• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.6
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• pp.43-51
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• 2010

It is meaningful to investigate data in unallocated space because we can investigate the deleted data. Consecutively complete file recovery using the File Carving is possible in unallocated area, but noncontiguous or incomplete data recovery is impossible. Typically, the analysis of the data fragments are needed because they should contain large amounts of information. Microsoft Word, Excel, PowerPoint and PDF document file's text are stored using compression or specific document format. If the part of aforementioned document file was stored in unallocated data fragment, text extraction is possible using specific document format. In this paper, we suggest the method of extracting a particular document file text in unallocated data fragment.

• Fashion & Textile Research Journal
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• v.23 no.3
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• pp.380-389
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• 2021

This study aimed to reveal the perceptions and trends of digital fashion technology through an informational approach. A big data analysis was conducted after collecting the text shown in a web environment from April 2019 to April 2021. Key words were derived through text mining analysis and network analysis, and the structure of perception of digital fashion technology was identified. Using textoms, we collected 8144 texts after data refinement, conducted a frequency of emergence and central component analysis, and visualized the results with word cloud and N-gram. The frequency of appearance also generated matrices with the top 70 words, and a structural equivalent analysis was performed. The results were presented with network visualizations and dendrograms. Fashion, digital, and technology were the most frequently mentioned topics, and the frequencies of platform, digital transformation, and start-ups were also high. Through clustering, four clusters of marketing were formed using fashion, digital technology, startups, and augmented reality/virtual reality technology. Future research on startups and smart factories with technologies based on stable platforms is needed. The results of this study contribute to increasing the fashion industry's knowledge on digital fashion technology and can be used as a foundational study for the development of research on related topics.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.371-376
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• 2012

In this paper, we propose a new data coding method and its circuits for minimizing data transition in text image. The proposed circuits can solve the synchronization problem between input data and output data in the modified LVDS algorithm. And the proposed algorithm is allowed to transmit two data signals through additional serial data coding method in order to minimize the data transition in text image and can reduce the operating frequency to a half. Thus, we can solve EMI(Electro-Magnetic Interface) problem and reduce the power consumption. The simulation results show that the proposed algorithm and circuits can provide an enhanced data transition minimization in text image and solve the synchronization problem between input data and output data.

• Smart Media Journal
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• v.11 no.10
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• pp.54-64
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• 2022

A big data-driven digital transformation is defined as a process that aims to innovate companies by triggering significant changes to their capabilities and designs through the use of big data and various technologies. For a successful big data-driven digital transformation, reviewing related literature, which enhances the understanding of research statuses and the identification of key research topics and relationships among key topics, is necessary. However, understanding and describing literature is challenging, considering its volume and variety. Establishing a common ground for central concepts is essential for science. To clarify key research topics on the big data-driven digital transformation, we carry out a comprehensive literature review by performing text mining of 439 articles. Text mining is applied to learn and identify specific topics, and the suggested key references are manually reviewed to develop a state-of-the-art overview. A total of 10 key research topics and relationships among the topics are identified. This study contributes to clarifying a systematized view of dispersed studies on big data-driven digital transformation across multiple disciplines and encourages further academic discussions and industrial transformation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.367-369
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• 2014

Digital data are mostly comprised of unstructured data such as text file, office file, image file, video file, and drawing file. The recent digital data being generated and used within enterprise are sharply increasing in quantity. Those digital data are becoming significant as digital assets, but the value of digital assets is not properly evaluated. Accordingly, this study will present a model to evaluate the value of unstructured data as digital assets within enterprise and will also present a differentiated management plan for unstructured data as assets.

• Journal of Distribution Science
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• v.22 no.3
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• pp.113-124
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• 2024

Purpose: This study is a quantitative study and analyzes the effect of evaluating the extreme and usefulness of product reviews on sales performance by using text mining techniques based on product review big data. We investigate whether the perceived helpfulness of product reviews serves as a mediating factor in the impact of product review extremity on sales performance. Research design, data and methodology: The analysis emphasizes customer interaction factors associated with both product review helpfulness and sales performance. Out of the 8.26 million Amazon product reviews in the book category collected by He & McAuley (2016), text mining using natural language processing methodology was performed on 300,000 product reviews, and the hypothesis was verified through hierarchical regression analysis. Results: The extremity of product reviews exhibited a negative impact on the evaluation of helpfulness. And the helpfulness played a mediating role between the extremity of product reviews and sales performance. Conclusion: Increased inclusion of extreme content in the product review's text correlates with a diminished evaluation of helpfulness. The evaluation of helpfulness exerts a negative mediating effect on sales performance. This study offers empirical insights for digital market distributors and sellers, contributing to the research field related to product reviews based on review ratings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.269-281
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• 2014

Compared to hiding information into digital image, hiding information into digital text file requires less storage space and smaller bandwidth for data transmission, and it has obvious universality and extensiveness. However, text files have low redundancy, so it is more difficult to hide information in text files. To overcome this difficulty, Wang et al. proposed a reversible information hiding scheme using left-right and up-down representations of Chinese characters, but, when the scheme is implemented, it does not provide good visual steganographic effectiveness, and the embedding and extracting processes are too complicated to be done with reasonable effort and cost. We observed that a lot of traditional and simplified Chinese characters look somewhat the same (also called lookalike), so we utilize this feature to propose a novel information hiding scheme for hiding secret data in lookalike Chinese characters. Comparing to Wang et al.'s scheme, the proposed scheme simplifies the embedding and extracting procedures significantly and improves the effectiveness of visual steganographic images. The experimental results demonstrated the advantages of our proposed scheme.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.9-13
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• 2021

With the development of communication technology, various forms of digital crime are increasing, and the need for digital forensics is increasing. Moreover, if a textual document containing sensitive data is deliberately deleted or modified by a particular person, it could be important data to prove its connection to a particular person and crime through a system that checks for data modification detection. This paper proposes a data modification detection system that can analyze the hash data, file size, file creation date, file modification date, file access date, etc. of SHA-256, one of the encryption techniques, focusing on text files, to compare whether the target text file is modified or not.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.33-43
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• 2024

Automated text summarization (ATS) systems rely on language resources as datasets. However, creating these datasets is a complex and labor-intensive task requiring linguists to extensively annotate the data. Consequently, certain public datasets for ATS, particularly in languages such as Thai, are not as readily available as those for the more popular languages. The primary objective of the ATS approach is to condense large volumes of text into shorter summaries, thereby reducing the time required to extract information from extensive textual data. Owing to the challenges involved in preparing language resources, publicly accessible datasets for Thai ATS are relatively scarce compared to those for widely used languages. The goal is to produce concise summaries and accelerate the information extraction process using vast amounts of textual input. This study introduced ThEconSum, an ATS architecture specifically designed for Thai language, using economy-related data. An evaluation of this research revealed the significant remaining tasks and limitations of the Thai language.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.189-191
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• 2022

In modern society, messenger services are necessary to communication with others, and criminals are no exception. In representative cases of Burning Sun Gate(2018) and NthRoom(2019), messenger data analysis was used as a smoking gun to solve these criminal cases. Therefore messenger text analytics is critical for the resolution of crimes in a modern environment. also, it takes a lot of time to analyze messenger data in the digital forensic investigation process, so researchers in text mining need to be more effective to respond with the current situation In this paper, we study various natural language preprocessing(NLP) methods according to the characteristics of instant messages to effectively proceed with NLP analysis on instant messengers.