• The Journal of Bigdata
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• v.5 no.1
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• pp.155-180
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• 2020

It takes the most important role the problem of assigining vehicles and desigining optimal routes for each vehicle in order to enhance the logistics service level. While solving the problem, various cost factors such as number of vehicles, the capacity of vehicles, total travelling distance, should be considered at the same time. Although most of logistics service providers introduced the Transportation Management System (TMS), the system has the limitation which can not consider the practical constraints. In order to make the solution of TMS applicable, it is required experts revised the solution of TMS based on their own experience and intuition. In this research, different from previous research which have focused on minimizing the total cost, it has been proposed the methodology which can enhance the efficiency and fairness of asset utilization, simultaneously. First of all, it has been adopted the Cluster-First Route-Second (CFRS) approach. Based on the location of customers, we have grouped customers as clusters by using four different clustering algorithm such as K-Means, K-Medoids, DBSCAN, Model-based clustering and a procedural approach, Fisher & Jaikumar algorithm. After getting the result of clustering, it has been developed the optiamal vehicle routes within clusters. Based on the result of numerical experiments, it can be said that the propsed approach based on CFRS may guarantee the better performance in terms of total travelling time and distance. At the same time, the variance of travelling distance and number of visiting customers among vehicles, it can be concluded that the proposed approach can guarantee the better performance of assigning tasks in terms of fairness.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.113-126
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• 2017

This study aimed to quantitatively analyze the trends in environmental research that utilize environmental geospatial information through text mining, one of the big data analysis technologies. The analysis was conducted on a total of 869 papers published in the Republic of Korea, which were collected from the National Digital Science Library (NDSL). On the basis of the classification scheme, the keywords extracted from the papers were recategorized into 10 environmental fields including "general environment", "climate", "air quality", and 20 environmental geospatial information fields including "satellite image", "numerical map", and "disaster". With the recategorized keywords, their frequency levels and time series changes in the collected papers were analyzed, as well as the association rules between keywords. First, the results of frequency analysis showed that "general environment"(40.85%) and "satellite image"(24.87%) had the highest frequency levels among environmental fields and environmental geospatial information fields, respectively. Second, the results of the time series analysis on environmental fields showed that the share of "climate" between 1996 and 2000 was high, but since 2001, that of "general environment" has increased. In terms of environmental geospatial information fields, the demand for "satellite image" was highest throughout the period analyzed, and its utilization share has also gradually increased. Third, a total of 80 correlation rules were generated for environmental fields and environmental geospatial information fields. Among environmental fields, "general environment" generated the highest number of correlation rules (17) with environmental geospatial information fields such as "satellite image" and "digital map".

• The Korean Journal of Archival Studies
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• no.39
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• pp.101-138
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• 2014

This study examined the influence and meaning of SNS as the hot topic of our time from the archival perspective and also studied the 'SNS records management'. The many users mean a high accessibility and utilization of SNS, which increase the influence and value of SNS as a record. Politically, SNS is a tool that strengthens the communication among the voters, politicians and the public while economically, it is a window to accept the complaints of the customers and a marketing tool. In addition, the voices of social minorities are also recorded unlike in the traditional media, which makes the SNS record a method to gain the social variety and diversity. SNS is a place of formation of collective memory and collective memory itself. Furthermore, it can play the role of public sphere. It also is a place for generation of 'big data' in an archival sense. In addition, this study has classified the SNS records management into primary and secondary management that include record management entities, subjects, periods, methods, and causes. This study analyzed the history, status, and the meaning of SNS to assess the values and meanings as the preliminary study for the future SNS record management studies.

• Journal of the Korean GEO-environmental Society
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• v.22 no.3
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• pp.45-51
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• 2021

The evaluation of the degree of compaction of the embankment area, which accounts for most of highway earthworks, is generally performed by a flat plate loading test. The plate loading test is a traditional test method and has high reliability in the field. However, as reaction force equipment must be carried out and it takes about 40 minutes per site during the test, there may be limitations in managing the entire expanse of earthworks. Meanwhile, in order to overcome this, the Ministry of Land, Infrastructure and Transport proposed a simple method of evaluating the level of compactness in the provisional guidelines for compaction management of the packaging infrastructure in 2010. However, it has not been utilized at the highway construction site until now, 10 years later. Therefore, this study attempted to verify the utility of the compaction evaluation method using LFWD (Light Falling Weight Deflectometer) of the impact loading method among the test methods suggested in the provisional guideline. To this end, the correlation was derived by conducting a plate loading test and an LFWD test for each site property and compaction degree. As a result of the test, there was no consistency of test data in the ground with a relative compaction of 80% or less. However, it was confirmed that the correlation has a tendency to increase beyond that. If the test method or test equipment is improved to ensure the consistency of the test values of the impact loading method in the future, it will play a big role in solving the blind spot for compaction management in the earthworks.

• Journal of Korean Library and Information Science Society
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• v.52 no.1
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• pp.155-178
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• 2021

In the era of the 4th industrial revolution, public libraries need a strategy for promoting intelligent library services in order to actively respond to changes in the external environment such as artificial intelligence. Therefore, in this study, based on the concept of artificial intelligence and analysis of domestic and foreign artificial intelligence related trends, policies, and cases, we proposed the future direction of introduction and development of artificial intelligence services in the library. Currently, the library operates a reference information service that automatically provides answers through the introduction of artificial intelligence technologies such as deep learning and natural language processing, and develops a big data-based AI book recommendation and automatic book inspection system to increase business utilization and provide customized services for users. Has been provided. In the field of companies and industries, regardless of domestic and overseas, we are developing and servicing technologies based on autonomous driving using artificial intelligence, personal customization, etc., and providing optimal results by self-learning information using deep learning. It is developed in the form of an equation. Accordingly, in the future, libraries will utilize artificial intelligence to recommend personalized books based on the user's usage records, recommend reading and culture programs, and introduce real-time delivery services through transport methods such as autonomous drones and cars in the case of book delivery service. Service development should be promoted.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.207-228
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• 2023

After COVID-19, organizations have widely adopted platforms such as zoom or developed their proprietary online real-time systems for remote work, with recent forays into incorporating the metaverse for meetings and publicity. While ongoing studies investigate the impact of avatar customization, expansive virtual environments, and past virtual experiences on participant satisfaction within virtual reality or metaverse settings, the utilization of the metaverse as a dedicated workspace is still an evolving area. There exists a notable gap in research concerning the factors influencing the performance of the metaverse as a workspace, particularly in non-immersive work-type metaverses. Unlike studies focusing on immersive virtual reality or metaverses emphasizing immersion and presence, the majority of contemporary work-oriented metaverses tend to be non-immersive. As such, understanding the factors that contribute to the success of these existing non-immersive metaverses becomes crucial. Hence, this paper aims to empirically analyze the factors impacting personal outcomes in the non-immersive metaverse workspace and derive implications from the results. To achieve this, the study adopts the Embodied Social Presence (ESP) model as a theoretical foundation, modifying and proposing a research model tailored to the non-immersive metaverse workspace. The findings validate that the impact of presence on task engagement and task involvement exhibits a moderating effect based on the metaverse platform used. Following interviews with participants engaged in non-immersive metaverse workplaces (specifically Gather Town and Ifland), a survey was conducted to gather comprehensive insights.

• Korean Journal of Human Ecology
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• v.14 no.2
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• pp.313-319
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• 2005

This survey was conducted in order to obtain the basic data for desirable consumption habits through investigation and analysis of university students' fast food consumption behaviors. Questionnaires were collected from a total of 374 male and female students living in big or small and medium-sized cities in August, 2004. The contents surveyed were utilization and expenses of fast foods, choice of fast foods, relationship between fast foods and a diet, and characteristics of fast food restaurants. The results obtained are summarized as follows: 1. The ratio of the surveyees varied according to gender, residence, and the size of a city they're living in. For example, males took up 48.66% of the surveyees, while females did 51.34%. The ratio of residents in apartments and stand-alone houses was 54.81% and 45.19% each. 47.33% of the respondents were living in big cities, while 52.67% of them in small and medium-sized cities. 2. 70.1% of the surveyees responded that they are with friends when having fast foods. There was a highly significant difference between male and female in the type of eating companions (p<0.001). The average number of days that they eat fast foods was 1 to 2 times a week, which accounted for 63.7% of the respondents. However, in the case of eating foods, there was no significant differences between two sexes. 3. 64.2% of the surveyees paid more than 20,000 won to buy fast foods for a week, which showed no significant differences between genders. They tend to split a bill, rather than one person pays all. There was a highly significant difference between genders in paying method (p<0.001). 4. 52.1 % of the respondents chose a menu themselves. Their most favored food was chickens (26.5%), which showed a statistically significant difference between genders (p<0.001). 46.8% of them preferred coke as a drink, which had no significant difference between genders. 42.2% of the surveyees had fast foods between lunch and dinner, which also had no significant difference between genders. The most important factor in choosing a menu was its taste (62.8%), which indicated a significant difference between males and females (p<0.05). 5. The preference to fast foods was due to the influence of western culture (36.4%) and eating-out habits (29.1%), which was significantly different between genders (p<0.05). Those who eat fast foods answered they have normal weight and normal body type (49.5%). 24.3% of them were relatively fat with significant difference between genders (p<0.05). 63.4% of the surveyees thought themselves not picky with foods, and there was a significant difference between genders (p<0.05). 78.3% of them mostly preferred franchise restaurants because they are convenient and cheap.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.85-105
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• 2021

In this study, a case study was conducted on Advanced Nano Products Co.,Ltd, a company that was established in 2000 and has the core technology to produce and commercialize nano materials and ultrafine nano powders based on nano technology. Deviating from the general case study, a case study analysis frame was set based on the theory of technology management and industry-university cooperation theory, and cases were analyzed. In this case study, Advanced Nano Products Co.,Ltd. was analyzed from two analytical perspectives: the establishment of a Management Of Technology system within the company and the Industry-Academic Cooperation activity. Based on this theoretical-based analysis framework, company visit interviews and related data research and analysis were conducted. As a result of the study of the case company, it was possible to derive how the technology management and industry-university cooperation affect the growth stage of the company as follows. First, the strategic use of technology management is an important factor in strengthening the competitive advantage and core competencies of venture companies, and for survival and growth of startups in the early stages. Second, strategic use of technology management and patents and establishment of a patent management system are a part of business strategy and play a pivotal role in corporate performance. Third, the human and material infrastructure of universities affects the growth of companies in the early stage of start-up, and the high utilization of industry-university cooperation promotes the growth of companies. Fourth, continuous industry-academic cooperation activities in the growth and maturity stages of a company's growth stage are the basis for activating external exchanges and building networks. Lastly, technology management and industry-university cooperation were found to be growth factors for each growth stage of a company. In order for a company to develop continuously from the start-up to the growth and maturity stages, it is necessary to establish a technology management system from the beginning and promote strategic technology management activities. In addition, it can be said that it is important to carry out various industry-academic cooperation activities outside the company. As a result of the case analysis, it was found that Advanced Nano Products Co.,Ltd, which performed these two major activities well, overcame the crisis step by step and continued to grow until now. This study shows how the use of technology management and industry-academic cooperation creates value in each growth stage of technology-based venture companies. In addition, its active use will play a big role in the growth of other venture companies. The results of this case study can be a valid reference for growth research of technology start-up venture companies and related field application and utilization.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.