• Journal of Information Processing Systems
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• v.10 no.4
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• pp.628-645
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• 2014

In past few years, distributed hash table (DHT)-based P2P systems have been proven to be a promising way to manage decentralized index information and provide efficient lookup services. However, the skewness of users' preferences regarding keywords contained in a multi-keyword query causes a query load imbalance that combines both routing and response load. This imbalance means long file retrieval latency that negatively influences the overall system performance. Although index replication has a great potential for alleviating this problem, existing schemes did not explicitly address it or incurred high cost. To overcome this issue, we propose, in this paper, an integrated solution that consists of three replication schemes to alleviate query load imbalance while minimizing the cost. The first scheme is an active index replication that is used in order to decrease routing load in the system and to distribute response load of an index among peers that store replicas of the index. The second scheme is a proactive pointer replication that places location information of each index to a predetermined number of peers for reducing maintenance cost between the index and its replicas. The third scheme is a passive index replication that guarantees the maximum query load of peers. The result of simulations indicates that the proposed schemes can help alleviate the query load imbalance of peers. Moreover, it was found by comparison that our schemes are more cost-effective on placing replicas than PCache and EAD.

• Journal of the Korean Society of Safety
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• v.36 no.1
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• pp.56-63
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• 2021

The narrative texts of industrial accident reports help to identify accident risk factors. They relate the accident triggers to the sequence of events and the outcomes of an accident. Particularly, a set of related keywords in the context of the narrative can represent how the accident proceeded. Previous studies on text analytics for structuring accident reports have been limited to extracting individual keywords without context. We proposed a context-based analysis using a Natural Language Processing (NLP) algorithm to remedy this shortcoming. This study aims to apply Word2Vec of the NLP algorithm to extract adjacent keywords, known as word embedding, conducted by the neural network algorithm based on supervised learning. During processing, Word2Vec is conducted by adjacent keywords in narrative texts as inputs to achieve its supervised learning; keyword weights emerge as the vectors representing the degree of neighboring among keywords. Similar keyword weights mean that the keywords are closely arranged within sentences in the narrative text. Consequently, a set of keywords that have similar weights presents similar accidents. We extracted ten accident processes containing related keywords and used them to understand the risk factors determining how an accident proceeds. This information helps identify how a checklist for an accident report should be structured.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.68-74
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• 2023

The food industry has been hit hard since the first outbreak of COVID-19 in 2019. However, as of April 2022, social distancing has been resolved and the restaurant industry has gradually recovered, interest in restaurant start-ups is increasing. Therefore, in this paper, 'restaurant start-up' was cited as a key keyword through social media big data analysis using TexTom, and word frequency and cone analysis were conducted for big data analysis. The keyword collection period was selected from May 1, 2022, when social distancing due to COVID-19 was lifted, to May 23, 2023, and based on this, a plan to develop chatbot services for restaurant start-ups was proposed. This paper was prepared in consideration of what to consider when starting a restaurant and a chatbot service that allows prospective restaurant founders to receive information more conveniently. Based on these analysis results, we expected to contribute to the process of developing chatbots for prospective restaurant founders in the future

• Journal of The Korean Association For Science Education
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• v.38 no.6
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• pp.865-874
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• 2018

The purpose of this study is to explore the process of inquiry problem finding in high school students' small group free-inquiry. For this purpose, 91 second grade high school students took part in small group free-inquiry. We conducted interviews with students (48 students in 15 groups) who were relatively successful in the inquiry performed for one semester (about 4 months). Based on the results of the interviews, we analyzed the characteristics of the inquiry problem finding through the steps and strategies in the inquiry problem finding process. The main results are as follows: First, in the inquiry problem finding process, steps such as selecting keyword, presenting an inconvenience, presenting a question, and finding an inquiry problem were found, and in particular, the process of selecting the keyword that correspond to the subject of inquiry, such as the material and situation of inquiry, is very important step in inquiry problem finding. Second, the strategies that students used in the process of finding inquiry problem included searching information, review of prior research, sharing of knowledge and experience, linking and extension of knowledge and experience, environmental awareness, expert consultation, discussion of suitability, elaboration, etc. Third, finding an inquiry problem was relatively easy in the inquiry for finding out problems (i.e. inconvenience) in everyday life and investigating ways to solve them. Fourth, the review of prior researches through the internet was useful in the process of selecting keyword and elaboration. Fifth, the factors that students consider when selecting one of several candidate inquiry problems are feasibility, real-life applicability, and economic condition. Sixth, the current affairs had a positive impact on the inquiry problem finding. Based on the above results, we discussed some ways to increase students' inquiry problem finding ability.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.189-200
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• 2021

Recently, Information and Communication Technology and SMART Phone Technology have been rapidly developed. According to the increase of data use, the era of big data has come. With the approach of non-contact society, QR Codes are becoming inseparable in our lives. In this paper, we are trying to figure out the implications of QR Code research based on Big Data in Korea. The purpose of this study is to first examine the previous studies on "QR Code" and conduct an analysis on keywords by field using Big Data. Second, for data visualization WordCloud analysis and network analysis are performed on "QR Code" frequent keyword. Third, we would like to present the research direction to future researchers regarding "QR Code". In the results, First of all, research trends showed that research is on the rise and that various fields are being utilized. Second, the results of the analysis of frequent keyword resulted in similar results overall, with some differences depending on the field and year. Third, we found that the visualization results according to the frequent keyword were also analyzed in the same way as the frequent keyword analysis results. The practical implications of the theoretical findings are as follows. First, 'QR Code' needs to be studied as a means of information delivery, not as a technical aspect. Second, it can be seen that "QR Code" is developing reflecting social trends or issues. With both theoretical and practical implications, we are trying to provide the strategic ways of QR-code in future.

• Journal of the Korean Society of Earth Science Education
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• v.13 no.3
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• pp.226-237
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• 2020

The purpose of the research is to analyze research trends related to climate change education by network analysis based on keywords extracted from the research title. For this purpose, 62 papers were selected from Korean Citation Index(KCI) journals published from 2011 to 2020 using such keywords as "climate change" and "climate change education" in the Research Information Sharing Service. The analysis procedure consisted of selection of analysis papers, keyword extraction and purification, and keyword network analysis and visualization. Textom, Ucinet 6.0, and NetDraw were used to analyze the frequency, degree centrality, and betweenness centrality. The results of the research showed that, first, Early 'Energy and Climate Change Education' had the highest frequency of papers examining climate change education. Second, the keywords/phrases that appeared most frequently in research on climate change education were "program" "energy," "analysis," "elementary school," "elementary school," "elementary school students," "development," and "impact." Third, the analysis of the centrality of betweenness centrality showed that the index of 'program', 'primary students' and 'primary schools' were the highest, and the largest group was 'development and effect of teaching and learning programs'. Based on these results, it was concluded that future research on climate change education needs to be examined in further detail and expanded into more specific areas.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.99-115
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• 2022

In this study, domestic academic journal papers on tax compliance, tax consciousness, and faithful tax payment (hereinafter referred to as "tax compliance") were comprehensively analyzed from an interdisciplinary perspective as a representative research topic in the field of tax science. To achieve the research purpose, topic modeling technique was applied as part of text mining. In the flow of data collection-keyword preprocessing-topic model analysis, potential research topics were presented from tax compliance related keywords registered by the researcher in a total of 347 papers. The results of this study can be summarized as follows. First, in the keyword analysis, keywords such as tax investigation, tax avoidance, and honest tax reporting system were included in the top 5 keywords based on simple term-frequency, and in the TF-IDF value considering the relative importance of keywords, they were also included in the top 5 keywords. On the other hand, the keyword, tax evasion, was included in the top keyword based on the TF-IDF value, whereas it was not highlighted in the simple term-frequency. Second, eight potential research topics were derived through topic modeling. The topics covered are (1) tax fairness and suppression of tax offenses, (2) the ideology of the tax law and the validity of tax policies, (3) the principle of substance over form and guarantee of tax receivables (4) tax compliance costs and tax administration services, (5) the tax returns self- assessment system and tax experts, (6) tax climate and strategic tax behavior, (7) multifaceted tax behavior and differential compliance intentions, (8) tax information system and tax resource management. The research comprehensively looked at the various perspectives on the tax compliance from an interdisciplinary perspective, thereby comprehensively grasping past research trends on tax compliance and suggesting the direction of future research.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.439-450
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• 2019

The purpose of the study was to critically explore the academic identity of dance education as a paradigm of practice-based education. Dance education is recognized as a school dance since the first curriculum was designed, which was announced by the Ministry of Education in 1955. Although Korea's dance education has 65 years of history, its academic identity as a dance education is not very strong. Traditional dance education was teacher-centered, skills-oriented, and exercise-oriented by following the practice-based paradigm. Recently, an initiative was taken to establish a new paradigm for dance education in schools, communities, and professional fields. This study followed the keyword network analysis and reviewed the main contents of each section of dance education research from a time-series perspective. The first section (1968-1979) is a practice of dance education based on physical education; the second section (1980-1989) is a creative-based movement education for primary education; the third section (1990-1999) is a systematization of dance education courses by class; the fourth section (2000-2009) is a paradigm for cultural and artistic education; the fifth section (2010-2019) consisted of various educational practices and institutions. Based on the research results, efforts are requested to establish an academic identity that can support dance education, interdisciplinary practice, and research.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.99-106
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• 2014

Generation and analysis methods have been proposed in recent years, such as using a natural language and formal language processing, artificial intelligence algorithms based knowledge model is effective meaning. its semantic based knowledge model has been used effective decision making tree and problem solving about specific context. and it was based on static generation and regression analysis, trend analysis with behavioral model, simulation support for macroeconomic forecasting mode on especially in a variety of complex systems and social network analysis. In this study, in this sense, integrating knowledge-based models, This paper propose a text mining derived from the inter-Topic model Integrated formal methods and Algorithms. First, a method for converting automatically knowledge map is derived from text mining keyword map and integrate it into the semantic knowledge model for this purpose. This paper propose an algorithm to derive a method of projecting a significant topic map from the map and the keyword semantically equivalent model. Integrated semantic-based knowledge model is available.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.75-86
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• 2020

Hotel review data can extract various information that includes purchasing factors that lead to consumption, advantages, and disadvantages for hotels. In particular, the sentiment keyword of the review data helps consumers understand the pros and cons of hotels. However, it is not efficient for consumers to read a large number of reviews. Therefore, it is necessary to offer a summary review to customers. In this study, we suggest providing summary information on sentiment keywords association as well as a network of hotels based on sentiment keywords. Based on a sentiment keyword dictionary, the extracted sentiment keywords associations construct the hotel network through topological data analysis based mapper. This hotel network allows a consumer to find some hotels associated with specific sentiment keywords as well as recommends the same related hotels. This summary information provides users with a summarized emotional assessment of hotels and helps hotel marketing teams understand consumers' perceptions of their hotel.