• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.909-928
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• 2020

From the early 1970s, the US government began to recognize that penetration testing could not assure the security quality of products. Results of penetration testing such as identified vulnerabilities and faults can be varied depending on the capabilities of the team. In other words none of penetration team can assure that "vulnerabilities are not found" is not equal to "product does not have any vulnerabilities". So the U.S. government realized that in order to improve the security quality of products, the development process itself should be managed systematically and strictly. Therefore, the US government began to publish various standards related to the development methodology and evaluation procurement system embedding "security-by-design" concept from the 1980s. Security-by-design means reducing product's complexity by considering security from the initial phase of development lifecycle such as the product requirements analysis and design phase to achieve trustworthiness of product ultimately. Since then, the security-by-design concept has been spread to the private sector since 2002 in the name of Secure SDLC by Microsoft and IBM, and is currently being used in various fields such as automotive and advanced weapon systems. However, the problem is that it is not easy to implement in the actual field because the standard or guidelines related to Secure SDLC contain only abstract and declarative contents. Therefore, in this paper, we present the new framework in order to specify the level of Secure SDLC desired by enterprises. Our proposed CIA (functional Correctness, safety Integrity, security Assurance)-level-based security-by-design framework combines the evidence-based security approach with the existing Secure SDLC. Using our methodology, first we can quantitatively show gap of Secure SDLC process level between competitor and the company. Second, it is very useful when you want to build Secure SDLC in the actual field because you can easily derive detailed activities and documents to build the desired level of Secure SDLC.

• Asia pacific journal of information systems
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• v.19 no.4
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• pp.77-100
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• 2009

Information Systems (IS) is an essential tool for any organizations. The last decade has seen an increasing body of knowledge on IS usage. Yet, IS often fails because of its misuse or non-use. In general, decisions regarding the selection of a system, which involve the evaluation of many IS vendors and an enormous initial investment, are made not through the consensus of employees but through the top-down decision making by top managers. In situations where the selected system does not satisfy the needs of the employees, the forced use of the selected IS will only result in their resistance to it. Many organizations have been either integrating dispersed legacy systems such as archipelago or adopting a new ERP (Enterprise Resource Planning) system to enhance employee efficiency. This study examines user resistance prior to the adoption of the selected IS or ERP system. As such, this study identifies the importance of managing organizational resistance that may appear in the pre-adoption context of an integrated IS or ERP system, explores key factors influencing user resistance, and investigates how prior experience with other integrated IS or ERP systems may change the relationship between the affecting factors and user resistance. This study focuses on organizational members' resistance and the affecting factors in the pre-adoption context of an integrated IS or ERP system rather than in the context of an ERP adoption itself or ERP post-adoption. Based on prior literature, this study proposes a research model that considers six key variables, including perceived benefit, system complexity, fitness with existing tasks, attitude toward change, the psychological reactance trait, and perceived IT competence. They are considered as independent variables affecting user resistance toward an integrated IS or ERP system. This study also introduces the concept of prior experience (i.e., whether a user has prior experience with an integrated IS or ERP system) as a moderating variable to examine the impact of perceived benefit and attitude toward change in user resistance. As such, we propose eight hypotheses with respect to the model. For the empirical validation of the hypotheses, we developed relevant instruments for each research variable based on prior literature and surveyed 95 professional researchers and the administrative staff of the Korea Photonics Technology Institute (KOPTI). We examined the organizational characteristics of KOPTI, the reasons behind their adoption of an ERP system, process changes caused by the introduction of the system, and employees' resistance/attitude toward the system at the time of the introduction. The results of the multiple regression analysis suggest that, among the six variables, perceived benefit, complexity, attitude toward change, and the psychological reactance trait significantly influence user resistance. These results further suggest that top management should manage the psychological states of their employees in order to minimize their resistance to the forced IS, even in the new system pre-adoption context. In addition, the moderating variable-prior experience was found to change the strength of the relationship between attitude toward change and system resistance. That is, the effect of attitude toward change in user resistance was significantly stronger in those with prior experience than those with no prior experience. This result implies that those with prior experience should be identified and provided with some type of attitude training or change management programs to minimize their resistance to the adoption of a system. This study contributes to the IS field by providing practical implications for IS practitioners. This study identifies system resistance stimuli of users, focusing on the pre-adoption context in a forced ERP system environment. We have empirically validated the proposed research model by examining several significant factors affecting user resistance against the adoption of an ERP system. In particular, we find a clear and significant role of the moderating variable, prior ERP usage experience, in the relationship between the affecting factors and user resistance. The results of the study suggest the importance of appropriately managing the factors that affect user resistance in organizations that plan to introduce a new ERP system or integrate legacy systems. Moreover, this study offers to practitioners several specific strategies (in particular, the categorization of users by their prior usage experience) for alleviating the resistant behaviors of users in the process of the ERP adoption before a system becomes available to them. Despite the valuable contributions of this study, there are also some limitations which will be discussed in this paper to make the study more complete and consistent.

• Journal of Venture Innovation
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• v.7 no.3
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• pp.25-43
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• 2024

The rapid advancement of the 4th Industrial Revolution and the ongoing effects of COVID-19 significantly accelerated the adoption of smart work practices, especially in the financial sector. This study aimed to empirically investigate the impact of smart work on job satisfaction among employees in this industry. Specifically, the study examined the effects of time flexibility and workplace flexibility (as quantitative elements of smart work) and work autonomy (as a qualitative element) on job satisfaction. Additionally, the study explored the impact of technostress factors, including techno-overload, techno-invasion, and techno-complexity. Using data from 250 valid survey responses collected from financial sector employees, the study employed structural equation modeling (SEM) with AMOS to analyze the relationships. The findings revealed that time flexibility and work autonomy positively influenced job satisfaction, with work autonomy being the most significant predictor. Conversely, techno-overload and techno-invasion negatively affected job satisfaction. However, workplace flexibility and techno-complexity did not show a significant relationship with job satisfaction, possibly due to the already established norms in the financial sector, where remote work and high-level technology usage were standard practices. A critical aspect of the study was the examination of work-life balance as a mediating factor. The analysis confirmed that work-life balance mediated the relationship between work autonomy, techno-overload, techno-invasion, and job satisfaction. This suggested that maintaining a balance between work and personal life was crucial for enhancing job satisfaction in smart work environments, particularly in the financial sector. Effective management of technostress was essential to preserving this balance and improving overall employee satisfaction. These findings contributed to the academic understanding of how smart work practices and technostress impacted job satisfaction. They offered practical insights for financial sector organizations seeking to optimize smart work environments by emphasizing the importance of work-life balance and carefully managing technostress factors.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.

• The Journal of Fisheries Business Administration
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• v.17 no.2
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• pp.65-106
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• 1986

1, The Purpose of Study This is a study on the Evaluation of the operating of Fisheries Extension Services of Korea, for performing the activities such as guiding fisheries technique as well as offering industrial information to the fishermen in fishing village. By doing so, the Fisheries Extension Sevices(FES) can materialize the continued growth of fisheries, the social and economic development of fishing village, and the increase in income by enhancing the knowledge level of Fishermen, etc. In performing fisheries policy, this activity plays a great role on the research and development activity, and it has become practical since 1976 in Korea. In order to meet immediately with the problem of fisheries technical innovation and rapid environmental changes surrounding the fisheries, the fishermen should not only enhance their scientific and comprehensive capacity in fisheries technique but abtain various effective information. Generally, as most of all the fishemen are poor in the managerial structure and scattered in fishing villages, they have little opportunity in the contact of information. As a result, it is nessessary for the FES to perform the fishing business by the extension service officials who has received special training and acquired fisheries know-how in these fields. And yet, FES is under the unfullfilled circumstance in such factors as manpower, technical know-how, equipment, and the service system etc., which is required in promoting the social, economic development of fishing village and in resolving the high technique demand of fisherman. This study on the fisheries extension services have been studied from those backgrounds. 2. Research Method The data of collecting methods which were necessary in carrying out this study was adopted by the questionaire research on the present extension service activity, through the subject of the extension services (driving agency of the work and the officials), the object(fishemen) and the 3rd observers to the extension services (the authorities concerned). The research sample was taken by the sampling extraction of total 1, 774 men from the above 3 groups. And the research was carried out from August, 1986 to October, 1986, supported from the Fisheries Extension Office (FEO) located in field during the research process. In this study, the levels of the extension operating were determined and estimated in accordance with the extension service method, morale of extension service officials and the extension service system, etc. through the collected data of the research questionaire paper. And based on this result, the essential conditions of the extension services were grasped, and also we tried to present the various activity plan necessary to promote the operating of the extension services. The questionaire research data was calculated by the computer center of National Fisheries University of Pusan, and the total result was again tried on the one demension analysis along with two dimension analysis to search out the relativity between the questionaire, and the statistical test was done $\chi$$^2$test in significance level of l~5%. 3. Contents of Study This study consists of 7 chapters and the contents are as follows : Chapter I : The object and method of the study Chapter II : The assessment and analysis of the extension services Chapter III : The contents and method of the extension services Chapter IV : Analysis of the essential conditions for the extension services Chapter V : The evaluation of activities of extension services Chapter Ⅵ : Conclusion.4. Results and RecommendationTherefore, the results of this study estimated by logical process and analysis are as follows : 1) Most of Korean fishing villages and coastal fishermen have shown much concerns about fisheries technique and social changes, thus many of them were confronted with new problems on how to adapt and to meet changes. 2) Majority of fishermen estimated FEO as an organization of specific technologies with all the thing concerning the fisheries technique in general. Therefore the fishermen wanted to utilize the FEO as an adaptable method for the modern fisheries techniques as well as the environmental changes. 3) In contrast with the fast changes of the fisheries technique, the complexity and variety of technical system and the broadness of fishing village and fishermen, it was revealed that the necessary factors such as the facilities, manpower, budget, and the level of applying techniques of the FEO located in field were highly insufficient. Accordingly, the guiding efficiency was low and the extension services did not provide full solution to the various request from fishermen. 4) It is possible to classify the activation factor for the extension service into two large dimension ; personal dimension relevant to guidance officials and work dimension relevant to the organization. And it was found that the activation level of the work dimension was far lower than the personal dimension between them. So, the activation should be done first in the dimesion to promote the activation of the extension services. 5) The extension services officials are now demoralized in general, thus it is necessary to take reality into consideration : the expense of activity, the adequate endowment of activity scope and the reasonable operation of the position class, etc to enhance its morale. However, in order to do the FES activation, first of all, the systems should be established which is lain unsettled stage until now. And there must be change in the understanding of government i.e. the fisheries extension services are the essential policy subject to build up the base of fisheries growth and modernize the fisheries management. And it should be driven positively with the recognition of the "lasting project".g project".uot;.

• Journal of Preventive Medicine and Public Health
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• v.11 no.1
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• pp.123-127
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• 1978

The major objective of this research is to identify those hospital characteristics that best explain cost variation among hospitals and to formulate linear models that can predict hospital costs. Specific emphasis is placed on hospital output, that is, the identification of diagnosis related patient groups (DRGs) which are medically meaningful and demonstrate similar patterns of hospital resource consumption. A casemix index is developed based on the DRGs identified. Considering the common problems encountered in previous hospital cost research, the following study requirements are estab-lished for fulfilling the objectives of this research: 1. Selection of hospitals that exercise similar medical and fiscal practices. 2. Identification of an appropriate data collection mechanism in which demographic and medical characteristics of individual patients as well as accurate and comparable cost information can be derived. 3. Development of a patient classification system in which all the patients treated in hospitals are able to be split into mutually exclusive categories with consistent and stable patterns of resource consumption. 4. Development of a cost finding mechanism through which patient groups' costs can be made comparable across hospitals. A data set of Medicare patients prepared by the Social Security Administration was selected for the study analysis. The data set contained 27,229 record abstracts of Medicare patients discharged from all but one short-term general hospital in Connecticut during the period from January 1, 1971, to December 31, 1972. Each record abstract contained demographic and diagnostic information, as well as charges for specific medical services received. The 'AUT-OGRP System' was used to generate 198 DRGs in which the entire range of Medicare patients were split into mutually exclusive categories, each of which shows a consistent and stable pattern of resource consumption. The 'Departmental Method' was used to generate cost information for the groups of Medicare patients that would be comparable across hospitals. To fulfill the study objectives, an extensive analysis was conducted in the following areas: 1. Analysis of DRGs: in which the level of resource use of each DRG was determined, the length of stay or death rate of each DRG in relation to resource use was characterized, and underlying patterns of the relationships among DRG costs were explained. 2. Exploration of resource use profiles of hospitals; in which the magnitude of differences in the resource uses or death rates incurred in the treatment of Medicare patients among the study hospitals was explored. 3. Casemix analysis; in which four types of casemix-related indices were generated, and the significance of these indices in the explanation of hospital costs was examined. 4. Formulation of linear models to predict hospital costs of Medicare patients; in which nine independent variables (i. e., casemix index, hospital size, complexity of service, teaching activity, location, casemix-adjusted death. rate index, occupancy rate, and casemix-adjusted length of stay index) were used for determining factors in hospital costs. Results from the study analysis indicated that: 1. The system of 198 DRGs for Medicare patient classification was demonstrated not only as a strong tool for determining the pattern of hospital resource utilization of Medicare patients, but also for categorizing patients by their severity of illness. 2. The wei틴fed mean total case cost (TOTC) of the study hospitals for Medicare patients during the study years was $11,27.02 with a standard deviation of $117.20. The hospital with the highest average TOTC ($1538.15) was 2.08 times more expensive than the hospital with the lowest average TOTC ($743.45). The weighted mean per diem total cost (DTOC) of the study hospitals for Medicare patients during the sutdy years was $107.98 with a standard deviation of $15.18. The hospital with the highest average DTOC ($147.23) was 1.87 times more expensive than the hospital with the lowest average DTOC ($78.49). 3. The linear models for each of the six types of hospital costs were formulated using the casemix index and the eight other hospital variables as the determinants. These models explained variance to the extent of 68.7 percent of total case cost (TOTC), 63.5 percent of room and board cost (RMC), 66.2 percent of total ancillary service cost (TANC), 66.3 percent of per diem total cost (DTOC), 56.9 percent of per diem room and board cost (DRMC), and 65.5 percent of per diem ancillary service cost (DTANC). The casemix index alone explained approximately one half of interhospital cost variation: 59.1 percent for TOTC and 44.3 percent for DTOC. Thsee results demonstrate that the casemix index is the most importand determinant of interhospital cost variation Future research and policy implications in regard to the results of this study is envisioned in the following three areas: 1. Utilization of casemix related indices in the Medicare data systems. 2. Refinement of data for hospital cost evaluation. 3. Development of a system for reimbursement and cost control in hospitals.

• 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.

• Analytical Science and Technology
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• v.7 no.2
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• pp.213-224
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• 1994

Several isotopomers of cyclooctanone were prepared by selective deuterium substitution. Intrinsic isotope effects on $^{13}C$ NMR chemical shifts of these isotopomers were investigated systematically at low temperature. These istope effects were discussed in relation to the preferred boat-chair conformation of cyclooctanone. Deuterium isotope effects on NMR chemical shifts have been known for a long time. Especially in a conformationally mobile molecule, isotope perturbation could affect NMR signals through a combination of isotope effects on equilibria and intrinsic effects. The distinction between intrinsic and nonintrinsic effects is quite difficult at ambient temperature due to involvement of both equilibrium and intrinsic isotope effects. However if equilibria between possible conformers of cyclooctanone are slowed down enough on the NMR time scale by lowering temperature, it should be possible to measure intrinsic isotope shifts from the separated signals at low temperature. $^{13}C$ NMR has been successfully utilized in the study on molecular conformation in solution when one deals with stable conformers or molecules were rapid interconversion occurs at ambient temperature. The study of dynamic processes in general requires analysis of spectra at several temperature. Anet et al. did $^1H$ NMR study of cyclooctanone at low temperature to freeze out a stable conformation, but were not able initially to deduce which conformation was stable because of the complexity of alkyl region in the $^1H$ NMR spectrum. They also reported the $^1H$ and $^{13}C$ NMR spectra of the $C_9-C_{16}$ cycloalkanones with changing temperature from $-80^{\circ}C$ to $-170^{\circ}C$, but they did not report a variable temperature $^{13}C$ NMR study of cyclooctanone. For the analysis of the intrinsic isotope effect with relation to cylooctanone conformation, $^{13}C$ NMR spectra are obtained in the present work at low temperatures (up to $-150^{\circ}C$) in order to find the chemical shifts at the temperature at which the dynamic process can be "frozen-out" on the NMR time scale and cyclooctanone can be observed as a stable conformation. Both the ring inversion and pseudorotational processes must be "frozen-out" in order to see separate resonances for all eight carbons in cyclooctanone. In contrast to $^1H$ spectra, slowing down just the ring inversion process has no apparent effects on the $^{13}C$ spectra because exchange of environments within the pairs of methylene carbons can still occur by the pseudorotational process. Several isotopomers of cyclooctanone were prepared by selective deuterium substitution (fig. 1) : complete deuterium labeling at C-2 and C-8 positions gave cyclooctanone-2, 2, 8, $8-D_4$ : complete labeling at C-2 and C-7 positions afforded the 2, 2, 7, $7-D_4$ isotopomer : di-deuteration at C-3 gave the 3, $3-D_2$ isotopomer : mono-deuteration provided cyclooctanone-2-D, 4-D and 5-D isotopomers : and partial deuteration on the C-2 and C-8 position, with a chiral and difunctional case catalyst, gave the trans-2, $8-D_2$ isotopomer. These isotopomer were investigated systematically in relation with cyclooctanone conformation and intrinsic isotope effects on $^{13}C$ NMR chemical shifts at low temperature. The determination of the intrinsic effects could help in the analysis of the more complex effects at higher temperature. For quantitative analysis of intrinsic isotope effects, the $^{13}C$ NMR spectrum has been obtained for a mixture of the labeled and unlabeled compounds because the signal separations are very small.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.39-58
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• 2014

As the importance of big data and related technologies continues to grow in the industry, it has become highlighted to visualize results of processing and analyzing big data. Visualization of data delivers people effectiveness and clarity for understanding the result of analyzing. By the way, visualization has a role as the GUI (Graphical User Interface) that supports communications between people and analysis systems. Usually to make development and maintenance easier, these GUI parts should be loosely coupled from the parts of processing and analyzing data. And also to implement a loosely coupled architecture, it is necessary to adopt design patterns such as MVC (Model-View-Controller) which is designed for minimizing coupling between UI part and data processing part. On the other hand, big data can be classified as structured data and unstructured data. The visualization of structured data is relatively easy to unstructured data. For all that, as it has been spread out that the people utilize and analyze unstructured data, they usually develop the visualization system only for each project to overcome the limitation traditional visualization system for structured data. Furthermore, for text data which covers a huge part of unstructured data, visualization of data is more difficult. It results from the complexity of technology for analyzing text data as like linguistic analysis, text mining, social network analysis, and so on. And also those technologies are not standardized. This situation makes it more difficult to reuse the visualization system of a project to other projects. We assume that the reason is lack of commonality design of visualization system considering to expanse it to other system. In our research, we suggest a common information model for visualizing text data and propose a comprehensive and reusable framework, TexVizu, for visualizing text data. At first, we survey representative researches in text visualization era. And also we identify common elements for text visualization and common patterns among various cases of its. And then we review and analyze elements and patterns with three different viewpoints as structural viewpoint, interactive viewpoint, and semantic viewpoint. And then we design an integrated model of text data which represent elements for visualization. The structural viewpoint is for identifying structural element from various text documents as like title, author, body, and so on. The interactive viewpoint is for identifying the types of relations and interactions between text documents as like post, comment, reply and so on. The semantic viewpoint is for identifying semantic elements which extracted from analyzing text data linguistically and are represented as tags for classifying types of entity as like people, place or location, time, event and so on. After then we extract and choose common requirements for visualizing text data. The requirements are categorized as four types which are structure information, content information, relation information, trend information. Each type of requirements comprised with required visualization techniques, data and goal (what to know). These requirements are common and key requirement for design a framework which keep that a visualization system are loosely coupled from data processing or analyzing system. Finally we designed a common text visualization framework, TexVizu which is reusable and expansible for various visualization projects by collaborating with various Text Data Loader and Analytical Text Data Visualizer via common interfaces as like ITextDataLoader and IATDProvider. And also TexVisu is comprised with Analytical Text Data Model, Analytical Text Data Storage and Analytical Text Data Controller. In this framework, external components are the specifications of required interfaces for collaborating with this framework. As an experiment, we also adopt this framework into two text visualization systems as like a social opinion mining system and an online news analysis system.

• Korean Journal of Construction Engineering and Management
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• v.16 no.6
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• pp.63-72
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• 2015

The importance of the appropriate use and procurement of Safety and Health Expense has been increasing along with the recent increase of construction projects in height, size and complexity. However, the current standards for deducting the Safety and Health Expense have shown limitations in applying the properties and environment of the construction project due to its Safety and Health Expense Rate's classification method. Therefore, the purpose of this study is to develop a prediction model for the Safety and Health Expense that enables the consideration of different environment and properties of construction projects. The study uses multiple regression analysis to analyze the Safety and Health Expense of Ordinary(A) of less than 0.5 billion WON. The research results have shown that the use of multiple regression analysis reduces the error rate to 4.38% which the current standard calculation method have shown 18.48%. Therefore, the use of the suggested model provides reliable Safety and Health Expense prediction values that considers the properties of the project. It is expected that the results of this study contributes to the effective safety management by providing the appropriate amount of Safety and Health Expense to the project. In this study, only projects of less than 5 billion WON have been considered in the analysis. Therefore, more data is required for future studies to suggest an overall Safety and Health Expense predict ion model that covers the whole construction industry.