• Information Systems Review
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• v.21 no.1
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• pp.135-152
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• 2019

Recently there are many development and support policies for start-up companies because of successful venture companies related to ICT services. However, as these policies have focused on the support for the initial stage of start-up, many start-up companies have difficulties to continuously grow up. The main reason for these difficulties is that they recognize start-up tasks as independent activities. However, many experts or related articles say that start-up tasks are composed of related processes from the initial stage to the stable stage of start-up firms. In this study, we models the start-up processes based on the survey collected by the start-up companies, and analyze the start-up process of ICT service companies with process mining techniques. Through process mining analysis, we can draw a sequential flow of tasks for start-ups and the characteristics of them. The analysis of start-up businessman, idea derivation, creating business model, business diversification processes are resulted as important processes, but marketing activity and managing investment funds are not. This result means that marketing activity and managing investment funds are activities that need ongoing attention. Moreover, we can find temporal and complementary tasks which could not be captured by independent individual-level activity analysis. Our process analysis results are expected to be used in simulation-based web-intelligent system to support start-up business, and more cumulated start-up business cases will be helpful to give more detailed individual-level personalization service. And our proposed process model and analyzing results can be used to solve many difficulties for start-up companies.

• Research in Plant Disease
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• v.30 no.1
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• pp.1-12
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• 2024

Since the List of Plant Diseases in Korea (DisList) was first published in 1986, the 6th edition appeared 36 years later. In 2023, the 6.1 edition, a revised and improved version of the 6th edition, was released to the public on the web free of charge. The contents of DisList increased, with the number of hosts increasing from 437 taxa to 1,420 taxa and the number of disease species increasing from 1,539 to 6,680. Among these, tree diseases are 3,586 species and their hosts include 504 taxa, providing much help to experts who need them. Meanwhile, the importance of accurate disease names continues to grow with the legalization of tree care, but many disease names are still inappropriate or misused, causing confusion. Disease names that do not follow the naming regulations are still registered, and even if the same pathogen infects hosts of the same taxa, the disease names are given differently, and there are many disease names that do not indicate the characteristics of the disease. Also, there are diseases reported without Korean names. In order to make DisList better, the review committee for disease names should establish the regulations to review and register disease names, and establish a system to review new disease names before publishing papers.

• Journal of Korean Library and Information Science Society
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• v.55 no.1
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• pp.193-214
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• 2024

This study aimed to propose a method for collecting and applying media platform based materials at the National Library of Korea. Firstly, we analyzed the current status and limitations of data collection based on domestic media platforms, including the National Library of Korea. Secondly, a literature review method was used to investigate the current status and types of digital content based on media platforms. Thirdly, we identified the types of materials based on media platforms that are not currently included in the National Central Library's online material collection guidelines through the examination of cases from major overseas libraries. Fourthly, after reviewing technical and legal elements such as the definition of collection targets and scope for each new media, and collection methods, we established collection criteria. Fifthly, based on the research results, the policies proposed in this study are as follows: 1) there is a need to establish a clear legal basis for the collection of media platform based materials; 2) the development and presentation of collection guidelines for media platform based materials is necessary; 3) the development of collection tools and infrastructure for media platform based materials is required; 4) for the collection of media platform based materials, it is necessary to obtain permission for collection from targeted social media organizations, and to cooperate in linkage with organizations that produce and service extended reality content; 5) for the service activation of media platform based materials, it is necessary to improve accessibility for the usage activation of these materials, to enhance the content extensibility and ease of use of the e-deposit system including extended reality content, and to advance and construct spaces for reproducing extended reality content.

• Journal of Korean Society of Transportation
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• v.19 no.4
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• pp.35-47
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• 2001

Traffic represents one of the largest sources of primary air pollutants in urban area. As a consequence. numerous abatement strategies are being pursued to decrease the ambient concentration of pollutants. A characteristic of most of the these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emission inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for vehicle types. The majority of inventories are compiled using passive data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. The study of current trends are towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this study, a methodology of motor vehicle emission calculation by using real-time traffic data was studied. A methodology for estimating emissions of CO at a test area in Seoul. Traffic data, which are required on a street-by-street basis, is obtained from induction loops of traffic control system. It was calculated speed-related mass of CO emission from traffic tail pipe of data from traffic system, and parameters are considered, volume, composition, average velocity, link length. And, the result was compared with that of a method of emission calculation by VKT(Vehicle Kilometer Travelled) of vehicles of category.

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.187-206
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• 2007

The development of computer and information technology has been combined with the information superhighway internet infrastructure, so information widely spreads not only in special fields but also in the daily lives of people. Information ubiquity influences the traditional way of transaction, and leads a new E-commerce which distinguishes from the existing E-commerce. Not only goods as physical but also service as non-physical come into E-commerce. As the scale of E-Commerce is being enlarged as well. It keeps people from finding information they want. Recommender systems are now becoming the main tools for E-Commerce to mitigate the information overload. Recommender systems can be defined as systems for suggesting some Items(goods or service) considering customers' interests or tastes. They are being used by E-commerce web sites to suggest products to their customers who want to find something for them and to provide them with information to help them decide which to purchase. There are several approaches of recommending goods to customer in recommender system but in this study, the main subject is focused on collaborative filtering technique. This study presents a possibility of pre-evaluation for the prediction performance of customer's preference in collaborative filtering before the process of customer's preference prediction. Pre-evaluation for the prediction performance of each customer having low performance is classified by using the statistical features of ratings rated by each customer is conducted before the prediction process. In this study, MovieLens 100K dataset is used to analyze the accuracy of classification. The classification criteria are set by using the training sets divided 80% from the 100K dataset. In the process of classification, the customers are divided into two groups, classified group and non classified group. To compare the prediction performance of classified group and non classified group, the prediction process runs the 20% test set through the Neighborhood Based Collaborative Filtering Algorithm and Correspondence Mean Algorithm. The prediction errors from those prediction algorithm are allocated to each customer and compared with each user's error. Research hypothesis : Two research hypotheses are formulated in this study to test the accuracy of the classification criterion as follows. Hypothesis 1: The estimation accuracy of groups classified according to the standard deviation of each user's ratings has significant difference. To test the Hypothesis 1, the standard deviation is calculated for each user in training set which is divided 80% from MovieLens 100K dataset. Four groups are classified according to the quartile of the each user's standard deviations. It is compared to test the estimation errors of each group which results from test set are significantly different. Hypothesis 2: The estimation accuracy of groups that are classified according to the distribution of each user's ratings have significant differences. To test the Hypothesis 2, the distributions of each user's ratings are compared with the distribution of ratings of all customers in training set which is divided 80% from MovieLens 100K dataset. It assumes that the customers whose ratings' distribution are different from that of all customers would have low performance, so six types of different distributions are set to be compared. The test groups are classified into fit group or non-fit group according to the each type of different distribution assumed. The degrees in accordance with each type of distribution and each customer's distributions are tested by the test of ${\chi}^2$ goodness-of-fit and classified two groups for testing the difference of the mean of errors. Also, the degree of goodness-of-fit with the distribution of each user's ratings and the average distribution of the ratings in the training set are closely related to the prediction errors from those prediction algorithms. Through this study, the customers who have lower performance of prediction than the rest in the system are classified by those two criteria, which are set by statistical features of customers ratings in the training set, before the prediction process.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.49-67
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• 2015

Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.

• Asia pacific journal of information systems
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• v.17 no.2
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• pp.123-137
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• 2007

The advent of the Internet and related Web technologies has created an easily accessible link between a firm and its customers, and has provided opportunities to a firm to use information technology to support supplementary after-sale services associated with a product or service. It has been widely recognized that supplementary services are an important source of customer value and of competitive advantage as the characteristics of the product itself. Many of these supplementary services are information-based and need not be co-located with the product, so more and more companies are delivering these services electronically. Net-based customer service, which is defined as an Internet-based computerized information system that delivers services to a customer, therefore, is the core infrastructure for supplementary service provision. The importance of net-based customer service in delivering supplementary after-sale services associated with product has been well documented. The strategic advantages of well-implemented net-based customer service are enhanced customer loyalty and higher lock-in of customers, and a resulting reduction in competition and the consequent increase in profits. However, not all customers utilize such net-based customer service. The digital divide is the phenomenon in our society that captures the observation that not all customers have equal access to computers. Socioeconomic factors such as race, gender, and education level are strongly related to Internet accessibility and ability to use. This is due to the differences in the ability to bear the cost of a computer, and the differences in self-efficacy in the use of a technology, among other reasons. This concept, applied to e-commerce, has been called the "e-commerce divide." High Internet penetration is not eradicating the digital divide and e-commerce divide as one would hope. Besides, to accommodate personalized support, a customer must often provide personal information to the firm. This personal information includes not only name and address, but also preferences information and perhaps valuation information. However, many recent studies show that consumers may not be willing to share information about themselves due to concerns about privacy online. Due to the e-commerce divide, and due to privacy and security concerns of the customer for sharing personal information with firms, limited numbers of customers adopt net-based customer service. The limited level of customer adoption of net-based customer service affects the firm profits and the customers' welfare. We use a game-theoretic model in which we model the net-based customer service system as a mechanism to enhance customers' loyalty. We model a market entry scenario where a firm (the incumbent) uses the net-based customer service system in inducing loyalty in its customer base. The firm sells one product through the traditional retailing channels and at a price set for these channels. Another firm (the entrant) enters the market, and having observed the price of the incumbent firm (and after deducing the loyalty levels in the customer base), chooses its price. The profits of the firms and the surplus of the two customers segments (the segment that utilizes net-based customer service and the segment that does not) are analyzed in the Stackelberg leader-follower model of competition between the firms. We find that an increase in adoption of net-based customer service by the customer base is not always desirable for firms. With low effectiveness in enhancing customer loyalty, firms prefer a high level of customer adoption of net-based customer service, because an increase in adoption rate decreases competition and increases profits. A firm in an industry where net-based customer service is highly effective loyalty mechanism, on the other hand, prefers a low level of adoption by customers.

• Journal of Hospice and Palliative Care
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• v.13 no.3
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• pp.181-189
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• 2010

Purpose: Recently, health policy making is increasingly based on evidence. Therefore, Korean Terminal Cancer Patient Information System (KTCPIS) was developed to meet such need. We aimed to report its developmental process and statistics from 6 months data. Methods: Items for KTCPIS were developed through the consultation with practitioners. E-Velos web-based clinical trial management system was used as a technical platform. Data were collected for patients who were registered to 34 inpatient palliative care services, designated by Ministry of Health, Welfare, and Family Affairs, from $1^{st}$ of January to $30^{th}$ of June in 2009. Descriptive statistics were used for the analysis. Results: From the nationally representative set of 2,940 patients, we obtained the following results. Mean age was $64.8{\pm}12.9$ years, and 56.6% were male. Lung cancer (18.0%) was most common diagnosis. Only 50.3% of patients received the confirmation of terminal diagnosis by two or more physicians, and 69.7% had an insight of terminal diagnosis at the time of admission. About half of patients were admitted to the units on their own without any formal referral. Average and worst pain scores were significantly reduced after 1 week when compared to those at the time of admission. 73.4% faced death in the units, and home-discharge comprised only 13.3%. Mean length of stay per admission was $20.2{\pm}21.2$ days, with median value of 13. Conclusion: Nationally representative data on the characteristics of patients and their caregiver, and current practice of service delivery in palliative care units were obtained through the operation of KTCPIS.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.35-51
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• 2012

Cloud computing is internet-based computing where computing resources are offered over the Internet as scalable and on-demand services. In particular, in case a number of various cloud services emerge in accordance with development of internet and mobile technology, to select and provide services with which service users satisfy is one of the important issues. Most of previous works show the limitation in the degree of user satisfaction because they are based on so called concept similarity in relation to user requirements or are lack of versatility of user preferences. This paper presents cloud service selection reasoning which can be applied to the general cloud service environments including a variety of computing resource services, not limited to web services. In relation to the service environments, there are two kinds of services: atomic service and composite service. An atomic service consists of service attributes which represent the characteristics of service such as functionality, performance, or specification. A composite service can be created by composition of atomic services and other composite services. Therefore, a composite service inherits attributes of component services. On the other hand, the main participants in providing with cloud services are service users, service suppliers, and service operators. Service suppliers can register services autonomously or in accordance with the strategic collaboration with service operators. Service users submit request queries including service name and requirements to the service management system. The service management system consists of a query processor for processing user queries, a registration manager for service registration, and a selection engine for service selection reasoning. In order to enhance the degree of user satisfaction, our reasoning stands on basis of the degree of conformance to user requirements of service attributes in terms of functionality, performance, and specification of service attributes, instead of concept similarity as in ontology-based reasoning. For this we introduce so called a service attribute graph (SAG) which is generated by considering the inclusion relationship among instances of a service attribute from several perspectives like functionality, performance, and specification. Hence, SAG is a directed graph which shows the inclusion relationships among attribute instances. Since the degree of conformance is very close to the inclusion relationship, we can say the acceptability of services depends on the closeness of inclusion relationship among corresponding attribute instances. That is, the high closeness implies the high acceptability because the degree of closeness reflects the degree of conformance among attributes instances. The degree of closeness is proportional to the path length between two vertex in SAG. The shorter path length means more close inclusion relationship than longer path length, which implies the higher degree of conformance. In addition to acceptability, in this paper, other user preferences such as priority for attributes and mandatary options are reflected for the variety of user requirements. Furthermore, to consider various types of attribute like character, number, and boolean also helps to support the variety of user requirements. Finally, according to service value to price cloud services are rated and recommended to users. One of the significances of this paper is the first try to present a graph-based selection reasoning unlike other works, while considering various user preferences in relation with service attributes.

• Journal of Life Science
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• v.26 no.8
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• pp.904-910
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• 2016

Complex traits are determined by the combined effects of many loci and are affected by gene networks or biological pathways. Systems biology approaches have an important role in the identification of candidate genes related to complex diseases or traits at the system level. The gene network analysis has been performed by diverse types of methods such as gene co-expression, gene regulatory relationships, protein-protein interaction (PPI) and genetic networks. Moreover, the network-based methods were described for predicting gene functions such as graph theoretic method, neighborhood counting based methods and weighted function. However, there are a limited number of researches in livestock. The present study systemically analyzed genes associated with 102 types of economic traits based on the Animal Trait Ontology (ATO) and identified their relationships based on the gene co-expression network and PPI network in cattle. Then, we constructed the two types of gene network databases and network visualization system (http://www.nabc.go.kr/cg). We used a gene co-expression network analysis from the bovine expression value of bovine genes to generate gene co-expression network. PPI network was constructed from Human protein reference database based on the orthologous relationship between human and cattle. Finally, candidate genes and their network relationships were identified in each trait. They were typologically centered with large degree and betweenness centrality (BC) value in the gene network. The ontle program was applied to generate the database and to visualize the gene network results. This information would serve as valuable resources for exploiting genomic functions that influence economically and agriculturally important traits in cattle.