• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.69-94
• /
• 2017

Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.

• Journal of Intelligence and Information Systems
• /
• v.21 no.3
• /
• pp.1-17
• /
• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.179-196
• /
• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.

• MISULJARYO - National Museum of Korea Art Journal
• /
• v.98
• /
• pp.226-241
• /
• 2020

Located on the right side of the third floor of the State Hermitage Museum in St. Petersburg, the "Art of Central Asia" exhibition boasts the world's finest collection of artworks and artifacts from the Silk Road. Every item in the collection has been classified by region, and many of them were collected in the early twentieth century through archaeological surveys led by Russia's Pyotr Kozlov, Mikhail Berezovsky, and Sergey Oldenburg. Some of these artifacts have been presented around the world through special exhibitions held in Germany, France, the United Kingdom, the Netherlands, Korea, Japan, and elsewhere. The fruits of Russia's Silk Road expeditions were also on full display in the 2008 exhibition The Caves of One Thousand Buddhas - Russian Expeditions on the Silk Route on the Occasion of 190 Years of the Asiatic Museum, held at the Hermitage Museum. Published in 2018 by the Shanghai Chinese Classics Publishing House in collaboration with the Hermitage Museum, Kuche Art Relics Collected in Russia introduces the Hermitage's collection of artifacts from the Kuche (or Kucha) region. While the book focuses exclusively on artifacts excavated from the Kuche area, it also includes valuable on-site photos and sketches from the Russian expeditions, thus helping to enhance readers' overall understanding of the characteristics of Kuche art within the Buddhist art of Central Asia. The book was compiled by Dr. Kira Samosyuk, senior curator of the Oriental Department of the Hermitage Museum, who also wrote the main article and the artifact descriptions. Dr. Samosyuk is an internationally renowned scholar of Central Asian Buddhist art, with a particular expertise in the art of Khara-Khoto and Xi-yu. In her article "The Art of the Kuche Buddhist Temples," Dr. Samosyuk provides an overview of Russia's Silk Road expeditions, before introducing the historical development of Kuche in the Buddhist era and the aspects of Buddhism transmitted to Kuche. She describes the murals and clay sculptures in the Buddhist grottoes, giving important details on their themes and issues with estimating their dates, and also explains how the temples operated as places of worship. In conclusion, Dr. Samosyuk argues that the Kuche region, while continuously engaging with various peoples in China and the nomadic world, developed its own independent Buddhist culture incorporating elements of Gandara, Hellenistic, Persian, and Chinese art and culture. Finally, she states that the culture of the Kuche region had a profound influence not only on the Tarim Basin, but also on the Buddhist grottoes of Dunhuang and the central region of China. A considerable portion of Dr. Samosyuk's article addresses efforts to estimate the date of the grottoes in the Kuche region. After citing various scholars' views on the dates of the murals, she argues that the Kizil grottoes likely began prior to the fifth century, which is at least 100 years earlier than most current estimates. This conclusion is reached by comparing the iconography of the armor depicted in the murals with related materials excavated from the surrounding area (such as items of Sogdian art). However, efforts to date the Buddhist grottoes of Kuche must take many factors into consideration, such as the geological characteristics of the caves, the themes and styles of the Buddhist paintings, the types of pigments used, and the clothing, hairstyles, and ornamentation of the depicted figures. Moreover, such interdisciplinary data must be studied within the context of Kuche's relations with nearby cultures. Scientific methods such as radiocarbon dating could also be applied for supplementary materials. The preface of Kuche Art Relics Collected in Russia reveals that the catalog is the first volume covering the Hermitage Museum's collection of Kuche art, and that the next volume in the series will cover a large collection of mural fragments that were taken from Berlin during World War II. For many years, the whereabouts of these mural fragments were unknown to both the public and academia, but after restoration, the fragments were recently re-introduced to the public as part of the museum's permanent exhibition. We look forward to the next publication that focuses on these mural fragments, and also to future catalogs introducing the artifacts of Turpan and Khotan. Currently, fragments of the murals from the Kuche grottoes are scattered among various countries, including Russia, Germany, and Korea. With the publication of this catalog, it seems like an opportune time to publish a comprehensive catalog on the murals of the Kuche region, which represent a compelling mixture of East-West culture that reflects the overall characteristics of the region. A catalog that includes both the remaining murals of the Kizil grottoes and the fragments from different parts of the world could greatly enhance our understanding of the murals' original state. Such a book would hopefully include a more detailed and interdisciplinary discussion of the artifacts and murals, including scientific analyses of the pigments and other materials from the perspective of conservation science. With the ongoing rapid development in western China, the grotto murals are facing a serious crisis related to climate change and overcrowding in the oasis city of Xinjiang. To overcome this challenge, the cultural communities of China and other countries that possess advanced technology for conservation and restoration must begin working together to protect and restore the murals of the Silk Road grottoes. Moreover, centers for conservation science should be established to foster human resources and collect information. Compiling the data of Russian expeditions related to the grottoes of Kuche (among the results of Western archaeological surveys of the Silk Road in the early twentieth century), Kuche Art Relics Collected in Russia represents an important contribution to research on Kuche's Buddhist art and the Silk Road, which will only be enhanced by a future volume introducing the mural fragments from Germany. As the new authoritative source for academic research on the artworks and artifacts of the Kuche region, the book also lays the groundwork for new directions for future studies on the Silk Road. Finally, the book is also quite significant for employing a new editing system that improves its academic clarity and convenience. In conclusion, Dr. Kira Samosyuk, who planned the publication, deserves tremendous praise for taking the research of Silk Road art to new heights.