• The Journal of the Korea Contents Association
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• v.17 no.10
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• pp.241-249
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• 2017

Gender innovation refers to the act of producing more trustworthy science and technology based on gender analysis in conducting and development. Science and technology have been considered to be irrelevant to gender and gender analysis. Science and technology lost opportunity to market and sometimes caused massive losses due to the ignorance of gender and the concept of gender differences. Despite importance in the field of science and technology, the reason why gender innovation does not take place is the lack of woman science in science and technology. Further, the promotion of female scientists policy bas been pursued by the government is one of the reasons. The government has been forcibly carried out without considering 'performanceism' spreading in the scientific and technological fields. This research argues that scientific research brings gender perspective back into all the level of research conduct. In order to improve gender ignorance in science and technology, gender sensitive education should be provided to scientists. Also, gender governance and women's committee for gender analysis should be established to adopt gender analysis in scientific research.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.163-173
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• 2016

Recently, massive amounts of stream data have been studied for distributed processing. In this paper, we propose an incremental stream data processing method based on in-memory in big data environments. The proposed method stores input data in a temporary queue and compare them with data in a master node. If the data is in the master node, the proposed method reuses the previous processing results located in the node chosen by the master node. If there are no previous results of data in the node, the proposed method processes the data and stores the result in a separate node. We also propose a job scheduling technique considering the load and performance of a node. In order to show the superiority of the proposed method, we compare it with the existing method in terms of query processing time. Our experimental results show that our method outperforms the existing method in terms of query processing time.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.595-598
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• 2007

Vast amount of XML documents raise interests in how they will be used and how far their usage can be expanded. This paper has two central goals: 1) easy and fast retrieval of XML documents or relevant elements; and 2) efficient and stable management of large-size XML documents. The keys to develop such a practical system are how to segment a large XML document to smaller fragments and how to store them. In order to achieve these goals, we designed SCOF(Service-centric Object Fragmentation) model, which is a semi-decomposition method based on conversion rules provided by XML database managers. Keyword-based search using SCOF model then retrieves the specific elements or attributes of XML documents, just as typical XML query language does. Even though this approach needs the wisdom of managers in XML document collection, SCOF model makes it efficient both retrieval and management of massive XML documents.

• The Journal of the Korea Contents Association
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• v.17 no.12
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• pp.226-235
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• 2017

Reflecting times, Korean TV dramas have gone through massive changes. So have their main characters. This study is about their jobs, which have become more professional as well as diverse. It is observed that the male characters" jobs and job-related episodes take the center stage in the stories of dramas, rather than love stories of those characters. While main characters' jobs used to be part of the overall backdrop in the past, it has been the latest trend for a drama to begin building conflicts around and in the meticulously described work settings. This is opening up the possibility for new categories of genre dramas, as opposed to the typical Koran melodramas. For further success of this newly burgeoning trend, the sense of reality matters the most. Then, it requires elaborately built narratives, based upon a high level of expertise of playwrights in the relevant fields, and realistic proper image processing techniques.

• The Journal of the Korea Contents Association
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• v.7 no.8
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• pp.13-20
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• 2007

Decision tree is one of the most useful analysis methods for various data mining functions, including prediction, classification, etc, from massive data. Decision tree grows by splitting nodes, during which the purity increases. It is needed to stop splitting nodes when the purity does not increase effectively or new leaves does not contain meaningful number of records. Pruning is done if a branch does not show certain level of performance. By pruning, the structure of decision tree is changed and it is implied that the previous splitting of the parent node was not effective. It is also implied that the splitting of the ancestor nodes were not effective and the choices of attributes and criteria in splitting them were not successful. It should be noticed that new attributes or criteria might be selected to split such nodes for better tries. In this paper, we suggest a procedure to modify decision tree by Fuzzy theory and splitting as an integrated approach.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.149-158
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• 2017

Big data have been presenting us with exciting opportunities and challenges in economic development. For instance, in the agriculture sector, mixing up of various agricultural data (e.g., weather data, soil data, etc.), and subsequently analyzing these data deliver valuable and helpful information to farmers and agribusinesses. However, massive data in agriculture are generated in every minute through multiple kinds of devices and services such as sensors and agricultural web markets. It leads to the challenges of big data problem including data collection, data storage, and data analysis. Although some systems have been proposed to address this problem, they are still restricted either in the type of data, the type of storage, or the size of data they can handle. In this paper, we propose a novel design of a platform for collecting and analyzing agricultural big data. The proposed platform supports (1) multiple methods of collecting data from various data sources using Flume and MapReduce; (2) multiple choices of data storage including HDFS, HBase, and Hive; and (3) big data analysis modules with Spark and Hadoop.

• Journal of Digital Contents Society
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• v.17 no.4
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• pp.219-226
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• 2016

In our general life, what we recognize information with our human eyes and use it is diverse and massive. But even the current technologies improved by artificial intelligence are exorbitantly deficient comparing to human visual processing ability. Nevertheless, many researchers are trying to get information in everyday life, especially concentrate effort on recognizing information consisted of text. In the fields of recognizing text, to extract the text from the general document is used in some information processing fields, but to extract and recognize the text from real image is deficient too much yet. It is because the real images have many properties like color, size, orientation and something in common. In this paper, we applies an adaptive edge enhanced MSER(Maximally Stable Extremal Regions) to extract the text area in those diverse environments and the scene text, and show that the proposed method is a comparatively nice method with experiments.

• The Journal of the Korea Contents Association
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• v.7 no.12
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• pp.292-302
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• 2007

Thousands of studies on predicting information and products that are suitable for customers' preference have been actively proceeding. In massive information, unnecessary information should be removed to satisfy customers' needs. This Information filtering has been proceeding with several methods such as content-based and collaborative filtering etc. These conventional filtering methods have scarcity and scalability problems. Thus, this paper proposes a recommendation system using BPN to solve them. Data obtained by survey questionnaire are used as training data of neural network. The recommendation system using neural network is expected to recommend suitable products because it creates optimal network. Finally, the prototype for recommendation system based on neural network is proposed to collect data and recommend appropriate methods through survey questionnaire. As a result, this research improved the problems of conventional information filtering.

• The Journal of the Korea Contents Association
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• v.7 no.12
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• pp.355-364
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• 2007

The paradigm of war is basically changed by development of information and communication technologies. One of the paradigms in future war is NCW(Network-Centric Warfare) that is purposed of achievement in information-superiority. In this future war, the level of menace and fragility is rapidly increased in information-security according to the massive information and complex system. Therefore the Korean army is developing the information-security technologies for NCW. But, until now patent analysis concerning NCW has not performed. In this paper, we suggest a meaningful data for efficient R&D through patent analysis of information-security technologies on NCW.

• International Journal of Contents
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• v.15 no.4
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• pp.65-73
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• 2019

Social media is a massive dataset in which individuals' thoughts are freely recorded. So there have been a variety of efforts to analyze it and to understand the social phenomenon. In this study, Twitter was used to define the moments when negative perceptions of the Korean Meteorological Administration (KMA) were displayed and the reasons people were dissatisfied with the KMA. Machine learning methods were used for sentiment analysis to automatically train the implied awareness on Twitter which mentioned the KMA July-October 2011-2014. The trained models were used to validate sentiments on Twitter 2015-2016, and the frequency of negative sentiments was compared with the satisfaction of forecast users. It was found that the frequency of the negative sentiments increased before satisfaction decreased sharply. And the tweet keywords and the news headlines were qualitatively compared to analyze the cause of negative sentiments. As a result, it was revealed that the individual caused the increase in the monthly negative sentiments increase in 2016. This study represents the value of sentiment analysis that can complement user satisfaction surveys. Also, combining Twitter and news headlines provided the idea of analyzing the causes of dissatisfaction that are difficult to identify with only satisfaction surveys. The results contribute to improving user satisfaction with weather services by efficiently managing changes in satisfaction.