• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.1-7
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• 2019

The objective of this study is to examine the trends in big data. Research abstracts were extracted from 4,019 articles, published between 1995 and 2018, on Web of Science and were analyzed using topic modeling and time series analysis. The 20 single-term topics that appeared most frequently were as follows: model, technology, algorithm, problem, performance, network, framework, analytics, management, process, value, user, knowledge, dataset, resource, service, cloud, storage, business, and health. The 20 multi-term topics were as follows: sense technology architecture (T10), decision system (T18), classification algorithm (T03), data analytics (T17), system performance (T09), data science (T06), distribution method (T20), service dataset (T19), network communication (T05), customer & business (T16), cloud computing (T02), health care (T14), smart city (T11), patient & disease (T04), privacy & security (T08), research design (T01), social media (T12), student & education (T13), energy consumption (T07), supply chain management (T15). The time series data indicated that the 40 single-term topics and multi-term topics were hot topics. This study provides suggestions for future research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5905-5926
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• 2017

Sentiments can profoundly affect individual behavior as well as decision-making. Confronted with the ever-increasing amount of review information available online, it is desirable to provide an effective sentiment model to both detect and organize the available information to improve understanding, and to present the information in a more constructive way for consumers. This study developed a unified phrase-based topic and sentiment detection model, combined with a tracking model using incremental hierarchical dirichlet allocation (PTSM_IHDP). This model was proposed to discover the evolutionary trend of topic-based sentiments from online reviews. PTSM_IHDP model firstly assumed that each review document has been composed by a series of independent phrases, which can be represented as both topic information and sentiment information. PTSM_IHDP model secondly depended on an improved time-dependency non-parametric Bayesian model, integrating incremental hierarchical dirichlet allocation, to estimate the optimal number of topics by incrementally building an up-to-date model. To evaluate the effectiveness of our model, we tested our model on a collected dataset, and compared the result with the predictions of traditional models. The results demonstrate the effectiveness and advantages of our model compared to several state-of-the-art methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.4
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• pp.613-620
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• 2017

The power grid has been changed to a smart grid system to satisfy the growing need for power grid complexity, demand, reliability, security, and efficiency with a combination of existing power and ICT technology. This study analyzes the research trends in smart grid technology in the period since the introduction of the smart grid system and compares it with industrial trends to grasp the progress and characteristics of Smart Grid technology and look for ways to innovate the technology. To do this, we analyze the research trends using dynamic topic modeling, which is capable of time-series research topic analysis. Next, we compare the results of research trends with industrial trends analyzed by Gartner's experts to demonstrate that smart grid research is evolving to the level of industrialization. The results of this study are quantitative analysis through data mining, and it is expected that it will be used in many fields such as companies that want to participate in industry and government agencies that need to establish policies by showing more objective analysis results.

• Proceedings of the CALSEC Conference
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• 2003.09a
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• pp.54-59
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• 2003

The information sharing is essential to make collaboration by participants in the collaboration environment. The sharing of the information is necessary to reduce time-to-market of new Product. In this paper, V2-model is proposed far supporting the sharing of the information on product development. V2-model supports collaborative product development in design and supply chain. Through viewpoints, V2-model supports 1) two-level structure that consist of private level and public level ,2) level-up process and 3) product development process. The public level information supports to share the product information on collaborative supply chain and design. The viewpoints in V2-model are divided into public viewpoints that point to the public level information and private viewpoints that point to the private level information. Private viewpoints are transformed into public viewpoints. The extended Topic Map has B-Topic, S-Topic and View for representing V2-model in this paper. The level-up process of V2-model is implemented through the merging of S-Topics. V2-model is implemented with washing machine model using extended Topic Maps. In this model, the public viewpoints and private viewpoints are represented and the level-up process, which transforms private viewpoints into public viewpoints, is implemented.

• The Journal of Economics, Marketing and Management
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• v.10 no.6
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• pp.1-7
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• 2022

Purpose: The purpose of this study is identifying the research trends of degree papers related to the beauty industry and providing information which can contribute to the development of the domestic beauty industry and the direction of various research about beauty industry. Research design, data and methodology: This study used 154 academic papers and 189 academic papers with English abstracts out of 299 academic papers. All of these papers were found by searching for the keyword "beauty industry" in ScienceON on August 15, 2022. For the analysis, BERTopic and LDA (Latent Dirichlet Allocation) analysis were conducted using Python 3.7. Also, OLS regression analysis was conducted to understand the annual increase and decrease trend of each topic derived with trend analysis. Results: As a result of word frequency analysis, the frequency of satisfaction, management, behavior, and service was found to be high. In addition, it was found that 'service', 'satisfaction' and 'customer' were frequently associated with program and relationship in the word co-occurrence frequency analysis. As a result of topic modeling, six topics were derived: 'Beauty shop', 'Health education', 'Cosmetics', 'Customer satisfaction', 'Beauty education', and 'Beauty business'. The trend analysis result of each topic confirmed that 'Beauty education' and 'Health education' are getting more attention as time goes by. Conclusions: The future studies must resolve the extreme polarization between the structure of the small beauty industry and beauty stores. Furthermore, the researches have to direct various ways to create the performance of internal personnel. The ways to maximize product capabilities such as competitive cosmetics and brands are also needed attentions.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.55-66
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• 2023

Community portraits can deeply explore the characteristics of community structures and describe the personalized knowledge needs of community users, which is of great practical significance for improving community recommendation services, as well as the accuracy of resource push. The current community portraits generally have the problems of weak perception of interest characteristics and low degree of integration of topic information. To resolve this problem, the reader community portrait method based on the thematic and timeliness characteristics of interest labels (UIT) is proposed. First, community opinion leaders are identified based on multi-feature calculations, and then the topic features of their texts are identified based on the LDA topic model. On this basis, a semantic mapping including "reader community-opinion leader-text content" was established. Second, the readers' interest similarity of the labels was dynamically updated, and two kinds of tag parameters were integrated, namely, the intensity of interest labels and the stability of interest labels. Finally, the similarity distance between the opinion leader and the topic of interest was calculated to obtain the dynamic interest set of the opinion leaders. Experimental analysis was conducted on real data from the Douban reading community. The experimental results show that the UIT has the highest average F value (0.551) compared to the state-of-the-art approaches, which indicates that the UIT has better performance in the smooth time dimension.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.5
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• pp.46-58
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• 2021

Parks became essential to people after the introduction of modern parks in Korea. Following mayoral elections by popular vote, issues surrounding parks, such as the creation of parks, have arisen and have been publicized by the media, allowing for the formation of discourse. Accordingly, this study conducted a topic analysis by collecting news articles from major media outlets in Korea that addressed issues related to parks since 1995, after the introduction of mayoral elections by popular vote, and analyzed changes over time in the discourse on parks through semantic network analysis. As a result of a Latent Dirichlet allocation topic modeling analysis, the following five topics were classified: urban park expansion (Topic 1), historical and cultural parks (Topic 2), use programs (Topic 3), zoo event (Topic 4), and conflicts in the park creation process (Topic 5). The park-related discourse addressed by the media is as follows. First, the creation process and conflicts regarding the quantitative expansion of parks are treated as the central discourse. Second, the names of parks appear as keywords every time a new park is created, and they are mentioned continuously from then on, thereby playing an important role in the formation of discourse. Third, 'residents' form discourse about the public nature of the park as the principal agent in park-related media. This study has significance in that it examines how parks are interpreted and how discourse is formed and changed by the media. It is expected that discourse on parks will be addressed from various perspectives in further research focusing on other media, such as regional and specialized magazines.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.521-526
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• 2016

As on-line informal text data have massive in its volume and have unstructured characteristics in nature, there are limitations in applying traditional relational data model technologies for data storage and data analysis jobs. Moreover, using dynamically generating massive social data, social user's real-time reaction analysis tasks is hard to accomplish. In the paper, to capture easily the semantics of massive and informal on-line documents with unsupervised learning mechanism, we design and implement automatic topic extraction systems according to the mass of the words that consists a document. The input data set to the proposed system are generated first, using N-gram algorithm to build multiple words to capture the meaning of the sentences precisely, and Hadoop and Spark (In-memory distributed computing framework) are adopted to run topic model. In the experiment phases, TB level input data are processed for data preprocessing and proposed topic extraction steps are applied. We conclude that the proposed system shows good performance in extracting meaningful topics in time as the intermediate results come from main memories directly instead of an HDD reading.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1258-1270
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• 2017

'Semantic gap' has been a year-old problem in automatic video summarization, which refers to the gap between semantics implied in video summarization algorithms and what people actually infer from watching videos. Using the external EEG bio-feedback obtained from video watchers as a solution of this semantic gap problem has several another issues: First, how to define and measure noises against ERP waveforms as signals. Second, whether individual differences among subjects in terms of noise and SNR for conventional ERP studies using still images captured from videos are the same with those differently conceptualized and measured from videos. Third, whether individual differences of subjects by noise and SNR levels help to detect topic-irrelevant shots as signals which are not matched with subject's own semantic topical expectations (mis-match negativity at around 400m after stimulus on-sets). The result of repeated measures ANOVA test clearly shows a 2-way interaction effect between topic-relevance and noise level, implying that subjects of low noise level for video watching session are sensitive to topic-irrelevant visual shots, while showing another 3-way interaction among topic-relevance, noise and SNR levels, implying that subjects of high noise level are sensitive to topic-irrelevant visual shots only if they are of low SNR level.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.57-64
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• 2016

In this paper, we propose the design of railway safety common data model to provide common transformation method for collecting data from railway facility fields to Real-time railway safety monitoring and control system. This common data model is divided into five abstract sub-models according to the characteristics of data such as 'StateInfoMessage', 'ControlMessage', 'RequestMessage', 'ResponseMessage' and 'ExtendedXXXMessage'. This kind of model structure allows diverse heterogeneous data acquisitions and its common conversion method to DDS (Data Distribution Service) format to share data to the sub-systems of Real-time railway safety monitoring and control system. This paper contains the design of common data model and its DDS Topic expression for DDS communication, and presents two kinds of data transformation case studied for verification of the model design.