• Journal of the Korean Society of School Health
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• v.5 no.2
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• pp.74-90
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• 1992

The purpose of this study is to know the effects on school lunch service programme of elementary school in rural area, by using the group consisting of the sixth year students in the schools that have provided them with the lunch for six years or longer(male student:312, & female student:324), while using the comparing group consisting of the sixth year students in the schools that have not provided them with the school lunch under their similar living condition(male student: 306 & female student:322). In addition, this study was carried out by examining all continued information about their height and weight shown in the developmetal record for six years from the 1st to 6th year, and by checking their eating habits on the basis of questionnaires. The result of this study is summarized as follows. As the result of comparing the values of their height and weight grown for 6 years, it was shown that the height of the male group provided with school lunch is 27.8 cm while the male group without lunch is 27.1 cm. And the female group provided with school lunch indicated the growing value of 29.9 cm while the group without lunch did 28.4 cm. Then, it appeared that both male and female groups provided with school lunch show higher growing values of 0.7 cm, respectively, and 1.5 cm than these groups without lunch. Also, the weight of the group without lunch was 14.8 kg. Moreover, the weight of the female group provided with school lunch was 16.9 kg while the group without lunch was 17.2 kg. Then, it was shown that the male group provided with school lunch indicates heavier growing value of 0.9 kg than the group without lunch while the female group without lunch does heavier value of 0.3 kg than the group provided with school lunch. It's figure showed that although this distribution according to percentile in the 1st year is similar to the standard regular curve it is positioned in the upper group(more thatn 70%) divided centering around 50% in the 6th year, of which distribution of children provided with school lunch was higher. When comparing the values of physical status in the 6th year, it was also shown that male children with school lunch are better than these children without lunch in jumping, throwing, chinning and lifting while female children are better than these children without lunch only in jumping, which were a significant difference. In addition, the group provided with lunch showed distribution of the higher physical grade. The result of analysis on their breakfast indicated that the children with every morning breakfast account for 67.6% of the group provided with school lunch while the group without lunch for 57.8%. Regarding the reason that they do not have the breakfast, the group with school lunch answered "Because of habits"(50.7%) while the group without lunch did "Because they have no appetite"(58.9%). When comparing the degree of preference for hot or salty food, it was apparent that these children with school lunch generally tend to prefer less hot or sailty food. With respect to the frequency and place of their eating between meals, it was shown that about 70.0% of both groups has the eating between meals, more than one time a day. Then, the group with school lunch had the eating between meals at home(45.2%) while the group without lunch did it in the process of returning to home(48.4%). Regarding the degree of their preference for a certain food, it was shown that more children of the group with school lunch do not prefer a food to others. Also, their eating attitude indicated that such children as eating the food with chat after completely swallowing food and with T.V watching are larger and lower among the group with school lunch, which showed a remarkable defference from the group without lunch. With respect to their sanitary habits such as hand washing and toothing, these children who always wash their hand before eating, accounted for 84.4 % of the group provided with school lunch while the group without lunch did for 63.6%, of which the female group with school lunch indicated a remarkable difference. The actual condition of their nutrition education showed that these children who answered "Received this education" accounted for 78.0% of the group with school lunch while the group without lunch accounted for 57.5%.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.