• The Research Journal of the Costume Culture
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• v.32 no.4
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• pp.517-546
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• 2024

The aim of this study is to investigate the fashion industry's response to climate change and how these discussions unfolded at the 28th Conference of the Parties (COP28) to the United Nations Framework Convention on Climate Change (UNFCCC). Climate change response projects by B Corp-certified fashion companies are examined, focusing on stakeholder efforts and reviewing online media reports. Text data were collected from web documents, interviews, and op-eds relating to COP28 from December 2018 to April 2024 and analyzed using text mining and semantic network analysis to identify critical keywords and contexts. The analysis revealed that the fashion industry is fulfilling its environmental responsibilities through various strategies, prompting changes in consumer behavior by advocating sustainable consumption, including carbon removal, energy transition, and recycling promotion. Stakeholders in online media and those present at COP28 discussed issues relating to climate change in the fashion industry, focusing on environmental protection, energy, greenhouse gas emissions, sustainable material usage, and social responsibility. Key issues at COP28 included policy and regulation, climate change response, energy transition, carbon emissions management, and environmental, social, and governance (ESG) standards. Additionally, by examining the main collections exhibited at the fashion show during COP28, the study analyzed how messages about climate change were conveyed. Fashion companies communicated the industry's response through exhibitions and fashion shows, suggesting a move toward balancing environmental protection and economic growth through the development of sustainable materials, the expansion of recycling and reuse practices, and the modern reinterpretation of cultural heritage.

• Journal of the Health Care and Life Science
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• v.9 no.2
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• pp.317-326
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• 2021

The purpose of this study is to understand more deeply the life of a woman with mental disabilities who experienced divorce through oral data analysis and to derive a therapeutic plan. Women with mental disorders diagnosed with depression and panic disorder participated in the study, and the text for life history analysis consisted of oral data interviews. Based on the life history analysis framework proposed by Mandelbaum (1973), the text was analyzed into three conceptual frameworks: 'Dimensions', 'Turning point', and 'Adaptation'. As a result of data analysis, the 'Dimensions' was analyzed as "Abandoned child", "The onset of mental illness", "Religion", and "Crime". In the 'Turning point' analysis, "Divorce" and "Life separation" were analyzed, and "Adaptation" was derived as "Rationalization". Based on the results of this study, a therapeutic intervention plan was proposed for the life of a female mentally disabled woman who has experienced divorce.

• Journal of KIISE:Databases
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• v.35 no.6
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• pp.495-504
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• 2008

Recently, there has been much work on supporting keyword search not only for text documents, but a]so for structured data such as relational data, XML data, and RDF data. In this paper, we propose an efficient keyword search method for RDF data. The proposed method first groups related nodes and edges in RDF data graphs to reduce data sizes for efficient keyword search and to allow relevant information to be returned together in the query answers. The proposed method also utilizes the semantics in RDF data to measure the relevancy of nodes and edges with respect to keywords for search result ranking. The experimental results based on real RDF data show that the proposed method reduces RDF data about in half and is at most 5 times faster than the previous methods.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.2
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• pp.184-190
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• 2008

This paper describes a message model based on XML (eXtensible Markup Language) to present real-time data from sensors and instruments at manufacturing processes for web service. HTML (Hyper Text Markup Language) is inadequate for describing real-time data from process control plants while it is suitable for displaying non-real-time multimedia data on web. For XML-based web service of process data, XML format for the data presentation was proposed after investigating data of various instruments at steel-making plants. Considering transmission delay inevitably caused from increased message length and processing delay from transformation of raw data into defined format, which was critical for operation of a real-time system, its performance was evaluated by simulation. In the simulation, we assumed two implementation models for conducting the transformation function. In one model, transformation was done at an SCC (Supervisory Control Computer) after receiving real-time data from instruments. In the other model, transformation had been carried out at instruments before the data were transmitted to the SCC. Various tests had been conducted under different conditions of offered loads and data lengths and their results were described.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3055-3073
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• 2019

In this paper, a new user identification method is presented using real environmental human-computer-interaction (HCI) behavior data to improve method usability. User behavior data in this paper are collected continuously without setting experimental scenes such as text length, action number, etc. To illustrate the characteristics of real environmental HCI data, probability density distribution and performance of keyboard and mouse data are analyzed through the random sampling method and Support Vector Machine(SVM) algorithm. Based on the analysis of HCI behavior data in a real environment, the Multiple Kernel Learning (MKL) method is first used for user HCI behavior identification due to the heterogeneity of keyboard and mouse data. All possible kernel methods are compared to determine the MKL algorithm's parameters to ensure the robustness of the algorithm. Data analysis results show that keyboard data have a narrower range of probability density distribution than mouse data. Keyboard data have better performance with a 1-min time window, while that of mouse data is achieved with a 10-min time window. Finally, experiments using the MKL algorithm with three global polynomial kernels and ten local Gaussian kernels achieve a user identification accuracy of 83.03% in a real environmental HCI dataset, which demonstrates that the proposed method achieves an encouraging performance.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.97-113
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• 2016

Due to the growth of internet data and the rapid development of internet technology, "big data" analysis is actively conducted to analyze enormous data for various purposes. Especially in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of existing structured data analysis. Various studies on sentiment analysis, the part of text mining techniques, are actively studied to score opinions based on the distribution of polarity of words in documents. Usually, the sentiment analysis uses sentiment dictionary contains positivity and negativity of vocabularies. As a part of such studies, this study tries to construct sentiment dictionary which is customized to specific data domain. Using a common sentiment dictionary for sentiment analysis without considering data domain characteristic cannot reflect contextual expression only used in the specific data domain. So, we can expect using a modified sentiment dictionary customized to data domain can lead the improvement of sentiment analysis efficiency. Therefore, this study aims to suggest a way to construct customized dictionary to reflect characteristics of data domain. Especially, in this study, movie review data are divided by genre and construct genre-customized dictionaries. The performance of customized dictionary in sentiment analysis is compared with a common sentiment dictionary. In this study, IMDb data are chosen as the subject of analysis, and movie reviews are categorized by genre. Six genres in IMDb, 'action', 'animation', 'comedy', 'drama', 'horror', and 'sci-fi' are selected. Five highest ranking movies and five lowest ranking movies per genre are selected as training data set and two years' movie data from 2012 September 2012 to June 2014 are collected as test data set. Using SO-PMI (Semantic Orientation from Point-wise Mutual Information) technique, we build customized sentiment dictionary per genre and compare prediction accuracy on review rating. As a result of the analysis, the prediction using customized dictionaries improves prediction accuracy. The performance improvement is 2.82% in overall and is statistical significant. Especially, the customized dictionary on 'sci-fi' leads the highest accuracy improvement among six genres. Even though this study shows the usefulness of customized dictionaries in sentiment analysis, further studies are required to generalize the results. In this study, we only consider adjectives as additional terms in customized sentiment dictionary. Other part of text such as verb and adverb can be considered to improve sentiment analysis performance. Also, we need to apply customized sentiment dictionary to other domain such as product reviews.

• Genomics & Informatics
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• v.9 no.1
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• pp.19-27
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• 2011

Gene Expression Omnibus (GEO) has kept the largest amount of gene-expression microarray data that have grown exponentially. Microarray data in GEO have been generated in many different formats and often lack standardized annotation and documentation. It is hard to know if preprocessing has been applied to a dataset or not and in what way. Standard-based integration of heterogeneous data formats and metadata is necessary for comprehensive data query, analysis and mining. We attempted to integrate the heterogeneous microarray data in GEO based on Minimum Information About a Microarray Experiment (MIAME) standard. We unified the data fields of GEO Data table and mapped the attributes of GEO metadata into MIAME elements. We also discriminated non-preprocessed raw datasets from others and processed ones by using a two-step classification method. Most of the procedures were developed as semi-automated algorithms with some degree of text mining techniques. We localized 2,967 Platforms, 4,867 Series and 103,590 Samples with covering 279 organisms, integrated them into a standard-based relational schema and developed a comprehensive query interface to extract. Our tool, GEOQuest is available at http://www.snubi.org/software/GEOQuest/.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.630-636
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• 2004

According to the increase of audio and video data utilization, the presentation of multimedia data contents and the work of retrieving, storing and manipulating a multimedia data have been the focus of recent work. The display for multimedia data should retrieve and access the contents easily that users want to present. This study is about the design and implementation of a system to retrieve multimedia data based on the contents of documentation or the caption information of a multimedia data for retrieving documentation including multimedia data. It intends to develop an filtering step to retrieve all of keyword within the caption information of multimedia data and text of a documentation. Also, the system is designed to retrieve a large amount of data quickly using an inverted file structure available for B+ tree.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.670-674
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• 2018

Social network service (SNS) related data including comments/text, images, videos, blogs, and user experiences contain a wealth of information which can be used to build recommendation systems for various clients' and provide insightful data/results to business analysts. Multimedia data, especially visual data like image and videos are the richest source of SNS data which can reflect particular region, and cultures values/interests, form a gigantic portion of the overall data. Mining such huge amounts of data for extracting actionable intelligence require efficient and smart data analysis methods. The purpose of this paper is to focus on this particular modality for devising ways to model, index, and retrieve data as and when desired.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.3
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• pp.42-49
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• 2023

Not only in aviation industry but also in other industries, safety data plays a key role to improve the level of safety performance. By analyzing safety data such as aviation safety report (text data), hazard can be identified and removed before it leads to a tragic accident. However, pre-processing of raw data (or natural language data) collected from each site should be carried out first to utilize proactive or predictive safety management system. As air traffic volume increases, the amount of data accumulated is also on the rise. Accordingly, there are clear limitation in analyzing data directly by manpower. In this paper, a topic prediction model for aviation safety mandatory report is proposed. In addition, the prediction accuracy of the proposed model was also verified using actual aviation safety mandatory report data. This research model is meaningful in that it not only effectively supports the current aviation safety mandatory report analysis work, but also can be applied to various data produced in the aviation safety field in the future.