• 한국체육학회지인문사회과학편
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• v.56 no.6
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• pp.595-605
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• 2017

The purpose of this study is to investigate what kind of research fields are preferred by the researcher of the Korean Physical Education Society using the Mecab-ko morpheme analysis and whether there are differences in the interests of researchers between the humanities and social sciences and natural sciences. A total of the data collected for this study are 5,014 papers published online from March 2002 to March 2017 in the Korean Journal of Physical Education was collected. In this study, we used Mecab-ko morpheme analyzer to extract the keyword from the collected documents. As a result, the study found that the number of papers published in KAHPERD appeared to be decreasing. It was also that the main concern of researchers in KAHPERD toward was leisure, live sports and health were relatively higher than the improvement of performance. The research subjects that were interested in the research were women, middle-aged and elderly. The study found that researchers in the humanities and social sciences have shown interest in both traditional research and social interests, while researchers in the natural sciences have shown an interest in a deeper study of traditional research. In conclusion, in order to realize the revitalization of sports convergence research, it is necessary to establish standards for the field of study which should focus on the depth and breadth of research.

• 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.

• Korean Journal of Heritage: History & Science
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• v.49 no.2
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• pp.142-157
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• 2016

In 2008, the oldest wooden tablets, in the Baekje area, were uncovered from the Bogam-ri site in Naju. This paper defines wooden tablets to as objects with inking inscriptions. Of 65 wooden tablets contained in the excavation report of this site, this paper examines the meanings of 13 tablets with inscriptions written in ink by comparing them with other tablets found in the Baekje area. All tablets were unearthed from Pit Feature No. 1, a large-scale feature, at this site. Vertical stratigraphy of the feature is divided into 43 layers; but it seems that it does not reflect the chronological order, since unearthed artefacts, including wooden tablets, pottery, and roof tiles, turned out to be produced at the same age. Wooden tablets were not found in other features, and intentionally buried in this feature. Typological characteristics of wooden tablets indicate that the pit was the secondary refuse place. The inscription of the wooden tablets labeled 'gyeongonyeon(庚午年)' and the radiocarbon dates of them indicate that these tablets were created in the early 7th century AD, centered in 610 AD. On the basis of contents and typological characteristics, these are classified into six documents, six tags, and one tablet for other purpose. Total 89 pieces of wooden tablets have been unearthed in the Baekje area. Except tablets found in Naju and Geumsan, all have been collected in palaces, royal gardens, and temples inside and outside of the Sabi Capital. The significant wooden tablets of Baekje, which can be compared with tablets from Bogam-ri, were unearthed at from the Gwanbuk-ri site, the Gungnamji site, and the Ssangbuk-ri 280-5 site. Comparative studies on wooden tablets have revealed that the place name during the Wungjin Commandery Period, the status marking method standardized in the order of place name, official rank and person's name, the fact that Baekje operated the system of prefecture(郡), and Bogam-ri was one of the places where prefecture was established, and the evidence of family register system. Wooden tablets at Bogam-ri record the documented date (610 AD), the documented place (Duhilseong where the prefecture established), and the writers (advisors and staffs of the prefecture). The recorded contents of them are invaluable data showing the local administrative system of Baekje, such as the status marking method, the means of description, the family-register system, and the land surveying system.