• The Journal of the Korea Contents Association
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• v.7 no.5
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• pp.138-145
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• 2007

Mobile network evolution and development of USN technologies introduce new business model based on context awareness. Cellular operators provide friend finding service using cell based location information and telematics service using GPS location information. Recently cellular operators provide yellow page service based cell based location information. In this paper, we develop mobile tour application on WIPI platform based on location information. Mobile tour information services provide the best information based on context awareness using user location information from LBS(Location Based Service) Platform, season, weather conditions, time from Web server, and personal preference information stored in database. Mobile tour information service application is developed on WIPI platform.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.149-169
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• 2018

Korea is currently accumulating a large amount of data in public institutions based on the public data open policy and the "Government 3.0". Especially, a lot of data is accumulated in the tourism field. However, the academic discussions utilizing the tourism data are still limited. Moreover, the openness of the data of restaurants, hotels, and online tourism information, and how to use SNS Big Data in tourism are still limited. Therefore, utilization through tourism big data analysis is still low. In this paper, we tried to analyze influencing factors on foreign tourists' satisfaction in Korea through numerical data using data mining technique and R programming technique. In this study, we tried to find ways to revitalize the tourism industry by analyzing about 36,000 big data of the "Survey on the actual situation of foreign tourists from 2013 to 2015" surveyed by the Korea Culture & Tourism Research Institute. To do this, we analyzed the factors that have high influence on the 'Satisfaction', 'Revisit intention', and 'Recommendation' variables of foreign tourists. Furthermore, we analyzed the practical influences of the variables that are mentioned above. As a procedure of this study, we first integrated survey data of foreign tourists conducted by Korea Culture & Tourism Research Institute, which is stored in the tourist information system from 2013 to 2015, and eliminate unnecessary variables that are inconsistent with the research purpose among the integrated data. Some variables were modified to improve the accuracy of the analysis. And we analyzed the factors affecting the dependent variables by using data-mining methods: decision tree(C5.0, CART, CHAID, QUEST), artificial neural network, and logistic regression analysis of SPSS IBM Modeler 16.0. The seven variables that have the greatest effect on each dependent variable were derived. As a result of data analysis, it was found that seven major variables influencing 'overall satisfaction' were sightseeing spot attraction, food satisfaction, accommodation satisfaction, traffic satisfaction, guide service satisfaction, number of visiting places, and country. Variables that had a great influence appeared food satisfaction and sightseeing spot attraction. The seven variables that had the greatest influence on 'revisit intention' were the country, travel motivation, activity, food satisfaction, best activity, guide service satisfaction and sightseeing spot attraction. The most influential variables were food satisfaction and travel motivation for Korean style. Lastly, the seven variables that have the greatest influence on the 'recommendation intention' were the country, sightseeing spot attraction, number of visiting places, food satisfaction, activity, tour guide service satisfaction and cost. And then the variables that had the greatest influence were the country, sightseeing spot attraction, and food satisfaction. In addition, in order to grasp the influence of each independent variables more deeply, we used R programming to identify the influence of independent variables. As a result, it was found that the food satisfaction and sightseeing spot attraction were higher than other variables in overall satisfaction and had a greater effect than other influential variables. Revisit intention had a higher ${\beta}$ value in the travel motive as the purpose of Korean Wave than other variables. It will be necessary to have a policy that will lead to a substantial revisit of tourists by enhancing tourist attractions for the purpose of Korean Wave. Lastly, the recommendation had the same result of satisfaction as the sightseeing spot attraction and food satisfaction have higher ${\beta}$ value than other variables. From this analysis, we found that 'food satisfaction' and 'sightseeing spot attraction' variables were the common factors to influence three dependent variables that are mentioned above('Overall satisfaction', 'Revisit intention' and 'Recommendation'), and that those factors affected the satisfaction of travel in Korea significantly. The purpose of this study is to examine how to activate foreign tourists in Korea through big data analysis. It is expected to be used as basic data for analyzing tourism data and establishing effective tourism policy. It is expected to be used as a material to establish an activation plan that can contribute to tourism development in Korea in the future.

• Journal of the Korean Geographical Society
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• v.44 no.3
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• pp.372-394
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• 2009

This study investigates the ontology-based public participation GIS(PPGIS). The major reason that ontology-based GIS has attracted attention in semantic communication in recent year is due to the wide availability of geographical variable and the imminent need for turning such recommendation into useful geographical knowledge. Therefore, this study has been focused on designing and implementing the pilot tested system for public participation GIS. The applicability of the pilot tested was validated through a simulation experiment for history tourism in Guri city Gyeongi-do, Focused on the methodology, the life cycle model which involves regional statues and user recognition, can be viewed as an important preprocessing step(specification, conceptualization, formalization, integration and implementation) for recommended geographical knowledge discovery by axiom. Focusing on practicality, ontology in this study would be recommended for geographical knowledge through reasoning. In addition, ontology-based public participation GIS would show integration epistemological and ontological approach, and be utilized as an index which is connected with semantic communication. The results of the pilot system was applied to the study area, which was a part of scenario. The model was carried out using axiom of logical constraint in the meaning of human-activity.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.05a
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• pp.243-248
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• 2005

우리나라의 농업은 주곡의 지속적인 자급기반을 마련하고 이제 단순한 농사형태에서 벗어나 새로운 소득작목을 개발하고 합리적인 경영개선을 통하여 소득을 높이는 상업농시대에 접어들게 되었다. 따라서 상업농시대에는 농업도 새로운 기술을 바탕으로 질 높은 농산물을 안정적으로 생산공급할 수 있는 기술의 확보가 더욱 요구되고 있다. 최근 농산물수입개방 시대에 접어들게 되면서 우리 농산물도 국제경쟁력향상을 위한 품질향상 및 새로운 소득작물의 개발이 절실히 요구되는 시점에 와있다. 여러 지방지치단체에서 그 지방에 알맞은 소득작물을 개발하여 많은 소득을 올리고 있는 사례를 많이 볼 수 있다. 예로서 보성의 녹차, 무안의 무화과, 구례의 오이 등이 대표적인 예라고 할 수 있다. 여러 농업연구기관에서도 과학적인 토양연구로 세부정밀토양조사를 실시하고 작물재배의 부적지에 대해서는 토양개량방법을 연구하고 있으며 그에 따른 시비추천과 소득작물의 적지적작추천을 하고 있다. 국내에서도 GIS의 발전 및 RS의 발전과 더불어 친환경농업의 일환으로 정밀농업분야에 많은 관심이 집중되고 있다. 정밀농업은 농업생산기술 분야에서 아직 생소한 접근방법으로서 여기에서는 지구측위시스템(GPS)와 지리정보시스템(GIS), 원격탐사(RS)기법들이 많이 응용되고 있다. 또한 농업의 1차(식량생산)적인 목적에서 2차, 3차(소득증대 및 부가가치 증대)적인 목적으로 변화되고 있다. 각 지자체에서 그 지역에 적합한 농산물 재배를 위한 재배단지 확산방안 연구가 진행중에 있으며, 과거 토양속성인자를 가지고 재배적지 선정을 해왔으나, 본 연구에서는 GIS와 RS을 중첩하여 적지선정에 관한 최적의 시스템을 구현하였다. 이런 적지 선정을 통하여 유기농업의 실현을 도모하여 소비자의 욕구에 맞는 작물 생산 및 농촌관광단지 조성을 통해 부가가치증대 및 소득증대를 꾀함으로 농촌문제 해결에 도움이 될 것으로 기대된다. 본 연구를 통해 GIS 와 RS의 기술이 농촌분야에 더 효율적으로 적용될 것으로 기대되며, 농업기술센터를 통한 정보제공을 함으로써 대농민 서비스 및 농업기관의 위상이 제고 될 것으로 기대된다.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.159-185
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• 2016

Planning a multi-day trip is a complex, yet time-consuming task. It usually starts with selecting a list of points of interest (POIs) worth visiting and then arranging them into an itinerary, taking into consideration various constraints and preferences. When choosing POIs to visit, one might ask friends to suggest them, search for information on the Web, or seek advice from travel agents; however, those options have their limitations. First, the knowledge of friends is limited to the places they have visited. Second, the tourism information on the internet may be vast, but at the same time, might cause one to invest a lot of time reading and filtering the information. Lastly, travel agents might be biased towards providers of certain travel products when suggesting itineraries. In recent years, many researchers have tried to deal with the huge amount of tourism information available on the internet. They explored the wisdom of the crowd through overwhelming images shared by people on social media sites. Furthermore, trip planning problems are usually formulated as 'Tourist Trip Design Problems', and are solved using various search algorithms with heuristics. Various recommendation systems with various techniques have been set up to cope with the overwhelming tourism information available on the internet. Prediction models of recommendation systems are typically built using a large dataset. However, sometimes such a dataset is not always available. For other models, especially those that require input from people, human computation has emerged as a powerful and inexpensive approach. This study proposes CYTRIP (Crowdsource Your TRIP), a multi-day trip itinerary planning system that draws on the collective intelligence of contributors in recommending POIs. In order to enable the crowd to collaboratively recommend POIs to users, CYTRIP provides a shared workspace. In the shared workspace, the crowd can recommend as many POIs to as many requesters as they can, and they can also vote on the POIs recommended by other people when they find them interesting. In CYTRIP, anyone can make a contribution by recommending POIs to requesters based on requesters' specified preferences. CYTRIP takes input on the recommended POIs to build a multi-day trip itinerary taking into account the user's preferences, the various time constraints, and the locations. The input then becomes a multi-day trip planning problem that is formulated in Planning Domain Definition Language 3 (PDDL3). A sequence of actions formulated in a domain file is used to achieve the goals in the planning problem, which are the recommended POIs to be visited. The multi-day trip planning problem is a highly constrained problem. Sometimes, it is not feasible to visit all the recommended POIs with the limited resources available, such as the time the user can spend. In order to cope with an unachievable goal that can result in no solution for the other goals, CYTRIP selects a set of feasible POIs prior to the planning process. The planning problem is created for the selected POIs and fed into the planner. The solution returned by the planner is then parsed into a multi-day trip itinerary and displayed to the user on a map. The proposed system is implemented as a web-based application built using PHP on a CodeIgniter Web Framework. In order to evaluate the proposed system, an online experiment was conducted. From the online experiment, results show that with the help of the contributors, CYTRIP can plan and generate a multi-day trip itinerary that is tailored to the users' preferences and bound by their constraints, such as location or time constraints. The contributors also find that CYTRIP is a useful tool for collecting POIs from the crowd and planning a multi-day trip.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.185-205
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• 2023

As the tourism industry recovers post the COVID-19 pandemic, an increasing number of tourists are utilizing various platforms to leave reviews. However, amidst the vast amount of data, finding useful information remains challenging, often leading to time and cost inefficiencies in selecting travel destinations. Despite ongoing research, there are limitations due to the absence of ratings or the presence of different rating formats across platforms. Moreover, inconsistencies between ratings and the content of reviews pose challenges in developing recommendation models. To address these issues, this study utilized 7,104 reviews of tourist spots in Jeju Island to develop a specialized satisfaction index for Jeju tourist attractions and employed this index to construct a 'Rating Prediction Model.' To validate the model's performance, we predicted the ratings of 700 experimental data points using both the developed model and an LSTM approach. The proposed model demonstrated superior performance with a weighted accuracy of 73.87%, which is approximately 4.67% higher than that of the LSTM. The results of this study are expected to resolve the discrepancies between ratings and review contents, standardize ratings in reviews without ratings or in various formats, and provide reliable rating indicators applicable across all areas of travel in different domains.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.537-546
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• 2014

The purpose of this study is to develope an algorithm which generates trip plans based on rating points of travel app users and travel experts to help potential travellers experience their desired destinations in advance. This algorithm uses the above rating points and the gradually created hierarchy to generate the most preferred and efficient trip courses. Users can go through video clips or panoramic VR videos of the actual destinations from their trip plans generated by the algorithm which may add excitement to their actual trips. With our heuristic methods, the more users input their ratings, the better trip plans can be generated. This algorithm has been tested on android OS and proven efficient in generating trip plans. This research introduces a way to experience travel destinations with panoramic VR video and proposes the algorithm which generates trip plans based on users' ratings. It is expected to be useful for travellers' trip planning and to contribute growth in the travel market.