• Journal of Korea Multimedia Society
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• v.11 no.8
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• pp.1043-1050
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• 2008

XML has become a standard for data representation and exchange on the Internet. With a large number of XML documents on the Web, there is an increasing need to automatically process those structurally rich documents for information retrieval, document management, and data mining applications. In this paper, we propose a new method to measure the similarity between XML documents by considering their structures and contents. The similarity of document's structure is found by a simple string matching technique and that of document's contents is found by weights taking into account of the names and positions of elements. The overall algorithm runs in time that is linear in the combined size of the two documents involved in comparison evaluation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1533-1540
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• 2012

u-Life care device collect body bio formation, and classify and store them in exercise patterns. Afterwards, the devices send the data through bluetooth wireless communication to the smart phones which set Google Android operation system at regular intervals. The information is checked out through application. u-Life care device calculates calories spent a day after monitoring activity quantity with 3-axis acceleration sensor. The device judges the status of health through body data mining and consults tailored exercise treatment. When sending body data, the device sends them in smart phone through Blue Tooth wireless communication at once. So, as a strong point, the device doesn't need mobile gateway or home gateway used for sending to web server information sensed from exercise life care products.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.521-526
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• 2017

Data format of Big data is diverse and vast, and its generation speed is very fast, requiring new management and analysis methods, not traditional data processing methods. Textual mining techniques can be used to extract useful information from unstructured text written in human language in online documents on social networks. Identifying trends in the message of politics, economy, and culture left behind in social media is a factor in understanding what topics they are interested in. In this study, text mining was performed on online news related to a given keyword using topic - oriented analysis technique. We use Latent Dirichiet Allocation (LDA) to extract information from web documents and analyze which subjects are interested in a given keyword, and which topics are related to which core values are related.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.182-183
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• 2018

Big data analysis is the process of discovering a meaningful correlation, pattern, and trends in large data set stored in existing data warehouse management tools and creating new values. In addition, by extracts new value from structured and unstructured data set in big volume means a technology to analyze the results. Most of the methods of Big data analysis technology are data mining, machine learning, natural language processing, pattern recognition, etc. used in existing statistical computer science. Global research institutes have identified Big data as the most notable new technology since 2011.

• Annual Conference on Human and Language Technology
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• 2006.10e
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• pp.273-278
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• 2006

이 논문에서 통계기반의 정렬기법을 이용한 한영/영한 양방향 명사구 기계번역 시스템을 설계하고 구현한다. 정렬기법을 이용한 기계번역 시스템을 구축하기 위해서는 않은 양의 병렬말뭉치(Corpus)가 필요하다. 이 논문에서는 병렬 말뭉치를 구축하기 위해서 웹으로부터 한영 대역쌍을 수집하였으며 수집된 병렬 말뭉치와 단어 정렬 도구인 GIZA++ 그리고 번역기(decoder)인 PARAOH(Koehn, 2004), RAMSES(Patry et al., 2002), MARIE(Crego et at., 2005)를 사용하여 한영/영한 양방향 명사구 번역 시스템을 구현하였다. 약 4만 개의 명사구 병렬 말뭉치를 학습 말뭉치와 평가 말뭉치로 분리하여 구현된 시스템을 평가하였다. 그 결과 한영/영한 모두 약 37% BLEU를 보였으나, 영한 번역의 성공도가 좀더 높았다. 앞으로 좀더 많은 양의 병렬 말뭉치를 구축하여 시스템의 성능을 향상시켜야 할 것이며, 지속적으로 병렬 말뭉치를 구축할 수 있는 텍스트 마이닝 기법이 개발되어야 할 것이다. 무엇보다도 한국어 특성에 적합한 단어 정렬 모델이 연구되어야 할 것이다. 또한 개발된 시스템을 다국어 정보검색 시스템에 직접 적용해서 그 효용성을 평가해보아야 할 것이다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.127-133
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• 2019

Since frequent item extraction so far requires searching for patterns and traversal for the FP-Tree, it is more likely to store the mining data in a tree and thus CPU time is required for its searching. In order to overcome these drawbacks, in this paper, we provide each item with its location identification of transaction data without relying on conditional FP-Tree and convert transaction data into 2-dimensional position information look-up table, resulting in the facilitation of time and spatial accessibility. We propose an algorithm that considers the mapping scheme between the location of items and items that guarantees the linear time complexity. Experimental results show that the proposed method can reduce many execution time and memory usage based on the data set obtained from the FIMI repository website.

• Journal of the Korea Convergence Society
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• v.9 no.4
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• pp.15-22
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• 2018

The purpose of this study is to investigate the consumer perception of packaging using big data analysis. This study use text mining to extract meaningful words from text and semantic network analysis to analyze connectivity and propagation trends. Data were collected by dividing the 'packaging(Korean)' and 'packaging(English)'. This study visualized the word network structure of the two key words and classified them into four groups with similar meaning through CONCOR analysis. The group name was specified based on the words constituting the classified group. These groups are a major category of consumers' perception of packaging. Especially cosmetics and design have high frequency of words and high centrality. Therefore it can be expected that the packaging design is perceived as important in the cosmetics industry. This study predicts consumers' perception of packaging so it can be a basis for future research and industry development.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.427-439
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• 2013

With the fast development of complex science, lots of social networks are studied. We know that the social network is widely applied in analyzing issues in human culture, economics and web sciences. Recently we witness that some researchers began to compare the social network constructed from fiction literatures(literature social network) and the real social network obtained from practice. But we point that previous approaches for literature social network have some drawbacks since they completely depend on the biographical dictionary constructed for a designated literature. So since the previous approach focus on the few important characters and peoples around them, we can not understand the global structure of all characters appeared in the literature at least once. We propose one method to extract all characters appeared in the literature and how to make the social network from that information. Also we newly propose K-critical network by applying frequency of the named characters and the strength of relationship among all textual characters. Our experiment shows that the K-critical measure could be one crucial quantitative measure to compute the relationship strength among characters appeared in the object literature.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.375-380
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• 2022

The purpose of this study is to find out consumers' perception and meaning of convenience store convenience food by using big data. For this study, NNAVER and Daum analyzed news, intellectuals, blogs, cafes, intellectuals(tips), and web documents, and used 'convenience store convenience food' as keywords for data search. The data analysis period was selected as 3 years from January 1, 2019 to December 31, 2021. For data collection and analysis, frequency and matrix data were extracted using TEXTOM, and network analysis and visualization analysis were conducted using the NetDraw function of the UCINET 6 program. As a result, convenience store convenience foods were clustered into health, diversity, convenience, and economy according to consumers' selection attributes. It is expected to be the basis for the development of a new convenience menu that pursues convenience and convenience based on consumers' meaning of convenience store convenience foods such as appropriate prices, discount coupons, and events.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.1-10
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• 2021

This study analyzes the use and meaning of consumer personality implication words in the review text of the Used Transaction Application . From of May 2021, the data were collected for the past six months by our Web crawler in Seoul and Gyeonggi Province, and a total of 1368 cases were collected first by random sampling, and finally 570 cases were preprocessed. The results are as follows. First, 48.2% of review texts were related to the personality of consumers even though it was a commercial platform of products. Second, the review text is mainly positive, which formed a text network structure based on the keyword 'gratitude'. Third, the review text, which implies consumer character, was divided into two groups: 'extrovert personality' and 'introvert personality' of consumers. And the individuality of the two groups worked together on the platform. In conclusion, we would like to suggest that consumer personality plays an important role in the platform transaction process, that consumer personality will play a role in the services of the platform in the future, and that consumer personality should be studied from various perspectives.