• Journal of the Korean Society for information Management
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• v.32 no.3
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• pp.397-412
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• 2015

The main goal of this study is to investigate how to route a question to some relevant users who have interest in the topic of the question based on users' topic interest. In order to assess users' topic interest, archived question-answer pairs in the community were used to identify latent topics in the chosen categories using LDA. Then, these topic models were used to identify users' topic interest. Furthermore, the topics of newly submitted questions were analyzed using the topic models in order to recommend relevant answerers to the question. This study introduces the process of topic modeling to investigate relevant users based on their topic interest.

• The Journal of the Korea Contents Association
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• v.16 no.6
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• pp.205-214
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• 2016

As a large amount of graph-structured data is widely used in various applications such as social networks, semantic web, and bio-informatics, keyword-based search over graph data has been getting a lot of attention. In this paper, we propose an efficient method for keyword search over graph data to find a set of top-k answers that are relevant as well as non-redundant in structure. We define a non-redundant answer structure for a keyword query and a relevance measure for the answer. We suggest a new indexing scheme on the relevant paths between nodes and keyword terms in the graph, and also propose a query processing algorithm to find top-k non-redundant answers efficiently by exploiting the pre-calculated indexes. We present effectiveness and efficiency of the proposed approach compared to the previous method by conducting an experiment using a real dataset.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.5
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• pp.803-813
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• 2013

As the developments of smart devices and social network services, the amount of data has been exploding. The world is facing Big data era. For these reasons, the Big data processing technology which is a new technology that can handle such data has attracted much attention. One of the most representative technologies is Hadoop. Hadoop Distributed File System(HDFS) designed to run on commercial Linux server is an open source framework and can store many terabytes of data. The initial version of Hadoop did not consider security because it only focused on efficient Big data processing. As the number of users rapidly increases, a lot of sensitive data including personal information were stored on HDFS. So Hadoop announced a new version that introduces Kerberos and token system in 2009. However, this system is vulnerable to the replay attack, impersonation attack and other attacks. In this paper, we analyze these vulnerabilities of HDFS security and propose a new protocol which complements these vulnerabilities and maintains the performance of Hadoop.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1460-1465
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• 2016

Lots of costs threaten the sustainability of the national health-guarantee system. Despite research by the national center for disease control and prevention on health care dynamics with its auditing systems, there are still restrictions of time limitation, sample limitation, and, target diseases limitation. Against this backdrop, using huge volume of total data, many technologies could be fully adopted to the preliminary forecasting and its target-disease expanding of health. With structured data from the national health insurance and unstructured data from the social network service, we attempted to design a model to predict disease. The model can enhance national health and maximize social benefit by providing a health warning service. Also, the model can reduce the advent increase of national health cost and predict timely disease occurrence based on Big Data analysis. We researched related medical prediction cases and performed an experiment with a pilot project so as to verify the proposed model.

• Journal of Digital Contents Society
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• v.16 no.2
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• pp.325-334
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• 2015

In this paper, we had tried to study advertising method of local industry in cooperation with SNS (Social Network Service). In particular, This paper analyzes the Facebook Page trying to find a way to take advantage of the local advertising. To analyse the Facebook page, I had created three Facebook pages such as at Hongik UNIV., Gangwon National UNIV., and specialities in Cuncheon cities. While I was managing the three pages, I had analyzed the results of quantities of activities, and in addition, had used NodeXL for the analysis of the network for the each page. After the analysis of the data of each page, I had found the possibility of the promotion for the successful the local advertizement associated with SNS. To verify and develop the results of my study, I had designed the " real time local advertising APP which is connected with SNS like Facebook, had created server program based on PHP, Android APP. In the future, I have found that there must be a lot of possibilities for the successful and various the local advertising methodologies by using SNS like Facebook, the operational data of the App.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.9
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• pp.1153-1158
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• 2018

Online customer evaluations and social media information among a variety of information sources are critical for businesses as it influences the customer's decision making. There are limitations on the time and money that the survey will ask to identify a variety of customers' needs and complaints. The customer review data at online shopping malls provide the ideal data sources for analyzing customer sentiment about their products. In this study, we collected product reviews data on the smartphone of Samsung and Apple from Amazon. We applied five classification algorithms which are used as representative sentiment analysis techniques in previous studies. The five algorithms are based on support vector machines, bagging, random forest, classification or regression tree and maximum entropy. In this study, we proposed entropy score which can comprehensively evaluate the performance of classification algorithm. As a result of evaluating five algorithms using an entropy score, the SVMs algorithm's entropy score was ranked highest.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.336-339
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• 2017

스마트폰 서드 파티 애플리케이션에 대한 포렌식 분석은 최근 수 년 간 탐구되어야 할 새로운 영역으로 떠올랐다. 현재 스마트폰 시장은 그 규모를 측정하는 것이 무의미할 만큼 커졌으며 각 스마트폰 플랫폼의 앱(App)마켓에는 셀 수 없이 많은 서드 파티 애플리케이션이 존재한다. 모바일 포렌식 소프트웨어 도구들은 일반적으로 연락처, 문자메시지, 통화기록 등의 전형적인 데이터를 수집한다. 이러한 도구들은 서드 파티 애플리케이션이 기기 내부에 저장하는 정보들을 간과하기 쉽다. 여러 제조사 중, 애플사의 모바일 기기에 설치된 많은 서드 파티 애플리케이션은 수사에 도움이 되는 많은 정보와 관련있는 디지털 증거를 남긴다. 이런 잠재적 증거들은 기기 내부에 저장되기도 하며, 비교적 손쉬운 방법으로 법정에 제출 가능하다. 스마트폰으로 이루어지는 많은 활동은 상당 부분 서드 파티 애플리케이션으로 이루어지며, 사이버 범죄 사건의 중심에 스마트폰이 있다면 서드 파티 애플리케이션 분석을 통한 핵심 증거 획득이 사건을 해결할 가능성이 높아진다. 본 논문에서는 스마트폰에서 널리 쓰이고 있는 소셜네트워크 애플리케이션인 '인스타그램(Instagram)'에서 행해진 포렌식 분석에 초점을 맞추고, 기기는 전 세계 적으로 가장 사용자 점유율이 높은 스마트폰인 아이폰에서 이루어졌다.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.149-155
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• 2018

In this paper we propose an algorithm for generating role-performer bipartite matrix for analyzing BPM-based human resource affiliations. Firstly, the proposed algorithm conducts the extraction of role-performer affiliation relationships from ICN(Infromation Contorl Net) based business process models. Then, the role-performer bipartite matrix is constructed in the final step of the algorithm. Conclusively, the bipartite matrix generated through the proposed algorithm ought to be used as the fundamental data structure for discovering the role-performer affiliation networking knowledge, and by using a variety of social network analysis techniques it enables us to acquire valuable analysis results about BPM-based human resource affiliations.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.67-101
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• 2014

The use of new technology greatly shapes the marketing strategies used by companies to engage their consumers. Among these new technologies, social media is used to reach out to the organization's audience online. One of the most popular social media channels to date is the microblogging platform Twitter. With 500 million tweets sent on average daily, the microblogging platform is definitely a rich source of data for researchers, and a lucrative marketing medium for companies. Nonetheless, one of the challenges for companies in developing an effective Twitter campaign is the limited theoretical and empirical evidence on the proper organizational usage of Twitter despite its potential advantages for a firm's external communications. The current study aims to provide empirical evidence on how firms can utilize Twitter effectively in their marketing communications using the association between brand personality and brand engagement that several branding researchers propose. The study extends Aaker's previous empirical work on brand personality by applying the Brand Personality Scale to explore whether Twitter brand communities convey distinctive brand personalities online and its influence on the communities' level or intensity of consumer engagement and sentiment quality. Moreover, the moderating effect of the product involvement construct in consumer engagement is also measured. By collecting data for a period of eight weeks using the publicly available Twitter application programming interface (API) from 23 accounts of Twitter-verified business-to-consumer (B2C) brands, we analyze the validity of the paper's hypothesis by using computerized content analysis and opinion mining. The study is the first to compare Twitter marketing across organizations using the brand personality concept. It demonstrates a potential basis for Twitter strategies and discusses the benefits of these strategies, thus providing a framework of analysis for Twitter practice and strategic direction for companies developing their use of Twitter to communicate with their followers on this social media platform. This study has four specific research objectives. The first objective is to examine the applicability of brand personality dimensions used in marketing research to online brand communities on Twitter. The second is to establish a connection between the congruence of offline and online brand personalities in building a successful social media brand community. Third, we test the moderating effect of product involvement in the effect of brand personality on brand community engagement. Lastly, we investigate the sentiment quality of consumer messages to the firms that succeed in communicating their brands' personalities on Twitter.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.137-147
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• 2022

Diagnosis and management of skin condition is a very basic and important function in performing its role for workers in the beauty industry and cosmetics industry. For accurate skin condition diagnosis and management, it is necessary to understand the skin condition and needs of customers. In this paper, we developed SCIS, a big data-based skin care information system that supports skin condition diagnosis and management using social media big data for skin condition diagnosis and management. By using the developed system, it is possible to analyze and extract core information for skin condition diagnosis and management based on text information. The skin care information system SCIS developed in this paper consists of big data collection stage, text preprocessing stage, image preprocessing stage, and text word analysis stage. SCIS collected big data necessary for skin diagnosis and management, and extracted key words and topics from text information through simple frequency analysis, relative frequency analysis, co-occurrence analysis, and correlation analysis of key words. In addition, by analyzing the extracted key words and information and performing various visualization processes such as scatter plot, NetworkX, t-SNE, and clustering, it can be used efficiently in diagnosing and managing skin conditions.