• Management & Information Systems Review
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• v.36 no.3
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• pp.131-150
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• 2017

The purpose of this study is to observe the spread pattern of open source software (OSS) while establishing relations with surrounding actors during its operation period. In order to investigate the change pattern of participants in the OSS, we use a netnography on the basis of online data, which can trace the change patterns of the OSS depending on the passage of time. For this, the cases of three OSSs (e.g. jQuery, MooTools, and YUI), which are JavaScript frameworks, were compared, and the corresponding data were collected from the open application programming interface (API) of GitHub as well as blog and web searches. This research utilizes the translation process of the actor-network theory to categorize the stages of the change patterns on the OSS translation process. In the project commencement stage, we identified the type of three different OSS-related actors and defined associated relationships among them. The period, when a master commences a project at first, is refined through the course for the maintenance of source codes with persons concerned (i.e. project growth stage). Thereafter, the period when the users have gone through the observation and learning period by being exposed to promotion activities and codes usage respectively, and becoming to active participants, is regarded as the 'leap of participants' stage. Our results emphasize the importance of promotion processes in participants' selection of the OSS for participation and confirm the crowding-out effect that the rapid speed of OSS development retarded the emergence of participants.

• Spatial Information Research
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• v.22 no.3
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• pp.23-24
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• 2014

NGII (National Geographic Information Institute) has been providing VRS (Virtual Reference Station) service so that could determine precise positioning in real time since 2007. However, since the VRS service has to maintain the connected status with VRS server, the number of users who can use VRS service are limited by capacity of VRS server. To solve this problem, NGII has been providing FKP (Virtual Reference Station) service using one way telecommunication from November 1, 2012. Therefore, it is predicted that the usage of FKP service will increase in public surveying and cadastral resurveying in the future. However, the studies with respect to analysis of FKP precision for applying to public surveying and cadastral resurveying is not conducted enough. In this study, to analyse the application possibility of FKP on the public surveying and cadastral resurveying, the two kind analysis were performed. First is the analysis of accuracy according to the configuration of reference station of FKP and VRS. One is consisted of same reference stations, another is consisted of different reference stations. Second is the accuracy anlalysis of horizontal and vertical positioning acquiring VRS and FKP data in various measurement environment based on VRS regulation. Result of first study, Positioning accuracy according to the configuration of the reference stations satisfies related regulation. However, accuracy of FKP in case of different reference stations is worse than in case of same reference stations.. The result of second test shows that the horizontal precision of FKP and VRS in good measurement environment satisfy the allowed precision. However, in some case, horizontal precision of FKP and VRS in poor measurement environment exceed the allowed precision. In addition, the number of exceeding the allowed precision in the FKP is more than the VRS. The vertical precision of the VRS satisfy related work provision. In conclusion, the result of this study shows that the FKP only in open area should be used for public survey and cadastral resurvey. Therefore the additional studies with respect to the improvement of FKP precision should be conducted.

• Progress in Medical Physics
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• v.25 no.4
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• pp.264-270
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• 2014

This study developed a portable respiratory training device to improve breathing stability, which is an important element in using the CyberKnife Synchrony respiratory tracking device, one of the typical Stereotactic Radiation Therapy (SRT) devices. It produced an interface for users to be able to select one of two displays, a graph type and a bar type, supported an auditory system that helps them expect next respiration by improving a sense of rhythm of their respiratory period, and provided comfortable respiratory inducement. By targeting 5 applicants and applying individual respiratory period detected through a self-developed program, it acquired signal data of 'guide respiration' that induces breathing through signal data gained from 'free respiration' and an auditory system, and evaluated the usability by comparing deviation average values of respiratory period and respiratory amplitude. It could be identified that respiratory period decreased $55.74{\pm}0.14%$ compared to free respiration, and respiratory amplitude decreased $28.12{\pm}0.10%$ compared to free respiration, which confirmed the consistency and stability of respiratory. SBRT, developed based on these results, using the portable respiratory training device, for liver cancer or lung cancer, is evaluated to be able to help reduce delayed treatment time due to respiratory instability and improve treatment accuracy, and if it could be applied to developing respiratory training applications targeting an android-based portable device in the future, even use convenience and economic efficiency are expected.

• The Journal of Information Systems
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• v.25 no.1
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• pp.1-23
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• 2016

Purpose Self-congruity deals with the effect of symbolic value-expressive attributes on consumer decision and behavior, which is the theoretical foundation of the "non-utilitarian destination positioning". Functional congruity refers to utilitarian evaluation of a product or service by consumers. In addition, recent years, social network services, especially mobile social network services have created many opportunities for e-WOM communication that enables consumers to share personal consumption related information anywhere at any time. Moreover, self-construal is a hot and popular topic that has been discussed in the field of modem psychology as well as in marketing area. This study aims to examine the moderating effect of self-construal on the relationship between self-congruity, functional congruity and tourists' positive electronic word of mouth (e-WOM). Design/methodology/approach In order to verify the hypotheses, we developed a questionnaire with 32 survey items. We measured all the items on a five-point Likert-type scale. We used Sojump.com to collect questionnaire and gathered 218 responses from whom have visited Korea before. After a pilot test, we analyzed the main survey data by using SPSS 20.0 and AMOS 18.0, and employed structural equation modeling to test the hypotheses. We first estimated the measurement model for its overall fit, reliability and validity through a confirmatory factor analysis and used common method bias test to make sure that whether measures are affected by common-method variance. Then we tested the hypotheses through the structural model and used regression analysis to measure moderating effect of self-construal. Findings The results reveal that the effect of self-congruity on tourists' positive e-WOM is stronger for tourists with an independent self-construal compared with those with interdependent self-construal. Moreover, it shows that the effect of functional congruity on tourists' positive e-WOM becomes salient when tourists' self-construal is primed to be interdependent rather than independent. We expect that the results of this study can provide important implications for academic and practical perspective.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.19-28
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• 2012

After the internet era, we are moving to the ubiquitous society. Nowadays the people are interested in the multimodal interaction technology, which enables audience to naturally interact with the computing environment at the exhibitions such as gallery, museum, and park. Also, there are other attempts to provide additional service based on the location information of the audience, or to improve and deploy interaction between subjects and audience by analyzing the using pattern of the people. In order to provide multimodal interaction service to the audience at the exhibition, it is important to distinguish the individuals and trace their location and route. For the location tracking on the outside, GPS is widely used nowadays. GPS is able to get the real time location of the subjects moving fast, so this is one of the important technologies in the field requiring location tracking service. However, as GPS uses the location tracking method using satellites, the service cannot be used on the inside, because it cannot catch the satellite signal. For this reason, the studies about inside location tracking are going on using very short range communication service such as ZigBee, UWB, RFID, as well as using mobile communication network and wireless lan service. However these technologies have shortcomings in that the audience needs to use additional sensor device and it becomes difficult and expensive as the density of the target area gets higher. In addition, the usual exhibition environment has many obstacles for the network, which makes the performance of the system to fall. Above all these things, the biggest problem is that the interaction method using the devices based on the old technologies cannot provide natural service to the users. Plus the system uses sensor recognition method, so multiple users should equip the devices. Therefore, there is the limitation in the number of the users that can use the system simultaneously. In order to make up for these shortcomings, in this study we suggest a technology that gets the exact location information of the users through the location mapping technology using Wi-Fi and 3d camera of the smartphones. We applied the signal amplitude of access point using wireless lan, to develop inside location tracking system with lower price. AP is cheaper than other devices used in other tracking techniques, and by installing the software to the user's mobile device it can be directly used as the tracking system device. We used the Microsoft Kinect sensor for the 3D Camera. Kinect is equippedwith the function discriminating the depth and human information inside the shooting area. Therefore it is appropriate to extract user's body, vector, and acceleration information with low price. We confirm the location of the audience using the cell ID obtained from the Wi-Fi signal. By using smartphones as the basic device for the location service, we solve the problems of additional tagging device and provide environment that multiple users can get the interaction service simultaneously. 3d cameras located at each cell areas get the exact location and status information of the users. The 3d cameras are connected to the Camera Client, calculate the mapping information aligned to each cells, get the exact information of the users, and get the status and pattern information of the audience. The location mapping technique of Camera Client decreases the error rate that occurs on the inside location service, increases accuracy of individual discrimination in the area through the individual discrimination based on body information, and establishes the foundation of the multimodal interaction technology at the exhibition. Calculated data and information enables the users to get the appropriate interaction service through the main server.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.493-513
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• 2014

With the development of related technologies, Location-Based Services (LBS) are growing fast and being used in many ways. Past LBS studies have focused on adoption of LBS because of the fact that LBS users have privacy concerns regarding revealing their location information. Meanwhile, the number of LBS users and revenues from LBS are growing rapidly because users can get some benefits by revealing their location information. Little research has been done on how LBS affects consumers' information search behavior in product purchase. The purpose of this paper is examining the effect of LBS information filtering on buyers' uncertainty and their information search behavior. When consumers purchase a product, they try to reduce uncertainty by searching information. Generally, there are two types of uncertainties - knowledge uncertainty and choice uncertainty. Knowledge uncertainty refers to the lack of information on what kinds of alternatives are available in the market and/or their important attributes. Therefore, consumers having knowledge uncertainty will have difficulties in identifying what alternatives exist in the market to fulfil their needs. Choice uncertainty refers to the lack of information about consumers' own preferences and which alternative will fit in their needs. Therefore, consumers with choice uncertainty have difficulties selecting best product among available alternatives.. According to economics of information theory, consumers narrow the scope of information search when knowledge uncertainty is high. It is because consumers' information search cost is high when their knowledge uncertainty is high. If people do not know available alternatives and their attributes, it takes time and cognitive efforts for them to acquire information about available alternatives. Therefore, they will reduce search breadth. For people with high knowledge uncertainty, the information about products and their attributes is new and of high value for them. Therefore, they will conduct searches more in-depth because they have incentive to acquire more information. When people have high choice uncertainty, people tend to search information about more alternatives. It is because increased search breadth will improve their chances to find better alternative for them. On the other hand, since human's cognitive capacity is limited, the increased search breadth (more alternatives) will reduce the depth of information search for each alternative. Consumers with high choice uncertainty will spend less time and effort for each alternative because considering more alternatives will increase their utility. LBS provides users with the capability to screen alternatives based on the distance from them, which reduces information search costs. Therefore, it is expected that LBS will help users consider more alternatives even when they have high knowledge uncertainty. LBS provides distance information, which helps users choose alternatives appropriate for them. Therefore, users will perceive lower choice uncertainty when they use LBS. In order to test the hypotheses, we selected 80 students and assigned them to one of the two experiment groups. One group was asked to use LBS to search surrounding restaurants and the other group was asked to not use LBS to search nearby restaurants. The experimental tasks and measures items were validated in a pilot experiment. The final measurement items are shown in Appendix A. Each subject was asked to read one of the two scenarios - with or without LBS - and use a smartphone application to pick a restaurant. All behaviors on smartphone were recorded using a recording application. Search breadth was measured by the number of restaurants clicked by each subject. Search depths was measured by two metrics - the average number of sub-level pages each subject visited and the average time spent on each restaurant. The hypotheses were tested using SPSS and PLS. The results show that knowledge uncertainty reduces search breadth (H1a). However, there was no significant correlation between knowledge uncertainty and search depth (H1b). Choice uncertainty significantly reduces search depth (H2b), but no significant relationship was found between choice uncertainty and search breadth (H2a). LBS information filtering significantly reduces the buyers' choice uncertainty (H4) and reduces the negative relationship between knowledge uncertainty and search breadth (H3). This research provides some important implications for service providers. Service providers should use different strategies based on their service properties. For those service providers who are not well-known to consumers (high knowledge uncertainty) should encourage their customers to use LBS. This is because LBS would increase buyers' consideration sets when the knowledge uncertainty is high. Therefore, less known services have chances to be included in consumers' consideration sets with LBS. On the other hand, LBS information filtering decrease choice uncertainty and the near service providers are more likely to be selected than without LBS. Hence, service providers should analyze geographically approximate competitors' strength and try to reduce the gap so that they can have chances to be included in the consideration set.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.102-116
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• 2006

Electronic catalogs (or e-catalogs) hold information about the goods and services offered or requested by the participants, and consequently, form the basis of an e-commerce transaction. Catalog management is complicated by a number of factors and product classification is at the core of these issues. Classification hierarchy is used for spend analysis, custom3 regulation, and product identification. Classification is the foundation on which product databases are designed, and plays a central role in almost all aspects of management and use of product information. However, product classification has received little formal treatment in terms of underlying model, operations, and semantics. We believe that the lack of a logical model for classification Introduces a number of problems not only for the classification itself but also for the product database in general. It needs to meet diverse user views to support efficient and convenient use of product information. It needs to be changed and evolved very often without breaking consistency in the cases of introduction of new products, extinction of existing products, class reorganization, and class specialization. It also needs to be merged and mapped with other classification schemes without information loss when B2B transactions occur. For these requirements, a classification scheme should be so dynamic that it takes in them within right time and cost. The existing classification schemes widely used today such as UNSPSC and eClass, however, have a lot of limitations to meet these requirements for dynamic features of classification. In this paper, we try to understand what it means to classify products and present how best to represent classification schemes so as to capture the semantics behind the classifications and facilitate mappings between them. Product information implies a plenty of semantics such as class attributes like material, time, place, etc., and integrity constraints. In this paper, we analyze the dynamic features of product databases and the limitation of existing code based classification schemes. And describe the semantic classification model, which satisfies the requirements for dynamic features oi product databases. It provides a means to explicitly and formally express more semantics for product classes and organizes class relationships into a graph. We believe the model proposed in this paper satisfies the requirements and challenges that have been raised by previous works.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.51-66
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• 2018

As AI (Artificial Intelligence) technologies have been swiftly evolved, a lot of products and services are under development in various fields for better users' experience. On this technology advance, negative effects of AI technologies also have been discussed actively while there exists positive expectation on them at the same time. For instance, many social issues such as trolley dilemma and system security issues are being debated, whereas autonomous vehicles based on artificial intelligence have had attention in terms of stability increase. Therefore, it needs to check and analyse major social issues on artificial intelligence for their development and societal acceptance. In this paper, multi-categorical sentiment analysis is conducted over online public opinion on artificial intelligence after identifying the trending topics related to artificial intelligence for two years from January 2016 to December 2017, which include the event, match between Lee Sedol and AlphaGo. Using the largest web portal in South Korea, online news, news headlines and news comments were crawled. Considering the importance of trending topics, online public opinion was analysed into seven multiple sentimental categories comprised of anger, dislike, fear, happiness, neutrality, sadness, and surprise by topics, not only two simple positive or negative sentiment. As a result, it was found that the top sentiment is "happiness" in most events and yet sentiments on each keyword are different. In addition, when the research period was divided into four periods, the first half of 2016, the second half of the year, the first half of 2017, and the second half of the year, it is confirmed that the sentiment of 'anger' decreases as goes by time. Based on the results of this analysis, it is possible to grasp various topics and trends currently discussed on artificial intelligence, and it can be used to prepare countermeasures. We hope that we can improve to measure public opinion more precisely in the future by integrating empathy level of news comments.

• Korea Science and Art Forum
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• v.37 no.1
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• pp.249-260
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• 2019

Recently, the proportion of single-person households is on the increase in Korea, expected to reach 34.6% in 2035. Among the single-person households, Young single family households are facing greater difficulties due to high house prices in Korea. The government is expanding its support to Young single family, executing various policies such as public lease housings, private lease housings for youth, youth dormitory, etc. The purpose of this study is to understand the exact housing requirement of Young single family households who have different lifestyles with other age groups and provide base line data for youth house planning which will be in use later on. Study methods are shown below. First, this research studied the status and characteristics of Young single family households by looking into literature. Second, by studying previous studies concerned with life style and housing preferences of youth, the tool for investigating preferences and needs of housing environment by Young single family households was composed. Third, survey on characterstics of space usage, preferences and needs on flat composition, and preferences of interior design were conducted based on lifestyle of Young single family-households. The survey was conducted as an online survey using SNS for 150 Young single family holds from the age of 20 and 39, including students and office workers from December 2018 to January 2019. The results are as following. (1) Looking into the space usage characteristics, considering that various activities other than basic functions take place in bedroom and living room of small-sized Young single family households, we need to consider this additionally when planning the housing. (2) Looking into the preferences and composite needs of flat composition, the subjects demand separate bed room and more living room space, and also demand expansion of living room space where various activities take place and additional storage such as dress room in bed room (3) The preferences toward interior design show preferences toward modern style and achromatic color, a representative color. The subjects also prefer floor finishing materials normally used for living spaces, and indirect, soft lighting that uses wall. Also, there are differences between interior design preferences between students (20's) and office workers(30's) due to their different lifestyles. Research is needed to propose practical residential environment requirements and plans through a case study of actual public rental housing and a wider range of users.