Namgyu Kim

Watermarking two dimensional data object identifier for authenticated distribution of digital multimedia contents

A digital object identifier refers to diverse technologies associated with assigning an identifier to a digital resource and managing the identification system. One type of implementation of a digital object identifier developed by the Korean Government is termed the Universal Content Identifier (UCI) system. It circulates and utilizes identifiable resources efficiently by connecting various online and offline identifying schemes. UCI tags can contain not only identifiers but also abundant additional information regarding contents. So, researchers and practitioners have shown great interest in methods that utilize the two-dimensional barcode (2D barcode) to attach UCI tags to digital contents. However, attaching a 2D barcode directly to a digital content raises two problems. First, quality of the content may deteriorate due to the insertion of the barcode; second, a malicious user can invalidate the identifying tag, simply by removing the tag from the original content. We believe that these concerns can be mitigated by inserting an invisible digital tag containing information about an identifier and digital copyrights into the entire area of the digital content. In this study, to protect copyrights of digital contents securely without quality degradation, we attempt to discover a sequence of process for generating a 2D barcode from a UCI tag and watermarking the barcode into a digital content. Such a UCI system can be widely applied to areas such as e-learning, distribution tracking, transaction certification, and reference linking services when the system is equipped with 2D barcode technology and secure watermarking algorithms. The latter part of this paper analyzes intensive experiments conducted to evaluate the robustness of traditional digital watermarking algorithms against external attacks.

A Multi-Dimensional Issue Clustering from the Perspective Consumers` Interests and R&D

The volume of unstructured text data generated by various social media has been increasing rapidly; therefore, use of text mining to support decision making has also been increasing. Especially, issue Clustering-determining a new relation with various issues through clustering-has gained attention from many researchers. However, traditional issue clustering methods can only be performed based on the co-occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be discovered using traditional issue clustering methods, even if those issues are strongly related in other perspectives. Therefore, issue clustering that fits each of criteria needs to be performed by the perspective of analysis and the purpose of use. In this study, a multi-dimensional issue clustering is proposed to overcome the limitation of traditional issue clustering. We assert, specifically in this study, that issue clustering should be performed for a particular purpose. We analyze the results of applying our methodology to two specific perspectives on issue clustering, (i) consumers` interests, and (ii) related R&D terms.

R&D Perspective Social Issue Packaging using Text Analysis

In recent years, text mining has been used to extract meaningful insights from the large volume of unstructured text data sets of various domains. As...

Constructing an Issue Network from the Perspective of Common R&D Keywords

The demand for extracting keywords related to national issues from various sources and using them to retrieve R&D information has increased rapidly recently. In order to satisfy this demand, three methodologies are proposed in this study: a hybrid methodology for extracting and integrating national issue keywords, a methodology for packaging R&D information that corresponds to national issues, and a methodology for generating an associative issue network related to relevant R&D information. Data analysis techniques, such as text mining, social network analysis, and association rules mining, are utilized to establish these methodologies.

Utilizing the Effect of Market Basket Size for Improving the Practicality of Association Rule Measures

ABSTRACT Association rule mining techniques enable us to acquire knowledge concerning sales patterns among individual items from voluminous transactional data. Certainly, one of the major purposes of association rule mining is utilizing the acquired knowledge to provide marketing strategies such as catalogue design, cross-selling and shop allocation. However, this requires too much time and high cost to only extract the actionable and profitable knowledge from tremendous numbers of discovered patterns. In currently available literature, a number of interest measures have been devised to accelerate and systematize the process of pattern evaluation. Unfortunately, most of such measures, including support and confidence, are prone to yielding impractical results because they are calculated only from the sales frequencies of items. For instance, traditional measures cannot differentiate between the purchases in a small basket and those in a large shopping cart. Therefore, some adjustment should be made to the size of market baskets because there is a strong possibility that mutually irrelevant items could appear together in a large shopping cart. Contrary to the previous approaches, we attempted to consider market basket’s size in calculating interest measures. Because the devised measure assigns different weights to individual purchases according to their basket sizes, we expect that the measure can minimize distortion of results caused by accidental patterns. Additionally, we performed intensive computer simulations under various environments, and we performed real case analyses to analyze the correctness and consistency of the devised measure.Keywords:Association Rule Mining, Data Mining, Market Basket Analysis, Interest Measures