• Journal of Internet Computing and Services
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• v.20 no.3
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• pp.43-59
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• 2019

Today, with the spread of smartphones and the development of social networking services, structured and unstructured big data have stored exponentially. If we analyze them well, we will get useful information to be able to predict data for the future. Large amounts of data need to be collected first in order to analyze big data. The web is repository where these data are most stored. However, because the data size is large, there are also many data that have information that is not needed as much as there are data that have useful information. This has made it important to collect data efficiently, where data with unnecessary information is filtered and only collected data with useful information. Web crawlers cannot download all pages due to some constraints such as network bandwidth, operational time, and data storage. This is why we should avoid visiting many pages that are not relevant to what we want and download only important pages as soon as possible. This paper seeks to help resolve the above issues. First, We introduce basic web-crawling algorithms. For each algorithm, the time-complexity and pros and cons are described, and compared and analyzed. Next, we introduce the state-of-the-art web crawling algorithms that have improved the shortcomings of the basic web crawling algorithms. In addition, recent research trends show that the web crawling algorithms with special purposes such as collecting sentiment words are actively studied. We will one of the introduce Sentiment-aware web crawling techniques that is a proactive web crawling technique as a study of web crawling algorithms with special purpose. The result showed that the larger the data are, the higher the performance is and the more space is saved.

• Knowledge Management Research
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• v.22 no.2
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• pp.145-165
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• 2021

As of late December 2019, the spread of COVID-19 pandemic began which put the entire world in panic. In order to overcome the crisis and minimize any subsequent damage, the government as well as its affiliated institutions must maximize effects of pre-existing policy support and introduce a holistic response plan that can reflect this changing situation- which is why it is crucial to analyze social topics and people's interests. This study investigates people's major thoughts, attitudes and topics surrounding COVID-19 pandemic through the use of social media and big data. In order to collect public opinion, this study segmented time period according to government countermeasures. All data were collected through NAVER blog from 31 December 2019 to 12 December 2020. This research applied TF-IDF keyword extraction and LDA topic modeling as text-mining techniques. As a result, eight major issues related to COVID-19 have been derived, and based on these keywords, this research presented policy strategies. The significance of this study is that it provides a baseline data for Korean government authorities in providing appropriate countermeasures that can satisfy needs of people in the midst of COVID-19 pandemic.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.52-60
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• 2021

The aim of this study was to derive a new trend by analyzing installations using 3D printing that are out of the limits of size and design according to the trends of developing 3D printing technology. This paper classified the types of installations using 3D printing and analyzed them with two trends: the trend of design and the trend of output. The trends of installations using 3D printing derived from this study are as follows. First, as the implementation of design through an algorithm is accomplished, the transformation appears with the atypical design that is prominent in complex expression. Second, Robotics and FDM 3D Printing is fused, which is changing the existing paradigm. Therefore, the production and utilization of installations using 3D printing proceeded at a faster pace through the interaction between the algorithm design method and freeform 3D printing technology. This study was conducted on installations using 3D printing around the world and played a basic role in the research on the production of installations using 3D printing along with domestic 3D printing technology to be developed in the future. Follow-up studies in various aspects, such as materials and combination methods, will be needed.

• Korean Chemical Engineering Research
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• v.60 no.1
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• pp.159-168
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• 2022

Catalytic activity in ammonia decomposition reaction was studied on Mo-Al nitride obtained through temperature programmed nitridation of calcined Mo-Al mixed oxide prepared by varying the MoO₃ quantity in the range of 10-50 wt%. N₂ sorption analysis, X-ray diffraction analysis (XRD), X-ray photoelectron spectroscopy (XPS) and H₂-temperature programmed reduction (H₂-TPR), and transmission electron microscopy (TEM) to investigate the physicochemical properties of the prepared catalyst were performed. After calcination at 600 ℃, the XRD of Mo-Al oxide showed γ-Al₂O₃ and Al₂(MoO₄)₃ phases, and the nitride after nitridation showed an amorphous form. The specific surface area after nitridation by topotactic transformation of MoO₃ to nitride was increased due to the formation of Mo nitride, and the Mo nitride was observed to be supported on γ-Al₂O₃. As for the catalytic activity in the ammonia decomposition reaction, 40 wt% MoO₃ showed the best activity, and as the nitridation time increases, the activity increased, and thus the activation energy decreased.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.47-51
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• 2021

In this study, we try to find out and analyze the results of reduced self-esteem and loss using text mining. Physical health is important, of course, but these days, mental health is considered more important. In order for the mind to be healthy, it is important to have self-esteem and self-confidence first. Self-esteem decreases, and if lost, it directly leads to depression. If depression is severe, the worst will lead to self-harm and suicide. However, more and more people are committing suicide these days because both ordinary people and entertainers cannot overcome depression. For this reason, the seriousness of depression and loss of self-esteem are also considered important and become an issue. Therefore, we want to collect data for a certain period of time through Naver, Instagram, and Twitter searches and extract the words of the data to anticipate and analyze the cause of loss of self-esteem, how serious the recent depression is, and what the consequences of loss of self-esteem are.

• Journal of the Korean earth science society
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• v.44 no.1
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• pp.79-89
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• 2023

The purpose of this study was to investigate the effects of engineering design-based ocean cleanup classes on STEAM attitudes and creative engineering problem-solving dispositions. Furthermore, during this process, we tried to determine interesting points that students encountered in engineering design-based classes. For this study, a science class with six lessons based on engineering design was developed and reviewed by a professor who majored in engineering design, along with five engineering design experts with a master's degree or higher. The subject of the class was selected as the design and implementation of scientific and engineering measures to reduce marine pollution based on the method implemented in an actual Ocean Clean-up Project. The engineering design process utilized the engineering design model presented by NGSS (2013), and was configured to experience redesign through the optimization process. To verify effectiveness, the STEAM attitude questionnaire developed by Park et al. (2019) and the creative engineering problemsolving propensity test tool developed by Kang and Nam (2016) were used. A pre and post t-test was used for statistical analysis for the effectiveness test. In addition, the contents of interesting points experienced by the learners were transcribed after receiving descriptive responses, and were analyzed and visualized through degree centrality analysis. Results confirmed that engineering design in science classes had a positive effect on both STEAM attitude and creative engineering problem-solving disposition (p< .05). In addition, as a result of unstructured data analysis, science and engineering knowledge, engineering experience, and cooperation and collaboration appeared as factors in which learners were interested in learning, confirming that engineering experience was the main factor.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.91-110
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• 2023

The importance of manuals is emphasized because chemical accidents require swift response and recovery, and often result in environmental pollution and casualties. In this regard, the OECD revised OECD Guidelines for the Prevention, Preparedness, and Response to Chemical Accidents (referred to as the OECD Guidelines), in June 2023. Moreover, while existing research primarily raises awareness about chemical accidents, highlighting the need for a system-wide response including laws, regulations, and manuals, it was difficult to find comparative research on the attributes of manuals. So, this paper aims to compare and analyze the second and third editions of the OECD Guidelines, in order to uncover the information attributes and implications of the revised version. Specifically, TF-IDF (Term Frequency-Inverse Document Frequency) was applied to understand which keywords have become more important, and Word2Vec was applied to identify keywords that were used similarly and those that were differentiated. Lastly, a 2×2 matrix was proposed, identifying the topics within each quadrant to provide a deeper comparison of the information attributes of the OECD Guidelines. This study offers a framework to help researchers understand information attributes. From a practical perspective, it appears valuable for the revision of standard manuals by domestic government agencies and corporations related to chemistry.

• The Journal of the Korea Contents Association
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• v.15 no.5
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• pp.570-581
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• 2015

In recent years, many institutions predict that cloud services and big data will be popular IT trends in the near future. A number of leading IT vendors are focusing on practical solutions and services for cloud and big data. In addition, cloud has the advantage of unrestricted in selecting resources for business model based on a variety of internet-based technologies which is the reason that provisioning and virtualization technologies for active resource expansion has been attracting attention as a leading technology above all the other technologies. Big data took data prediction model to another level by providing the base for the analysis of unstructured data that could not have been analyzed in the past. Since what cloud services and big data have in common is the services and analysis based on mass amount of data, efficient operation and designing of mass data has become a critical issue from the early stage of development. Thus, in this paper, I would like to establish data processing architecture based on technological requirements of mass data for cloud and big data services. Particularly, I would like to introduce requirements that must be met in order for distributed file system to engage in cloud computing, and efficient compression technology requirements of mass data for big data and cloud computing in terms of cost-saving, as well as technological requirements of open-source-based system such as Hadoop eco system distributed file system and memory database that are available in cloud computing.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.6
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• pp.888-895
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• 2015

For effective utilization of butter clam shell as a natural calcium resource, the optimal conditions for preparation of calcium acetate (BCCA) with high solubility were determined using response surface methodology (RSM). The polynomial models developed by RSM for pH, solubility, and yield were highly effective in describing the relationships between factors (P<0.05). Increased molar ratio of calcined powder (BCCP) from butter clam shell led to reduction of solubility, yield, color values, and overall quality. Critical values of multiple response optimization to independent variables were 2.70 M and 1.05 M for acetic acid and BCCP, respectively. The actual values (pH 7.04, 93.0% for solubility and 267.5% for yield) under optimization conditions were similar to predicted values. White indices of BCCAs were in the range of 89.7~93.3. Therefore, color value was improved by calcination and organic acid treatment. Buffering capacity of BCCAs was strong at pH 4.88 to 4.92 upon addition of ~2 mL of 1 N HCl. Calcium content and solubility of BCCAs were 20.7~22.8 g/100 g and 97.2~99.6%, respectively. The patterns of fourier transform infrared spectrometer and X-ray diffractometer analyses from BCCA were identified as calcium acetate monohydrate, and microstructure by field emission scanning electron microscope showed an irregular form.

• The Journal of the Korea Contents Association
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• v.19 no.6
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• pp.10-23
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• 2019

Although social big data can provide a multi-faceted perspective on school bullying experiences among children and adolescents, the complexity and variety of unstructured text presents a challenge for systematic collection and analysis of the data. Development of an ontology, which identifies key terms and their intricate relationships, is crucial for extracting key concepts and effectively collecting data. The current study elaborated on the definition of an ontology, carefully described the 7 stage development process, and applied the ontology for collecting and analyzing school bullying social big data. As a result, approximately 2,400 key terms were extracted in top-, middle-, and lower-level categories, concerning domains of participants, causes, types, location, region, and intervention. The study contributes to the literature by explaining the ontology development process and proposing a novel alternative research model that uses social big data in school bullying research. Findings from this ontology study may provide a basis for social big data research. Practical implications of this study lie in not only helping to understand the experience of school bullying participants, but also in offering a macro perspective on school bullying as a social phenomenon.