는 우리나라 육상물류시스템이 안고 있는 문제점의 해결을 위한 방안들 중의 하나로 정보기술의 활용에 관한 내용을 다루고 있다. 즉 영세한 기업들도 누구나 손쉽게 이용할 수 있도록 인터넷을 이용한 육상물류중개시스템의 개발에 관한 내용을 소개하고 있다. 육상물류중개시스템은 복합화물주선업체인 (주) 대형물류와 함께 개발한 시스템으로 인터넷을 통하여 화주의 화물 운송의뢰를 접수받아 이를 여러 운송업체에게 제공해 주는 역할을 수행하게 된다. 특히 육상물류중개시스템은 화물의 운송과 관련하여 발생하는 다양한 정보들을 데이터베이스에 저장하여 두었다가 세관을 비롯한 터미널에 대한 각종 신고업무에 이용할 수 있으며, 이밖에도 교통정보 및 화물 위치정보 등 다양한 서비스를 제공해 줄 수 있다. 따라서 운송업체의 공차율을 줄이고 화주에게는 자신의 화물에 대한 정보를 실 시간으로 전달해 줄 수 있다는 장점이 있다. 또한 이러한 육상물류중개시스템은 현재 개발중인 통합데이터베이스를 기반으로한 항만물류원스톱서비스 시스템과 연계되어 차후에는 물류원스톱시스템으로 발전할 수 있을 것이다.용되어져 왔다. 그러나 MCRDR 이론이 적용된 전문가시스템들의 경우 MCRDR이론을 기본으로한 개발 툴로서 개발된 시스템들이 아니고 해당분야에서 MCRDR이론을 적용한 엔진을 직접 설계 구현하여 온 것이 사실이다. KEE(Knowledge Engineer for Experts) 시스템은 최근 개발된 MCRDR기반 전문가시스템 개발 툴로서 본 논문에서는 이러한 분야별 전문가시스템 개발을 지양하고 MCRDR 이론을 기반으로 한 범용성 있는 전문가시스템 개발 툴의 개발에 관한 연구를 소개한다.-based Data Mining Architecture를 제시하였다. 본 연구의 의의로는 데이터 마이닝을 통한 귀납적 지식생성에 있어 귀납적 오류의 발생을 도메인 지식을 통해 설명가능 함을 보임으로 검증하고 아울러 이러한 설명을 통해 연역적으로 새로운 가설지식을 생성시켜 이를 가설검증방식으로 검증함으로써 귀납적 접근과 연역적 접근의 통합 데이터 마이닝 접근을 제시하였다는데 있다.osed algorithm are faster and lower than the existing LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.8
• /
• pp.521-529
• /
• 2020

Forest fires are one of the most important environmental risks that have adverse effects on many aspects of life, such as the economy, environment, and health. The early detection, quick prediction, and rapid response of forest fires can play an essential role in saving property and life from forest fire risks. For the rapid discovery of forest fires, there is a method using meteorological data obtained from local sensors installed in each area by the Meteorological Agency. Meteorological conditions (e.g., temperature, wind) influence forest fires. This study evaluated a Data Mining (DM) approach to predict the burned area of forest fires. Five DM models, e.g., Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Decision Tree (DT), Random Forests (RF), and Deep Neural Network (DNN), and four feature selection setups (using spatial, temporal, and weather attributes), were tested on recent real-world data collected from Gyeonggi-do area over the last five years. As a result of the experiment, a DNN model using only meteorological data showed the best performance. The proposed model was more effective in predicting the burned area of small forest fires, which are more frequent. This knowledge derived from the proposed prediction model is particularly useful for improving firefighting resource management.

• Journal of Internet Computing and Services
• /
• v.21 no.1
• /
• pp.127-135
• /
• 2020

As the importance of the use and analysis of big data is emerging, there is a growing interest in natural language processing techniques for unstructured data such as news articles and comments. Particularly, as the collection of big data becomes possible, data mining techniques capable of pre-processing and analyzing data are emerging. In this case study with a telecom company, we propose a methodology how to formalize unstructured data using text mining. The domain is determined as equipment failure and the data is about 2.2 million equipment check ledger data. Data on equipment failures by 800,000 per year is accumulated in the equipment check ledger. The equipment check ledger coexist with both formal and unstructured data. Although formal data can be easily used for analysis, unstructured data is difficult to be used immediately for analysis. However, in unstructured data, there is a high possibility that important information. Because it can be contained that is not written in a formal. Therefore, in this study, we study to develop digital transformation method for unstructured data in equipment check ledger.

• Journal of the Korea Society of Computer and Information
• /
• v.12 no.2 s.46
• /
• pp.231-242
• /
• 2007

The existing pattern mining methods[1,2,3,4,5,6,11,12,13] do not use location generalization method on the set of location history data of moving object, but even so they simply do extract only frequent patterns which have no spatio-temporal constraint in moving patterns on specific space. Therefore, it is difficult for those methods to apply to frequent pattern mining which has spatio-temporal constraint such as optimal moving or scheduling paths among the specific points. And also, those methods are required more large memory space due to using pattern tree on memory for reducing repeated scan database. Therefore, more effective pattern mining technique is required for solving these problems. In this paper, in order to develop more effective pattern mining technique, we propose new location generalization method that converts data of detailed level into meaningful spatial information for reducing the processing time for pattern mining of a massive history data set of moving object and space saving. The proposed method can lead the efficient spatial moving pattern mining of moving object using by creating moving sequences through generalizing the location attributes of moving object into 2D spatial area based on $R^*$-Tree and Area Grid Hash Table(AGHT) in preprocessing stage of pattern mining.

• The Journal of the Convergence on Culture Technology
• /
• v.6 no.4
• /
• pp.385-389
• /
• 2020

The medical drama has developed into a story centered on 'people', raising viewers' sympathy. The story of the drama is the true life story of doctors, patients and families. It is also a story that reminds me of 'a little special day of our ordinary people'. And the song played and sung by five characters in the drama became a factor that stimulates nostalgia and increases immersion. The highest viewer rating was 14.1%, and 51,584 blogs alone were registered. According to the big data analysis, the related words were 'Wise OST', 'Album Name', 'Artist Name', 'Two Hours in a row', 'Record', 'Remake', 'OST Revealed', 'Advertisement Revenue', 'Playlist', 'Aroha' and 'Cho Jung-seok'. The commercialization of medical dramas includes 'Sales of Drama OST Albums', 'Organizing Online Live Concerts (PPL in Advertising)', 'Publishing Piano Music', 'Picture of People-Oriented Photography', 'Making Music Video Editing Drama Highlight', 'YouTube Upload Profits', 'Mask' and 'Disinfectant'. it is predicted that the touching story of Corona 19 and the charming humanity will unfold. The limitations of the research will require analysis of various works by genre and attempts to analyze consumer values by industry.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.2
• /
• pp.126-132
• /
• 2015

Technology forecasting is about understanding a status of a specific technology in the future, based on the current data of the technology. It is useful when planning technology management strategies. These days, it is common for countries, companies, and researchers to establish R&D directions and strategies by utilizing experts' opinions. However, this qualitative method of technology forecasting is costly and time consuming since it requires to collect a variety of opinions and analysis from many experts. In order to deal with these limitations, quantitative method of technology forecasting is being studied to secure objective forecast result and help R&D decision making process. This paper suggests a methodology of technology forecasting based on quantitative analysis. The methodology consists of data collection, principal component analysis, and technology forecasting by logistic regression, which is one of the data mining techniques. In this research, patent documents related to autonomous vehicle are collected. Then, the texts from patent documents are extracted by text mining technique to construct an appropriate form for analysis. After principal component analysis, logistic regression is performed by using principal component score. On the basis of this result, it is possible to analyze R&D development situation and technology forecasting.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.10a
• /
• pp.506-509
• /
• 2017

Opinion mining is a way to analyze the emotions in the text and is used to identify the emotional state of the author and to find out the opinions of the public. As you can analyze individual emotions through opinion mining, if you analyze the text by region, you can find out the emotional state you have in each region. The regional sentiment analysis can obtain information that could not be obtained from personal sentiment analysis, and if a certain area has emotions, it can understand the cause. For regional sentiment analysis, we need text data created by region, so we need to collect data through Twitter crawling. Therefore, this paper designs a Twitter data collection system for regional sentiment analysis. The client requests the tweet data of the specific region and time, and the server collects and transmits the requested tweet data from the client. Through the latitude and longitude values of the region, it collects the tweet data of the area, and it can manage the text by region and time through collected data. We expect efficient data collection and management for emotional analysis through the design of this system.

• Journal of Wetlands Research
• /
• v.23 no.4
• /
• pp.364-371
• /
• 2021

Public awareness of alien species can vary by generation, period, or specific events associated with these species. An understanding of public awareness is important for the management of alien species because differences in public awareness can affect the establishment and implementation of management plans. We analyzed digital texts on social media platforms, news articles, and internet search volumes used in conservation culturomics to understand public interest and sentiment regarding alien freshwater species. The number of tweets, number of news articles, and relative search volume to 11 freshwater alien species were extracted to determine public interest. Additionally, the trend over time, seasonal variability, and repetition period of these data were confirmed. We also calculated the sentiment score and analyzed public sentiment in the collected data using sentiment analysis based on text mining techniques. The American bullfrog, nutria, bluegill, and largemouth bass drew relatively more public interest than other species. Some species showed repeated patterns in the number of Twitter posts, media coverage, and internet searches found according to the specified periods. The text mining analysis results showed negative sentiments from most people regarding alien freshwater species. Particularly, negative sentiments increased over the years after alien species were designated as ecologically disturbing species.

• The Journal of the Korea Contents Association
• /
• v.18 no.12
• /
• pp.468-488
• /
• 2018

The maker movement is a phenomenon of society and culture where people who make necessary things come together and share knowledge and experience through creativity. However, as the maker movement has grown rapidly over the past decade, there is still a lack of consensus for how far they will be viewed as a maker movement. We need to look at how the maker movement has changed so far in order to find the direction of development of the maker movement. This study analyzes the media articles using text-based big data analysis methodology to understand how the issue of the maker movement has changed in general media. In particular, we apply Keyword Network Analysis and DTM(Dynamic Topic Model) to analyze changes of interest according to time. The Keyword Network Analysis derives major keywords at the word level in order to analyze the evolution of the maker movement, and DTM helps to identify changes in interest in different areas of the maker movement at three levels: word, topic, and document. As a result, we identified major topics such as start-ups, makerspaces, and maker education, and the major keywords have changed from 3D printer and enterprise to education.