• Journal of the Korean Institute of Landscape Architecture
• /
• v.44 no.1
• /
• pp.16-28
• /
• 2016

The study was conducted to compare and analyze the importance and performance of leisure destination selection attributes of persons who use recreational forests and rural tourism villages. This researcher investigated the use patterns of users to identify the ground for connection between recreational forest and rural tourism village, analyzed their recognition differences in physical selection attribute, program selection attribute, and service selection attribute in order for leisure destination selection, and conducted importance-performance analysis(IPA analysis) to draw a plan for connection. The main results and suggestions are presented as follows. First, recreational forests were visited by family users in order for rest and emotional cultivation and provided experience programs using simple public interest function of forest, whereas rural tourism villages were visited by family users, friends and co-workers, groups and club members to experience a variety of annual programs and understand regional cultures. It was found that it was necessary to connect natural forest with rural tourism village in order to meet the leisure needs of the people changed in diversified ways. Secondly, it was found that the connection between rural tourism village and recreational forest visited mainly for simple rest led to positive visit intention of users. It was expected that there will be various kinds of uses, including experience program participation, child education, and safe accommodations security. In other words, the connection between recreational forest and rural tourism village is an alternative to trigger actual demands and recreational forest activities with high quality. Thirdly, in the case of users of recreational forests, their performance of all selection attributes was lower than their importance of them. Therefore, overall improvements were needed. In particular, needed were the diversity, benefit, and promotion of programs, improvements in locality(themes), supply of lodges and convenient facilities, booking system, the purchase system of local special products, and professional skills of operators and managers. On contrary, the performance of program selection attribute of rural tourism village was high. Therefore, it was found that program attribute of rural tourism village was the main connection factor to activate recreational forest use. Fourthly, according to IPA analysis, the proper connections between loges, convenient facilities, and nearby touristattractions, which give high expectations and satisfaction to users, needed to remain. And it was required to make common efforts to accomplish the goal (income creation) of rural tourism village and improve booking system for visitors and performance of local special products sales opportunity. In addition, the essential factors to induce users' leisure destination selection were found to be maintenance of the use fee system of recreational forest, diversity of rural tourism village program, and retention of locality.

• Journal of Intelligence and Information Systems
• /
• v.25 no.4
• /
• pp.123-139
• /
• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.