Earticle

인터넷 시대의 도래와 COVID-19의 발생으로 많은 기업들이 온라인 거래를 진행하고 있다. 하지만 사이버경제 가 작동하는 방식 때문에 거래금액과 고객번호 등을 허위로 조작해 재무부정을 벌이는 기업이 점차 늘고 있다. 본 연구 의 목적은 인터넷 시대 기업의 재무부정 행위를 분석하고 해결책을 제시하는 것이다. 이에 본 연구는 중국 루이씽커피 의 재무부정 행태를 예시로 분석하고 재무부정의 원인과 대응방안을 연구했다. 결과적으로 재무부정의 원인은 온라인 거래로 인한 현금흐름의 불투명성에 있다는 것을 발견했다. 본 연구는 기업 내부통제 시스템의 개선, 리스크 관리 시스 템 개발, 종합적인 외부감독 시스템 구축을 제안했다.

With the advent of the internet age and the outbreak of COVID-19, many companies have embraced online trade. However, due to the way the cyber economy works, the number of companies engaged in financial fraud by falsifying their transaction amounts and customer numbers has been gradually increasing. The purpose of this study is to analyze financial fraud of companies in the Internet era and to present solutions. Therefore, this study analyzed the financial fraud behavior of Luckin Coffee in China as an example and studied the causes and countermeasures of financial fraud. As a result, it was found that the cause of financial fraud lies in the opacity of cash flows from online transactions. The recommendations proposed by this study is to improve internal control systems in companies, develop risk management system, and establish comprehensive external supervision system

In this paper, an adaptive least recently frequently used (LRFU) replacement policy is proposed for named data network (NDN) in the industrial internet of things (IIoT) environment. Low-latency network communication has become the main focus in IIoT development. By applying NDN architecture with the proposed replacement policy, the system can minimize the network latency of IIoT due to the NDN router’s capabilities to cache content. The simulation result shows that the proposed adaptive LRFU outperforms other popular replacement policies based on various network performances metrics. In addition, future research trends regarding the testbed implementation NDN replacement policy are suggested.

In-band network telemetry (INT) is an emerging network monitoring framework based on a protocol-independent packet processor (P4). Network devices can be programmed with a domain-specific language to embed switch-internal states into data packets as they traverse through networks. However, the current P4-based INT does not support a sampling; thus, INT headers should be augmented for all incoming packets, which will incur high overhead in a large-scale network. In this paper, we propose a flexible sampling-based INT (FS-INT) scheme to address the aforementioned issues. Simulation results show that FS-INT can reduce the protocol overhead in a controlled manner while providing sufficiently high accuracy.

Smart transportation, powered by IoT, transforms mobility with interconnected sensors and devices collecting real-time data on traffic, vehicle locations, and passenger needs. This fosters a safer and more sustainable transportation ecosystem, optimizing traffic flow and enhancing public transit efficiency. However, security and privacy challenges emerge in smart transportation systems. Our proposed solution involves a deep neural network (DNN) model trained on extensive datasets from sustainable cities, incorporating historical information like traffic patterns and sensor readings. This model identifies potential malicious nodes, achieving a 90% accuracy rate in predicting threats such as Denial of Service 88%, Whitewash attacks 80%, and Brute Force attacks 75%. This high precision ensures the security and privacy of passenger vehicle data and routes.

이 연구는 지역 기업의 기술혁신성과 기업 성과의 관계를 울산지역 DNA(Data, Network, AI) 산업 분야 핵심원천기술 보유 기업의 특성 자료를 활용하여 실증적으로 분석하였다. 본 연구에서는 기업 성과를 기업의 매출액과 고용 창출로 구분하였으며, 주요 분석결과를 정리하면 다음과 같다. 첫째, 이노비즈기업 여부로 측정된 기업의 기술혁신성은 기업의 매 출액 제고에 긍정적인 기여를 하는 것으로 나타났다. 둘째, 기업이 보유한 연구원수와 특 허 등록건수도 기업의 매출액 증대에 긍정적인 영향을 미치는 것으로 나타났다. 셋째, 기 업의 기술혁신성은 기업의 고용 창출 성과 제고에 긍정적인 기여를 하는 것으로 나타났다. 넷째, 벤처기업일수록 그렇지 않은 기업에 비해 기업의 고용 창출 성과가 더 높은 것으로 나타났다. 본 연구의 이러한 분석결과들이 시사하는 바는 다음과 같다. 먼저, DNA 산업 분 야에서는 미래 유망분야 핵심원천기술을 보유한 기업들이 중요하기 때문에 기업의 기술혁 신성이 기업 성과를 제고하는데 매우 중요한 요소라는 점이다. 따라서, 기업의 R&D 투자와 신기술 특허 등록을 장려하는 정책적 지원을 통해 기업이 기술혁신성을 보유할 수 있도록 장려 필요가 있다. 다음으로, DNA 산업 분야에서는 기업이 미래 유망분야 핵심원천기술을 보유함으로써 기술혁신성을 지니고, 이를 통해 미래의 성장 가능성을 인정받는 벤처기업일 수록 더 많은 고용 창출 성과를 거둘 수 있을 것으로 예측할 수 있기 때문에 지방정부는 지역의 우수한 벤처기업들을 많이 발굴하여 육성할 수 있는 생태계를 조성할 필요성이 있다.

This study seeks to empirically analyze the relationship between technological innovativeness and corporate performance by using data of local corporate with original/core technology in the DNA (Data, Network, AI) industry in Ulsan. In this study, corporate performance was divided into corporate sales and job creation, and the empirical results of this study are summarized as follows. First, it was found that the technological innovativeness of a corporate, measured by whether it was an INNOBIZ company, positively contributed to the increase of the corporate's sales. Second, the number of R&D researchers and patent registrations held by a corporate also had a positive effect on the corporate's sales growth. Third, it was found that the technological innovativeness of a corporate makes a positive contribution to the increase in the employment of the corporate. Fourth, it was found that venture companies have a higher number of employees than those that do not. This study implies that corporate with original/core technology for a quite promising field of industry are very important economic actors in the DNA industry, therefore, technological innovativeness of the corporate is an extremely important factor in improving corporate performance. Thus, it is necessary to encourage corporate to possess technological innovativeness through government support policy that encourages the corporate to invest in R&D and register new patents. In the DNA industry, it can be predicted that venture companies that are recognized for their growth potential in the future will be able to increase employment more because they possess technological innovativeness by possessing original/core technologies in a quite promising field of industry for the future. Therefore, local governments need to create an ecosystem that can discover and nurture many excellent local venture companies.

최근 디지털 사회의 도래로 다양한 데이터가 폭발적으로 증가하고, 그중 문헌 내 주제어를 도출하는 토픽 모델링 에 관한 연구가 활발히 진행되고 있다. 본 논문의 연구목표는 토픽 모델링 방법 중 하나인 DTM(Dynamic Topic Model) 모델을 적용해 D.N.A.(Data, Network, A.I) 분야에 대한 연구동향을 탐색하는데 있다. 실험 데이터는 최근 6년간(2015∼2020) ICT(Information and Communication Technology) 분야 중 기술대분류가 SWㆍAI에 해당하 는 연구과제 1,519개 사업에 대해 DTM 모델을 적용하였다. 실험결과로, D.N.A. 분야의 기술 키워드 Big data, Cloud, Artificial Intelligence와 확장된 의미의 기술 키워드 Unstructured, Edge Computing, Learning, Recognition 등 이 매년 연구에 표출되었으며, 해당 키워드 들이 특정 연구과제에 종속되지 않고 다른 연구과제에서도 포괄적으로 연구 되고 있음을 확인하였다. 끝으로 본 논문의 연구결과는 향후 D.N.A. 분야에 대한 정책기획ㆍ과제기획 등 연구개발 기획 과정과 기업의 기술 확보전략ㆍ마케팅 전략 등 다양한 곳에 활용될 수 있을 것으로 기대한다.

The Topic Modeling research, the methodology for deduction keyword within literature, has become active with the explosion of data from digital society transition. The research objective is to investigate research trends in D.N.A.(Data, Network, Artificial Intelligence) field using DTM(Dynamic Topic Model). DTM model was applied to the 1,519 of research projects with SWㆍA.I technology classifications among ICT(Information and Communication Technology) field projects between 6 years(2015∼2020). As a result, technology keyword for D.N.A. field; Big data, Cloud, Artificial Intelligence, extended keyword; Unstructured, Edge Computing, Learning, Recognition was appeared every year, and accordingly that the above technology is being researched inclusively from other projects can be inferred. Finally, it is expected that the result from this paper become useful for future policyㆍR&D planning and corporation’s technologyㆍmarketing strategy.

소셜 네트워크는 이용자들의 공통적 관심 분야를 기반으로 서로 연결된 개별적 온라인 서비스 구조이다. 소셜 네트워킹 웹사이트의 사용은 온라인 커뮤니케이션과 데이터 공유 및 상호작용 방법을 혁신적으로 바꿔놓았다. 소셜 네트워킹 사이트의 사용량이 증가함에 따라 막대한 양의 사용자 상호작용 데이터가 매일 축적되고 있어 다양한 분야에서의 적용을 위한 대량의 소셜 네트워크 데이터 마이닝 및 분석 방안이 갈수록 주목받고 있다. OLAP데이터 분석은 효과적으로 소셜 데이터를 분석하기 위하여 사용될 수 있는 유용한 데이터 분석방법이지만 계속적으로 가변하는 대량 데이터와 OLAP쿼리의 복잡성 때문에 작업 시간이 오래걸리는 문제점이 있다. 본 논문은 대량의 소셜 네트워크 데이터 분석에 적합한 클라우드 컴퓨팅의 발전이 이루어지면서 효율적이고 빠른 데이터 분석을 위하여 클라우드 컴퓨팅 플랫폼을 사용하는 OLAP기반 소셜 데이터 분석도구에 대해 설명한다.

A social network is an online structure of individuals which are related to each other based on some common relationship of interest. The usage of social networking websites has literally revolutionized the methods of online communication, data sharing and interaction. As a result of the increased usage of social networking sites a massive amount of user interaction data is been generated daily. The mining and analysis of this large amount of social network data for a variety of applications is getting popularity. OLAP analysis is a famous data analysis method which can be used to effectively analyze social data. However, because of the huge amount of continuously changing data and the complexity of OLAP queries, this data analysis in a time consuming task. The advances in cloud computing motivated us to use the cost effective cloud computing infrastructure for the task of analyzing large amount of social network data. This paper presents an OLAP based social data analysis tool which uses cloud computing platform for efficient and timely analysis of data. The usage of cloud computing infrastructure results in reduction of computational cost, device and location independence, and an increase in peak-load capacity.

Since the government issued ICT priority policy for the last decade, Vietnam was reported to have impressed development of ICT infrastructure and Internet users. Until May of 2015, Vietnam has 39.7 millions of Internet users and 31 millions of social network user accounts. Facebook is the dominant website with more than 22 million Vietnamese users and 70% of those accesses Facebook via mobile phone. Several companies have utilized Facebook as the most effective interaction channels. The increasing of big text data such as posts and comments on Facebook that embed customer opinions requires method to mine sentiment text in Vietnamese language. This research applies machine learning with several algorithms such Naive-Bayes, decision trees and Support Vector Machine (SVM) for Vietnamese text data collected from fast-food industry on Facebook. The experiment results show that machine learning methods are able to classify Vietnamese sentiment text with the accuracy over 70%. Thus we proposed several recommendations for mining Vietnamese social text data.

This study explores the dimensions of resort guest experiences through big data collected from electronic word-of-mouth (eWOM) reviews of the top 25 all-inclusive resorts recognized by TripAdvisor’s Travelers’ Choice Awards 2025. A total of 59,857 online reviews collected from Google Reviews were analyzed using text mining, semantic network analysis, and CONCOR clustering techniques to identify the major experiential dimensions reflected in customer perceptions. The findings reveal that resort guest experiences are multidimensional and are shaped by both tangible and intangible attributes. Frequency and semantic network analyses identified highly central experiential dimensions related to service quality, food and beverage experiences, recreational activities, environmental aesthetics, emotional engagement, and destination atmosphere. The CONCOR analysis further classified the experiential dimensions into four major clusters: (1) service quality and hospitality, (2) recreational and emotional experiences, (3) food and beverage experiences, and (4) brand identity and destination atmosphere. The results indicate that guests evaluate all-inclusive resort experiences holistically through interconnected experiential structures rather than isolated service attributes. This study contributes to hospitality and tourism literature by demonstrating the usefulness of big data analytics and semantic network analysis in exploring guest experiences within eWOM environments. The findings also provide practical implications for resort managers seeking to improve customer experiences and strengthen competitive positioning in the hospitality industry.

As South Korea increasingly relies on foreign workers to address workforce shortages in its aging society, this study investigates occupational safety challenges foreign workers face, especially in labor-intensive industries. Despite their critical role in sectors such as manufacturing, foreign workers remain highly vulnerable to workplace hazards, primarily due to language barriers and cultural gaps that obstruct effective risk communication and safety instruction. Although multilingual policies have been introduced to address these limitations, they often fail to reflect the linguistic and cultural diversity of the foreign workforce. Existing research has underscored the necessity of culturally adaptive safety education; however, previous studies have predominantly relied on static survey data, limiting their responsiveness to rapidly evolving industrial risks. To bridge this gap, this study aims to analyze a substantial corpus of unstructured news articles related to industrial safety using text mining techniques and BERTopic-based modelling. This study identified seven key themes related to safety education. However, the current safety training were found to inadequately address the linguistic and cultural needs of foreign workers. Network analysis revealed structural links among policy, education, and integration, with foreign workers and safety education as central terms. The findings suggest the importance of developing safety training strategies that are culturally and linguistically responsive to the needs of foreign workers. By offering a data-driven exploration of overlooked safety issues, this study provides practical implications for improving occupational safety policy and fostering inclusive safety practices in South Korea’s high-risk industries.

무선센서 네트워크에서 안전한 데이터 수집은 중요한 보안이슈 중에 하나이다. 일반적으로 안전한 데이터 수집이란 데이터 자체의 보안과 안전한 전송 경로 확보를 의미한다. 본 논문은 이와 같은 관점에서 무선센서 네트워 크에서 안전한 데이터 수집 프로토콜을 제안한다. 제안하는 프로토콜은 계층형 센서네트워크를 고려한 계층형 키 보 안 기법과 안전을 보장할 수 있는 전송경로 설정을 핵심적으로 제시한다. 프로토콜은 네트워크 부하를 최소화 할 수 있도록 최적화 되었으며 네트워크 공격으로 인해 발생하는 문제점을 효과적으로 차단한다. 성능평가 결과 제안하는 프로토콜은 네트워크 퍼포먼스를 고려한 데이터 수집에 효율적이다. 데이터 수집 시 안전을 확보하기 위한 보안 분 석 역시 검증해 보았다.

A secure data gathering in a Wireless Sensor Network(WSN) has given attention to one of security issues. In general, the process of secure data gathering causes difficulties: one process is exchanging the secured data and the other is constructing secured data path. The previous studies have been resolving the difficulties in terms of two problems: security and data gathering in WSNs. However, a WSN requires a protocol that has to guarantee a security of path between sensors and sink, or a cluster head. Thus how to gather data securely is an important issue. In this paper, we propose a secure data gathering protocol over WSNs, which consists of hierarchical key settlement and secure path construction, and aims at tackling two problems. The proposed protocol causes little overhead to sensor nodes for secured key settlement and path construction. This work provides security analysis focused on the key settlement protocol and evaluates network performance for the proposed data gathering protocol through simulation.

The analytics lens of big data is very popular in both industry press and academic literature where big data is often discussed in the contexts of volume, velocity, and variety. This study offered an innovation lens of big data as digital service innovation. An evolutionary, ecosystem-based framework is adopted for digital service innovation. Digital trace data from Twitter provides the opportunity to see glimpses of big data ecosystems and the elements of big data services at two stages in the evolutionary process. Also, co-hashtag network analysis and community structure detection are used to understand the structure and dynamics of the big data ecosystem and its elements, over time. Finally, discussions and implications are presented.

본 연구는 근골격계 질환으로 입원한 환자의 의무기록지 키워드 네트워크 분석을 통해 근골격계와 관련된 표준 의료용어를 유추하여 보건의료현장의 비정형화된 데이터 활용 방안을 제시하기 위함이다. 분석 대상은 2010년부터 2019년까지 근골격계 질환 환자의 입퇴원요약지 145부로, 더아이엠씨(The IMC)에서 개발한 빅데이터 분석 솔루션인 TEXTOM을 활용하여 분석하였다. 1차·2차 정제과정을 통해 도출된 177개의 근골격계 관련 용어를 최종 분석하였다. 연구결과 다빈도 용어는 ‘Metastasis’, 의료용어 체계별 분석 결과에서 임상소견은 ‘Metastasis’, 증상은 ‘Weakness’, 진단은 ‘Hepatitis’, 처치는 ‘Remove’, 신체구조는 ‘Spine’, 약물은 ‘Oxycodone’이 가장 많이 사용되었다. 이러한 결과를 바탕으로 정형화되지 않은 의료데이터의 분석과 활용 및 관리 방안에 대한 시사점을 제안하고자 한다.

The purpose of this study is to suggest a plan to utilize atypical data in the health care field by inferring standard medical terms related to the musculoskeletal system through keyword network analysis of medical records of patients hospitalized for musculoskeletal disorders. The analysis target was 145 summaries of discharge with musculoskeletal disorders from 2015 to 2019, and was analyzed using TEXTOM, a big data analysis solution developed by The IMC. The 177 musculoskeletal related terms derived through the primary and secondary refining processes were finally analyzed. As a result of the study, the frequent term was 'Metastasis', the clinical findings were 'Metastasis', the symptoms were 'Weakness', the diagnosis was 'Hepatitis', the treatment was 'Remove', and the body structure was 'Spine' in the analysis results for each medical terminology system. 'Oxycodone' was used the most. Based on these results, we would like to suggest implications for the analysis, utilization, and management of unstructured medical data.

본 연구는 연구데이터 관련 국내 연구의 동향을 파악하기 위하여 RISS에서 연구데이터 관련 논문을 수집하였으며, 데이터 정제 후 총 58건의 연구논문을 대상으로 134개의 저자 키워드를 추출하여 키워드 네트워크 분석을 수행하였다. 분석 결과, 첫째, 아직까지 국내에서 연구데이터 관련 연구의 수가 58건에 지나지 않아 추후 많은 관련 연구가 진행될 필요가 있음을 알 수 있었다. 둘째, 연구데이터 관련 연구 분야는 대부분 복합학 중 문헌정보학에 집중되어 있었다. 셋째, 연구데이터 관련 저자 키워드의 빈도분석 결과 ‘연구데이터관리’, ‘연구데이터공유’, ‘데이터리포지터리’, ‘오픈사이언스’ 등이 다빈도 주요 키워드로 분석되어 연구데이터 관련 연구는 위의 키워드를 중심으로 진행되고 있음을 알 수 있었다. 키워드 네트워크 분석 결과에서도 다빈도 키워드는 연결 중심성 및 매개 중심성에서 중심적인 위치를 차지하며 관련 연구에서 핵심 키워드에 위치하고 있음을 알 수 있었다. 본 연구의 결과를 통하여 최근의 연구데이터 관련 동향을 파악할 수 있었고, 향후 집중적으로 연구해야 하는 분야를 확인할 수 있었다.

The goal of this study is to investigate domestic research trend on research data study. To achieve this goal, articles related research data topic were collected from RISS. After data cleansing, 134 author keywords were extracted from a total of 58 articles and keyword network analysis was performed. As a result, first, the number of studies related to research data in Korea is still only 58, so it was found that many related studies need to be conducted in the future. Second, most research fields related to research data were focused on library and information science among complex studies. Third, as a result of frequency analysis of author keywords related to research data, ‘research data management’, ‘research data sharing’, ‘data repository’, and ‘open science’ were analyzed as major frequent keywords, so research data-related research focuses on the above keywords. The keyword network analysis results also showed that high-frequency keywords occupy a central position in degree centrality and betweenness centrality and are located as core keywords in related studies. Through the results of this study, we were able to identify trends related to recent research data and identify areas that require intensive research in the future.

본 논문에서는 검색로그 데이터의 네트워크 분석방법을 통해 검색자들의 검색행위에 나타난 다양한 특성을 살펴보았다. 이러한 작업을 통해 얻어진 결과는 다음과 같다. 첫째, 검색자들은 검색어의 유사성에 따라 네트워크라는 연결구조를 나타내었다. 둘째, 특정한 검색자 네트워크에서 중심적인 위치를 차지하는 검색자들이 존재하였다. 셋째, 중심 검색자들은 다른 검색자들과 검색 키워드를 공유하고 있었다. 넷째, 전체 검색자들은 다수의 하위 집단으로 군집되어 있다. 이 연구의 결과는 네트워크 분석 방법에 의한 연관된 검색자와 검색어를 추천하는 알고리즘을 개발하는데 활용이 가능할 것이다.

This paper used the network analysis method to analyse a variety of attributes of searcher's search behaviors which was appeared on search access log data. The results of this research are as follows. First, the structure of network represented depending on the similarity of the query that user had inputed. Second, we can find out the particular searchers who occupied in the central position in the network. Third, it showed that some query were shared with ego-searcher and alter searchers. Fourth, the total number of searchers can be divided into some sub-groups through the clustering analysis. The study reveals a new recommendation algorithm of associated searchers and search query through the social network analysis, and it will be capable of utilization.

최근 전 세계적으로 운영되는 하·폐수 처리시설 관리에 있어 환경 보호를 위한 엄격한 규제로 수질오염 측정 시스템에 관한 시스템 이 부각되고 있으며, 대형시설 또는 산업단지 내의 자체 하수처리장이 건립되면서 하수처리장의 효율성 및 자동차에 대한 연구 및 개발이 활성화되고 있다. 하지만 기존 하수처리 시스템은 하·폐수에 특화된 수질감시 기술 및 이를 실시간 전송하기 위한 신뢰성 있는 네트워크 구축이 미흡한 실정이다. 따라서, 본 논문은 하수처리시설에서 슬러지의 농도를 효과적으로 측정 가능한 슬러지 멀티 메터의 데이터를 수집하고 멀티메터를 제어하기 위한 근거리 무선 네트워크의 데이터 구조 설계를 제안하였다. 근거리 무선 네트워 크를 통해 수집된 데이터는 중앙 모니터링 시스템으로 전달되어 실시간으로 데이터를 누적하고 통계를 산출하여 하수 처리 시설의 상태를 모니터링이 가능하다. 제안된 시스템은 근거리 무선 네트워크 시스템으로 IEEE 802.15.4와 비콘(Beacon)을 사용하여, 중앙시 스템에서 실시간 상태 확인이 가능하도록 IEEE 802.11네트워크를 구성하였으며, 하수처리시설에 슬러지 멀티 메터를 설치하고 통신 네트워크를 구축해서 그 효율성을 입증함으로써 제안 기술의 유용성을 확인하였다.

Recently, the management system of wastewater treatment facility has magnified due to the stringent regulations for the protection of the environment, and a sewage treatment plant efficiency and research of the car development are activated in large facilities or industrial park. however, the existing sewerage disposal system and specific water quality monitoring network reliability for real-time transmission of this building is insufficient. In this paper, we proposed a local wireless network design for sludge multi meter data collection and control for measuring the concentration of the sludge efficiently. Also, the collected data over the local wireless network to transmitted to the central monitoring system and accumulate the data in real time to calculate statistics is possible to monitor the status of the sewage treatment facilities. The proposed system uses a short-range wireless networks of IEEE 802.15.4 and configures an IEEE 802.11 network which can monitor real-time status in central system. Also, we install a sludge multimeter and communication network in sewage treatment facilities and confirm the usefulness of the proposed technique by demonstrating its effectiveness.