• Informatization Policy
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• v.29 no.1
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• pp.38-59
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• 2022

The development of IT technology and the spread of smartphones are having a great impact on businesses and consumers. Consumers have been able to acquire more diverse and larger amounts of information than in the past due to information provided through smartphones and information search on the internet, which has a significant influence on decision-making. Companies have also become sensitive to such changes in consumer behavior patterns, reflected in their marketing. In addition, among the various characteristics of smartphones, location-based technology has become an important factor in providing targeted marketing from a company's point of view. Such technological development and social change have led to the expansion of the mobile advertising market, which promotes products or services based on the location of consumers and provides benefits such as discount coupons. In this study, we have analyzed the influence of consumer characteristics on intention to use in mobile location-based advertising, which has become an important marketing method in the mobile advertising market. The effects of variables of personalization, engagement, coupon proneness, economic efficiency, and irritation on attitude and information privacy concerns were analyzed, and the effects of attitude and information privacy concerns on intention to use were analyzed. The results of this study are deemed to be able to suggest factors to consider when providing mobile location-based advertising to consumers in the future.

• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.83-94
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• 2004

Recent researches which purport to Integrate distributed information have been concentrated on developing efficient mediator systems that not only provide a high degree of autonomy for local users but also support the flexible integration of required functions for global users. However, there has been little attention on how to evaluate a global query in a mediator. A global query is transformed into a set of its sub-queries and each sub-query is the unit of evaluation in a remote server. Therefore, it is possible to speed up the execution of a global query if the previous results of frequently evaluated sub-queries are materialized in a mediator. Since the Integration schema of a mediator can be incrementally modified and the evaluation frequency of a global query can also be continuously varied, query usage should be carefully monitored to determine the optimized set of materialized sub-queries. Furthermore, as the number of sub-queries increases, the optimization process itself may take too long, so that the optimized set Identified by a long optimization process nay become obsolete due to the recent change of query usage. This paper proposes the adaptive selection of materialized sub-queries such that available storage in a mediator can be highly utilized at any time. In order to differentiate the recent usage of a query from the past, the accumulated usage frequency of a query decays as time goes by.

• Economic and Environmental Geology
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• v.54 no.3
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• pp.373-387
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• 2021

In this study, the impact of clustered groundwater usage facilities and the proper amount of groundwater usage in the Daejeong-Hangyeong watershed of Jeju island were evaluated based on the data-driven analysis methods. As the applied data, groundwater level data; the corresponding precipitation data; the groundwater usage amount data (Jeoji, Geumak, Seogwang, and English-education city facilities) were used. The results show that the Geumak usage facility has a large influence centering on the corresponding location; the Seogwang usage facility affects on the downstream area; the English-education usage facility has a great impact around the upstream of the location; the Jeoji usage facility shows an influence around the up- and down-streams of the location. Overall, the influence of operating the clustered groundwater usage facilities in the watershed is prolonged to approximately 5km. Additionally, the appropriate groundwater usage amount to maintain the groundwater base-level was analyzed corresponding to the precipitation. Considering the recent precipitation pattern, there is a need to limit the current amount of groundwater usage to 80%. With increasing the precipitation by 100mm, additional groundwater development of approximately 1,500m³-1,900m³ would be reasonable. All the results of the developed data-driven estimation model can be used as useful information for sustainable groundwater development in the Daejeong-Hangyeong watershed of Jeju island.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.12-23
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• 2021

Due to high proportion of exports, Korea has a higher dependence on the global value chain (GVC) than other major developed countries. This reason, Korea has a structure that is sensitive to GVC changes. This is because Korean exports are concentrated on specific countries and items, and most of the materials for export tend to depend on imports. Currently, export restrictions resulting from trade disputes with Japan can affect the industry of Korea as a whole due to the supply of core materials. Therefore, in order to minimize economic damage caused by export regulations in the current situation, it is necessary to reorganize the GVC, through efforts to rapidly diversify imports and localize imports that depend on Japan. To this end, it is necessary to derive and classify imported goods that depend on Japan, and to localize items that are difficult to diversify imports, and prompt R&D investment is required for this. This study aims to support R&D investment policy through quantitative analysis based on big data rather than a decision-making method based on expert-centered qualitative analysis.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.697-723
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• 2022

Data-driven models to predict groundwater levels 30 days in advance were developed for 12 groundwater monitoring stations in the middle-Jeju watershed, Jeju Island. Stacked long short-term memory (stacked-LSTM), a deep learning technique suitable for time series forecasting, was used for model development. Daily time series data from 2001 to 2022 for precipitation, groundwater usage amount, and groundwater level were considered. Various models were proposed that used different combinations of the input data types and varying lengths of previous time series data for each input variable. A general procedure for deep-learning-based model development is suggested based on consideration of the comparative validation results of the tested models. A model using precipitation, groundwater usage amount, and previous groundwater level data as input variables outperformed any model neglecting one or more of these data categories. Using extended sequences of these past data improved the predictions, possibly owing to the long delay time between precipitation and groundwater recharge, which results from the deep groundwater level in Jeju Island. However, limiting the range of considered groundwater usage data that significantly affected the groundwater level fluctuation (rather than using all the groundwater usage data) improved the performance of the predictive model. The developed models can predict the future groundwater level based on the current amount of precipitation and groundwater use. Therefore, the models provide information on the soundness of the aquifer system, which will help to prepare management plans to maintain appropriate groundwater quantities.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.121-145
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• 2010

University timetabling depending on the educational environments of universities is an NP-hard problem that the amount of computation required to find solutions increases exponentially with the problem size. For many years, there have been lots of studies on university timetabling from the necessity of automatic timetable generation for students' convenience and effective lesson, and for the effective allocation of subjects, lecturers, and classrooms. Timetables are classified into a course timetable and an examination timetable. This study focuses on the former. In general, a course timetable for liberal arts is scheduled by the office of academic affairs and a course timetable for major subjects is scheduled by each department of a university. We found several problems from the analysis of current course timetabling in departments. First, it is time-consuming and inefficient for each department to do the routine and repetitive timetabling work manually. Second, many classes are concentrated into several time slots in a timetable. This tendency decreases the effectiveness of students' classes. Third, several major subjects might overlap some required subjects in liberal arts at the same time slots in the timetable. In this case, it is required that students should choose only one from the overlapped subjects. Fourth, many subjects are lectured by same lecturers every year and most of lecturers prefer the same time slots for the subjects compared with last year. This means that it will be helpful if departments reuse the previous timetables. To solve such problems and support the effective course timetabling in each department, this study proposes a university timetabling support system based on two phases. In the first phase, each department generates a timetable template from the most similar timetable case, which is based on case-based reasoning. In the second phase, the department schedules a timetable with the help of interactive user interface under the timetabling criteria, which is based on rule-based approach. This study provides the illustrations of Hanshin University. We classified timetabling criteria into intrinsic and extrinsic criteria. In intrinsic criteria, there are three criteria related to lecturer, class, and classroom which are all hard constraints. In extrinsic criteria, there are four criteria related to 'the numbers of lesson hours' by the lecturer, 'prohibition of lecture allocation to specific day-hours' for committee members, 'the number of subjects in the same day-hour,' and 'the use of common classrooms.' In 'the numbers of lesson hours' by the lecturer, there are three kinds of criteria : 'minimum number of lesson hours per week,' 'maximum number of lesson hours per week,' 'maximum number of lesson hours per day.' Extrinsic criteria are also all hard constraints except for 'minimum number of lesson hours per week' considered as a soft constraint. In addition, we proposed two indices for measuring similarities between subjects of current semester and subjects of the previous timetables, and for evaluating distribution degrees of a scheduled timetable. Similarity is measured by comparison of two attributes-subject name and its lecturer-between current semester and a previous semester. The index of distribution degree, based on information entropy, indicates a distribution of subjects in the timetable. To show this study's viability, we implemented a prototype system and performed experiments with the real data of Hanshin University. Average similarity from the most similar cases of all departments was estimated as 41.72%. It means that a timetable template generated from the most similar case will be helpful. Through sensitivity analysis, the result shows that distribution degree will increase if we set 'the number of subjects in the same day-hour' to more than 90%.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.

• Journal of Korean Society of Forest Science
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• v.113 no.1
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• pp.40-50
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• 2024

Pine forests are continuously declining due to competition with broadleaf trees, such as oaks, as a consequence of changes in the natural dynamics of forest ecosystem. This natural decline creates a risk of losing the various benefits pine trees have provided to people in the past. Therefore, it is necessary to prepare future forest management directions by considering the state of pine tree decline in each region. The goal of this study is to understand the characteristics of pine forest changes according to forest dynamics and to predict future regional changes. For this purpose, we evaluated the trend of change in pine forests and extracted various variables(topography, forest stand type, disturbance, and climate) that affect the change, using time-series National Forest Inventory (NFI) data. Also, using selected key variables, a model was developed to predict future changes in pine forests. As a results, it showed that the importance of pine trees in forests across the country has decreased overall over the past 10 years. Also, 75% of the sample points representing pine trees remained unchanged, while the remaining 25% had changed to mixed forests. It was found that these changes mainly occurred in areas with good moisture conditions or disturbance factors inside and outside the forest. In the next 10 years, approximately 14.2% of current pine forests was predicted to convert to mixed forests due to changes in natural forest dynamics. Regionally, the rate of pine forest change was highest in Jeju(42.8%) and Gyeonggi(26.9%) and lowest in Gyeongbuk(8.8%) and Gangwon(13.8%). It was predicted that pine forests would be at a high risk of decline in western areas of the Korean Peninsula, including Gyeonggi, Chungcheong, and Jeonnam. This results can be used to make a management plan for pine forests throughout the country.

• Journal of the Korean Geographical Society
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• v.41 no.1 s.112
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• pp.106-120
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• 2006

Natural or man-made disaster has been expected to be one of the potential themes that can integrate human geography and physical geography. Typhoons like Rusa and Maemi caused great loss to insurance companies as well as public sectors. We have implemented a natural disaster management system for a private insurance company to produce better estimation of hazards from high wind as well as calculate vulnerability of damage. Climatic gauge sites and addresses of contract's objects were geo-coded and the pressure values along all the typhoon tracks were vectorized into line objects. National GIS topog raphic maps with scale of 1: 5,000 were updated into base maps and digital elevation model with 30 meter space and land cover maps were used for reflecting roughness of land to wind velocity. All the data are converted to grid coverage with $1km{\times}1km$. Vulnerability curve of Munich Re was ad opted, and preprocessor and postprocessor of wind velocity model was implemented. Overlapping the location of contracts on the grid value coverage can show the relative risk, with given scenario. The wind velocities calculated by the model were compared with observed value (average $R^2=0.68$). The calibration of wind speed models was done by dropping two climatic gauge data, which enhanced $R^2$ values. The comparison of calculated loss with actual historical loss of the insurance company showed both underestimation and overestimation. This system enables the company to have quantitative data for optimizing the re-insurance ratio, to have a plan to allocate enterprise resources and to upgrade the international creditability of the company. A flood model, storm surge model and flash flood model are being added, at last, combined disaster vulnerability will be calculated for a total disaster management system.