• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.1996-2011
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• 2021

To understand a trend is to explore the intricate process of how something or a particular situation is constantly changing or developing in a certain direction. This exploration is about observing and describing an unknown field of knowledge, not testing theories or models with a preconceived hypothesis. The purpose is to gain knowledge we did not expect and to recognize the associations among the elements that were suspected or not. This generally requires examining a massive amount of data to find information that could be transformed into meaningful knowledge. That is, looking through the lens of big-data analytics with an inductive reasoning approach will help expand our understanding of the complex nature of a trend. The current study explored the trend of well-being in South Korea using big-data analytic techniques to discover hidden search patterns, associative rules, and keyword signals. Thereafter, a theory was developed based on inductive reasoning - namely the hook, upward push, and downward pull to elucidate a holistic picture of how big-data implications alongside social phenomena may have influenced the well-being trend.

• Journal of Smart Tourism
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• 제1권3호
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• pp.3-5
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• 2021

The advancement of Information Communication Technology has provided tourism researchers with a golden opportunity to access big data, which plays a critical role in smart tourism. Recognizing the current issue, this paper discusses the evolution of the literature on tourism big data focusing on conceptual understanding of and types of big data, and insights from big data analytics. Indeed, this article provides important research agenda for future tourism researchers who would like to conduct academic research about big data and smart tourism.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2016년도 춘계학술대회
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• pp.317-320
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• 2016

2011년 NH농협 전산망마비 사건, 2013년 3.20 사이버테러 및 2015년 12월의 한국수력원자력 원전 중요자료 유출사건이 있었다. 이러한 사이버테러는 해외(북한)에서 조직적이고 장기간의 걸친 고도화된 APT공격을 감행하여 발생한 사이버테러 사건이다. 하지만, 이러한 APT공격(Advanced Persistent Threat Attack)을 방어하기 위한 탁월한 방안 아직 마련되지 못했다. APT공격은 현재의 관제 방식으로는 방어하기가 힘들다. 따라서, 본 논문에서는 빅데이터 분석을 통해 APT공격을 예측할 수 있는 방안을 연구한다. 본 연구는 대한민국 3계층 보안관제 체계 중, 정보공유분석센터(ISAC)를 기준으로 하여 빅데이터 분석, APT공격 및 취약점 분석에 대해서 연구와 조사를 한다. 그리고 외부의 블랙리스트 IP 및 DNS Log를 이용한 APT공격 예측 방안의 설계 방법, 그리고 전조현상 분석 방법 및 APT 공격에 대한 대응방안에 대해 연구한다.

• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.71-84
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• 2024

This paper presents a critical analysis of the current application of big data in higher education and how Learning Analytics (LA), and Educational Data Mining (EDM) are helping to shape learning in higher education institutions that have applied the concepts successfully. An extensive literature review of Learning Analytics, Educational Data Mining, Learning Management Systems, Informal Learning and Online Social Networks are presented to understand their usage and trends in higher education pedagogy taking advantage of 21st century educational technologies and platforms. The roles of and benefits of these technologies in teaching and learning are critically examined. Imperatively, this study provides vital information for education stakeholders on the significance of establishing a teaching and learning agenda that takes advantage of today's educational relevant technologies to promote teaching and learning while also acknowledging the difficulties of 21st-century learning. Aside from the roles and benefits of these technologies, the review highlights major challenges and research needs apparent in the use and application of these technologies. It appears that there is lack of research understanding in the challenges and utilization of data effectively for learning analytics, despite the massive educational data generated by high institutions. Also due to the growing importance of LA, there appears to be a serious lack of academic research that explore the application and impact of LA in high institution, especially in the context of informal online social network learning. In addition, high institution managers seem not to understand the emerging trends of LA which could be useful in the running of higher education. Though LA is viewed as a complex and expensive technology that will culturally change the future of high institution, the question that comes to mind is whether the use of LA in relation to informal learning in online social network is really what is expected? A study to analyze and evaluate the elements that influence high usage of OSN is also needed in the African context. It is high time African Universities paid attention to the application and use of these technologies to create a simplified learning approach occasioned by the use of these technologies.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.193-197
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• 2021

Customers are increasingly attracted towards different e-commerce websites and applications for the purchase of products significantly. This is the reason the sellers are moving to different internet based services to sell their products online. The growth of customers in this sector has resulted in the use of big data analytics to understand customers' behavior in predicting the demand of items. It uses a complex process of examining large amount of data to uncover hidden patterns in the information. It is established on the basis of finding correlation between various parameters that are recorded, understanding purchase patterns and applying statistical measures on collected data. This paper is a document of the bottom-up strategy used to manage the selling price of a first-time product for maximizing profit while selling it online. It summarizes how existing customers' expectations can be used to increase the sale of product and attract the attention of the new customer for buying the new product.

• Journal of applied mathematics & informatics
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• 제40권3_4호
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• pp.563-575
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• 2022

Many organizations seek statistical modelling facilitated by data analytics technologies for determining the prediction models associated with M&A (Merger and Acquisition). By combining these data analytics tool alongside with data collection approaches aids organizations towards M&A decision making, followed by achieving profitable insights as well. It promotes for better visibility, overall improvements and effective negotiation strategies for post-M&A integration. This paper explores on the impact of pre and post integration of M&A in a standard organizational setting via devising a suitable statistical model via employing techniques such as Naïve Bayes, K-nearest neighbour (KNN), and Decision Tree & Support Vector Machine (SVM).

• International Journal of Contents
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• 제17권1호
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• pp.1-10
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• 2021

Visual analytics is an emerging research field that combines the strength of electronic data processing and human intuition-based social background knowledge. This study demonstrates useful visual analytics with Tableau in conjunction with semantic network analysis using examples of sentiment flow and strategic communication strategies via Twitter in a blockchain domain. We comparatively investigated the sentiment flow over time and language usage patterns between companies with a good reputation and firms with a poor reputation. In addition, this study explored the relations between reputation and marketing communication strategies. We found that cryptocurrency firms more actively produced information when there was an increased public demand and increased transactions and when the coins' prices were high. Emotional language strategies on social media did not affect cryptocurrencies' reputations. The pattern in semantic representations of keywords was similar between companies with a good reputation and firms with a poor reputation. However, the reputable firms communicated on a wide range of topics and used more culturally focused strategies, and took more advantages of social media marketing by expanding their outreach to other social media networks. The visual big data analytics provides insights into business intelligence that helps informed policies.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2012년도 춘계학술발표대회논문집
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• pp.102-105
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• 2012

ONVIF is becoming a de factor standard specification for supporting interoperability among network video products, which also supports a specification for video analytics service. A smart IP network camera is an IP network supporting video analytics. In this paper, we present our efforts in integrating ONVIF Video Analytics Service into our currently developing smart IP network camera(SS IPNC; Soongsil Smart IP Network Camera). SSIPNC supports object detection, tracking, classification, and event detection with proprietary configuration protocol and meta data formats. SSIPNC is based on TI' IPNC ONVIF implementation which supports ONVI Core specification, and several ONVIF services such as device service, imaging service and media service, but not video analytics service.

• 정보보호학회논문지
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• 제30권5호
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• pp.957-966
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• 2020

최근 산업기밀보호센터의 통계에 의하면 국내 기밀유출 사고의 경우 전·현직 직원에 의해 기업기밀유출의 약 80%를 차지하고 이러한 내부자에 의한 정보유출 사고의 대다수가 허술한 보안 관리체계와 정보유출 탐지기술의 이유로 발생하고 있다. 내부자의 기밀유출을 차단하는 업무는 기업보안 부문에서 매우 중요한 문제이지만 기존의 많은 연구들은 내부자에 의한 유출위협보다는 외부 위협에 의한 침입에 대응하는데 초점이 맞추어져 있다. 따라서 본 논문에서는 기업 내에서 발생하는 다양한 비정상 행위를 효과적이고 효율적으로 탐지하기 위해 내부정보 유출 시나리오를 설계하고 시나리오에서 도출 된 유출 징후의 핵심 위험지표를 데이터 분석(Data analytics)함 으로써 정교하지만 신속하게 유출행위를 탐지하는 모형을 제시하고자 한다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권10호
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• pp.477-482
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• 2016

기업의 신속한 의사결정 및 전략적 정책 결정을 위해 빅데이터에 대한 고도 분석이 필수적으로 요구됨에 따라 대량의 데이터를 복수의 노드에 분산하여 처리하는 하둡 또는 스파크와 같은 분산 처리 플랫폼이 주목을 받고 있다. 최근 공개된 Spark SQL은 Spark 환경에서 SQL 기반의 분산 처리 기법을 지원하고 있으나, 기계학습이나 그래프 처리와 같은 반복적 처리가 요구되는 고도 분석 분야에서는 효율적 처리가 불가능한 문제가 있다. 따라서 본 논문은 이러한 문제점을 바탕으로 Spark 환경에서 고도 분석 지원을 위한 SQL 기반의 빅데이터 최적처리 엔진설계와 처리 프레임워크를 제안한다. 복수의 조건과 다수의 조인, 집계, 소팅 연산이 필요한 복합 SQL 질의를 분산/병행적으로 처리할 수 있는 최적화 엔진과 관계형 연산을 지원하는 기계학습 최적화하기 위한 프레임워크를 설계한다.