• The Korean Journal of Archival Studies
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• no.23
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• pp.3-43
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• 2010

Finding aids are tools which facilitate to locate and understand archives and records. Traditionally there are two types of archival finding aids: vertical and horizontal. Vertical finding aids such as inventories have multi-level descriptions based on provenance, while horizontal ones such as catalogs and index are tools to guide to the vertical finding aids based on the subject. In the web environment, traditional finding aids are evolving into more dynamic forms. Respecting the principles of provenance and original order, vertical finding aids are changing to multi-entity structures with development of ISAD(G), ISAAR(CPF) and ISDF as standards for describing each entity. However, vertical finding aids can be too difficult, complicated, and boring for many users, who are accustomed to the easy and exciting searching tools in the internet world. Complementing them, new types of finding aids are appearing to provide easy, interesting, and extensive access channels. This study investigates the development and limitation of vertical finding aids, and the recent trend of evolving new finding aids complementing the vertical ones. The study finds three new trends of finding aid development. They are (i) mixture, (ii) integration, and (iii) openness. In recent days, certain finding aids are mixed with stories and others provide integrated searches for the collections of various heritage institutions. There are cases for experimenting user participation in the development of finding aids using Web 2.0 applications. These new types of finding aids can also cause some problems such as decontextualised description and prejudices, especially in the case of mixed finding aids and quality control of user contributed annotations and comments. To solve these problems, the present paper suggests to strengthen the infrastructure of vertical finding aids and to connect them with various new ones and to facilitate interactions with users of finding aids. It is hoped that the present paper will provide impetus for archives including the National Archives of Korea to set up and evaluate the development strategies for archival finding aids.

• 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.

• Journal of Service Research and Studies
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• v.12 no.1
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• pp.25-35
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• 2022

Covid-19, of which the first patient in Korea occurred in January 2020, has affected various fields. Of these, the tourism sector might havebeen hit the hardest. In particular, since tourism-based industrial structure forms the basis of the region, Gangwon-province, and the tourism industry is the main source of income for small businesses and small enterprises, the damage is great. To check the situation and extent of such damage, targeting the Chuncheon region, where public access is the most convenient among the Gangwon regions, one-day tours are possible using public transportation from Seoul and the metropolitan area, with a general image that low expense tourism is recognized as possible, this study conducted empirical analysis through data analysis. For this, the general status of the region was checked based on the visitor data of Chuncheon city provided by the tourist information system, and to check the levels ofinterest in 2019, before Covid-19, and in 2020, after Covid-19, by comparing keywords collected from the web service sometrend of Vibe Company Inc., a company specializing in keyword collection, with SK Telecom's T-map search site data, which in parallel provides in-vehicle navigation service and communication service, this study analyzed the general regional image of Chuncheon-city. In addition, by comparing data from two years by developing a tourism popularity index applying keywords and T-map search site data, this study examined how much the Covid-19 situation affected the level of interest of visitors to the Chuncheon area leading to actual visits using a data analysis approach. According to the results of big data analysis applying the tourism popularity index after designing the data mart, this study confirmed that the effect of the Covid-19 situation on tourism popularity in Chuncheon-city, Gangwon-provincewas not significant, and confirmed the image of tourist destinations based on the regional characteristics of the region. It is hoped that the results of this research and analysis can be used as useful reference data for tourism economic policy making.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.77-97
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• 2019

The large amount of data that emerges from the initial connection environment of the Fourth Industrial Revolution is a major factor that distinguishes the Fourth Industrial Revolution from the existing production environment. This environment has two-sided features that allow it to produce data while using it. And the data produced so produces another value. Due to the massive scale of data, future information systems need to process more data in terms of quantities than existing information systems. In addition, in terms of quality, only a large amount of data, Ability is required. In a small-scale information system, it is possible for a person to accurately understand the system and obtain the necessary information, but in a variety of complex systems where it is difficult to understand the system accurately, it becomes increasingly difficult to acquire the desired information. In other words, more accurate processing of large amounts of data has become a basic condition for future information systems. This problem related to the efficient performance of the information system can be solved by building a semantic web which enables various information processing by expressing the collected data as an ontology that can be understood by not only people but also computers. For example, as in most other organizations, IT has been introduced in the military, and most of the work has been done through information systems. Currently, most of the work is done through information systems. As existing systems contain increasingly large amounts of data, efforts are needed to make the system easier to use through its data utilization. An ontology-based system has a large data semantic network through connection with other systems, and has a wide range of databases that can be utilized, and has the advantage of searching more precisely and quickly through relationships between predefined concepts. In this paper, we propose a defense ontology as a method for effective data management and decision support. In order to judge the applicability and effectiveness of the actual system, we reconstructed the existing air force munitions situation management system as an ontology based system. It is a system constructed to strengthen management and control of logistics situation of commanders and practitioners by providing real - time information on maintenance and distribution situation as it becomes difficult to use complicated logistics information system with large amount of data. Although it is a method to take pre-specified necessary information from the existing logistics system and display it as a web page, it is also difficult to confirm this system except for a few specified items in advance, and it is also time-consuming to extend the additional function if necessary And it is a system composed of category type without search function. Therefore, it has a disadvantage that it can be easily utilized only when the system is well known as in the existing system. The ontology-based logistics situation management system is designed to provide the intuitive visualization of the complex information of the existing logistics information system through the ontology. In order to construct the logistics situation management system through the ontology, And the useful functions such as performance - based logistics support contract management and component dictionary are further identified and included in the ontology. In order to confirm whether the constructed ontology can be used for decision support, it is necessary to implement a meaningful analysis function such as calculation of the utilization rate of the aircraft, inquiry about performance-based military contract. Especially, in contrast to building ontology database in ontology study in the past, in this study, time series data which change value according to time such as the state of aircraft by date are constructed by ontology, and through the constructed ontology, It is confirmed that it is possible to calculate the utilization rate based on various criteria as well as the computable utilization rate. In addition, the data related to performance-based logistics contracts introduced as a new maintenance method of aircraft and other munitions can be inquired into various contents, and it is easy to calculate performance indexes used in performance-based logistics contract through reasoning and functions. Of course, we propose a new performance index that complements the limitations of the currently applied performance indicators, and calculate it through the ontology, confirming the possibility of using the constructed ontology. Finally, it is possible to calculate the failure rate or reliability of each component, including MTBF data of the selected fault-tolerant item based on the actual part consumption performance. The reliability of the mission and the reliability of the system are calculated. In order to confirm the usability of the constructed ontology-based logistics situation management system, the proposed system through the Technology Acceptance Model (TAM), which is a representative model for measuring the acceptability of the technology, is more useful and convenient than the existing system.

• Journal of the Korean Society of Food Science and Nutrition
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• v.31 no.5
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• pp.847-885
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• 2002

The purpose of this study was to develop a web-based internet program for nutritional assessment and diet Prescription by renal diseases. Renal diseases were classified by nephrotic syndrome, renal failure, hemodialysis and peritoneal dialysis. The system consisted of five parts according to their functions and contents. The first part is to assess the general health status such as body weight, obesity index, basal metabolic rate and total energy requirement by the input of age, sex, height, weight and degree of activity. The second part was designed to investigate dietary history of patient, that is, to find out his inappropriate dietary habit and give him some suggestions for appropriate dietary behavior by investigating his dietary history. This part also offers the diet and nutrition management by personal status with renal disease, and the information for food selection, snacks, convenience foods, dine-out, behavioral modification, cooking methods, food exchange lists and terms. The third part is evaluating their energy and nutrients intake by comparing with recommended dietary allowance for Koreans or standardized data for patient with renal disease. In this part, it is also analyzing energy and nutrients of food consumed by food group and meals, and evaluating the status of nutrient intake. The fort]1 one, a major part of the system, is implementing the diet and menu planning by using food exchange lists. This Part Provides the patient with menus lists and I day menu suitable to his weight, activity and the status of renal disease. The fifth part is providing information on energy and nutrients of foods and drinks, and top 20 foods classified by nutrients. These results are finally displayed as tabular forms and graphical forms on the computer screen.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.81-96
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• 2016

As the internet has become widespread and easy to access everywhere, it is common for people to search information via online search engines such as Google and Naver in everyday life. Recent studies have used online search volume of specific keyword as a measure of the internet users' attention in order to predict disease outbreaks such as flu and cancer, an unemployment rate, and an index of a nation's economic condition, and etc. For stock traders, web search is also one of major information resources to obtain data about individual stock items. Therefore, search volume of a stock item can reflect the amount of investors' attention on it. The investor attention has been regarded as a crucial factor influencing on stock price but it has been measured by indirect proxies such as market capitalization, trading volume, advertising expense, and etc. It has been theoretically and empirically proved that an increase of investors' attention on a stock item brings temporary increase of the stock price and the price recovers in the long run. Recent development of internet environment enables to measure the investor attention directly by the internet search volume of individual stock item, which has been used to show the attention-induced price pressure. Previous studies focus mainly on Dow Jones and NASDAQ market in the United States. In this paper, we investigate the relationship between the individual investors' attention measured by the internet search volumes and stock price changes of individual stock items in the KOSDAQ market in Korea, where the proportion of the trades by individual investors are about 90% of the total. In addition, we examine the difference between industries in the influence of investors' attention on stock return. The internet search volume of stocks were gathered from "Naver Trend" service weekly between January 2007 and June 2015. The regression model with the error term with AR(1) covariance structure is used to analyze the data since the weekly prices in a stock item are systematically correlated. The market capitalization, trading volume, the increment of trading volume, and the month in which each trade occurs are included in the model as control variables. The fitted model shows that an abnormal increase of search volume of a stock item has a positive influence on the stock return and the amount of the influence varies among the industry. The stock items in IT software, construction, and distribution industries have shown to be more influenced by the abnormally large internet search volume than the average across the industries. On the other hand, the stock items in IT hardware, manufacturing, entertainment, finance, and communication industries are less influenced by the abnormal search volume than the average. In order to verify price pressure caused by investors' attention in KOSDAQ, the stock return of the current week is modelled using the abnormal search volume observed one to four weeks ahead. On average, the abnormally large increment of the search volume increased the stock return of the current week and one week later, and it decreased the stock return in two and three weeks later. There is no significant relationship with the stock return after 4 weeks. This relationship differs among the industries. An abnormal search volume brings particularly severe price reversal on the stocks in the IT software industry, which are often to be targets of irrational investments by individual investors. An abnormal search volume caused less severe price reversal on the stocks in the manufacturing and IT hardware industries than on average across the industries. The price reversal was not observed in the communication, finance, entertainment, and transportation industries, which are known to be influenced largely by macro-economic factors such as oil price and currency exchange rate. The result of this study can be utilized to construct an intelligent trading system based on the big data gathered from web search engines, social network services, and internet communities. Particularly, the difference of price reversal effect between industries may provide useful information to make a portfolio and build an investment strategy.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.419-428
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• 2021

In order to develope a mobile-based greenhouse energy calculation program, firstly, the overall thermal transmittance of 10 types of major covers and 16 types of insulation materials were measured. In addition, to estimate the overall thermal transmittance when the cover and insulation materials were installed in double or triple layers, 24 combinations of double installations and 59 combinations of triple installations were measured using the hotbox. Also, the overall thermal transmittance value for a single material and the thermal resistance value were used to calculate the overall thermal transmittance value at the time of multi-layer installation of covering and insulating materials, and the linear regression equation was derived to correct the error with the measured values. As a result of developing the model for estimating thermal transmittance when installing multiple layers of coverings and insulating materials based on the value of overall thermal transmittance of a single-material, the model evaluation index was 0.90 (good when it is 0.5 or more), indicating that the estimated value was very close to the actual value. In addition, as a result of the on-site test, it was evaluated that the estimated heat saving rate was smaller than the actual value with a relative error of 2%. Based on these results, a mobile-based greenhouse energy calculation program was developed that was implemented as an HTML5 standard web-based mobile web application and was designed to work with various mobile device and PC browsers with N-Screen support. It had functions to provides the overall thermal transmittance(heating load coefficient) for each combination of greenhouse coverings and thermal insulation materials and to evaluate the energy consumption during a specific period of the target greenhouse. It was estimated that an energy-saving greenhouse design would be possible with the optimal selection of coverings and insulation materials according to the region and shape of the greenhouse.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.87-95
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• 2020

The Science and Technology Information Service provided by the Korea Institute of Science and Technology Information (KISTI) is a service designed to allow users to easily and conveniently search and view content that is built similar to the general information service. NDSL is KISTI's core science, technology and information service, providing about 138 million content and having about 93 million page views in a year of 2019. In this paper, various insights were derived through the analysis of how science and technology information such as academic papers, reports and patents provided by NDSL is searched and utilized through web services (https://www.ndsl.kr) and search query words. In addition to general statistics such as the status of content construction, utilization status and utilization methods by type of content, monthly/weekly/time-of-day content usage, content view rate per one-time search by content type, the comparison of the use status of academic papers by year, the relationship between the utilization of domestic academic papers and the KCI index we analyzed the usability of each content type, such as academic papers and patents. We analyzed query words such as the language form of query words, the number of words of query words, and the relationship between query words and timeliness by content type. Based on the results of these analyses, we would like to propose ways to improve the service. We suggest that NDSL improvements include ways to dynamically reflect the results of content utilization behavior in the search results rankings, to extend query and to establish profile information through non-login user identification for targeted services.