• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.334-342
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• 2022

Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.

• 한국콘텐츠학회논문지
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• 제22권9호
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• pp.614-627
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• 2022

본 연구는 국내 HR Analytics 연구에서 활용한 데이터와 분석방법을 탐색하여 향후 연구를 위한 기초자료를 제공하고 HR Analytics 연구 현황을 밝히는 것을 목적으로 한다. 이를 위하여 체계적 문헌고찰 방법을 활용하여 국내 KCI 등재 학술지에 수록된 실증연구 논문 78편을 선정하였고 해당 논문을 근로자 생애주기에 따라 분류하여 검토하였다. 문헌고찰 결과 다음과 같은 결과를 얻을 수 있었다. 첫째, 근로자 생애주기에 따른 HR Analytics 연구 동향을 살펴본 결과, 선행연구에서는 구성원의 유지(retention)와 관련한 연구가 가장 많았고 성과 관리에 대한 연구가 그 뒤를 이었다. 둘째, HR Analytics 연구에서 사용한 데이터를 살펴본 결과 각 연구는 해당 연구문제에 따라 다양한 데이터(정형, 비정형)를 활용하고 있었으며 데이터 출처 또한 조직내부 시스템부터 국가 통계 DB까지 매우 다양한 것으로 확인하였다. 셋째, 문헌고찰 결과 국내 HR Analytics 연구는 기술적, 진단적 분석이 가장 많으며, 예측 및 처방과 관련한 연구는 미미한 수준임을 알 수 있었다.

• International Journal of Computer Science & Network Security
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• 제23권6호
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• pp.68-76
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• 2023

Nowadays, the power of data analytics in general and visual data analytics, in particular, have been proven to be an important area that would help development in any domain. Many well-known IT services best practices have touched on the importance of data analytics and visualization and what it can offer to information technology service management. Yet, little research exists that summarises what is already there and what can be done to utilise further the power of data analytics and visualization in this domain. This paper is divided into two main parts. First, a number of IT service management tools have been summarised with a focus on the data analytics and visualization features in each of them. Second, interviews with five senior IT managers have been conducted to further understand the usage of these features in their organisations and the barriers to fully benefit from them. It was found that the main barriers include a lack of good understanding of some visualization design principles, poor data quality, and limited application of the technology and shortage in data analytics and visualization expertise.

• Educational Technology International
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• 제24권2호
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• pp.295-329
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• 2023

The recent development of metaverse-related technology has led to efforts to overcome the limitations of time and space in education by creating a virtual educational environment. To make use of this platform efficiently, applying learning analytics has been proposed as an optimal instructional and learning decision support approach to address these issues by identifying specific rules and patterns generated from learning data, and providing a systematic framework as a guideline to instructors. To achieve this, we employed an inductive, bottom-up approach for framework modeling. During the modeling process, based on the activity system model, we specifically derived the fundamental components of the learning analytics framework centered on learning activities and their contexts. We developed a prototype of the framework through deduplication, categorization, and proceduralization from the components, and refined the learning analytics framework into a 7-stage framework suitable for application in the metaverse through 3 steps of Delphi surveys. Lastly, through a framework model evaluation consisting of seven items, we validated the metaverse learning analytics framework, ensuring its validity.

• 한국정보시스템학회지:정보시스템연구
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• 제30권1호
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• pp.21-44
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• 2021

Purpose The opportunities and approaches to big data have grown in various ways in the digital era. Business analytics is nowadays an inevitable strategy for organizations to earn a competitive advantage in order to survive in the challenged environments. The purpose of this study is to analyze the intellectual structure of business analytics literature to have a better insight for the organizations to the field. Design/methodology/approach This research analyzed with the data extracted from the database Web of Science. Total of 427 documents and 23,760 references are inserted into the analysis program CiteSpace. Author co-citation analysis is used to analyze the intellectual structure of the business analytics. We performed clustering analysis, burst detection and timeline analysis with the data. Findings We identified seven sub- areas of business analytics field. The top four sub-areas are "Big Data Analytics Infrastructure", "Performance Management System", "Interactive Exploration", and "Supply Chain Management". We also identified the top 5 references with the strongest citation bursts including Trkman et al.(2010) and Davenport(2006). Through timeline analysis we interpret the clusters that are expected to be the trend subjects in the future. Lastly, limitation and further research suggestion are discussed as concluding remarks.

• 한국컴퓨터그래픽스학회논문지
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• 제22권5호
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• pp.27-36
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• 2016

컴퓨터 그래픽스 (Computer Graphics) 및 인간-컴퓨터 상호작용 (Human-Computer Interaction, HCI) 기술을 기반으로 효과적인 데이터 분석을위한 가시화 툴 (Tool) 기술이 크게 발전 하였다. 해당 기술 분야는 Visual Analytics (비주얼애널리틱스)라는 연구 분야로 발전하여 2006년 첫 심포지엄이 열린 이래, 다양한 데이터 마이닝 (Data Mining), 상호작용 (Interaction) 기술이 정보 가시화 (Information Visualization) 기술에 접목하여 사용자 중심의 빅 데이터분석 및 의사 결정 시스템을 연구하는 분야로 확장 되었다. 그러나 국내에서는 아직 해당 연구 분야에 대하여 제대로 알려지지 않아, 국내 컴퓨터 그래픽스 및 HCI 기술 연구에 비하여, 가시화 기술을 통한 빅데이터 분석 및 의사결정을 지원하는 시스템을 설계 하는 기술이 뒤쳐지는 편이다. 따라서 본 논문에서는 비주얼 애널리틱스 연구의 기본 철학을 살펴 보고, IEEE Symposium on Visual Analytics Science and Technology (VAST) 학회에 2015년 출판된 논문으로 사용된 데이터 및 가시화 기술 분석 서베이를 진행함으로써 국내 컴퓨터 그래픽스 연구자들의 해당 분야에 대한 이해를 돕고자 한다.

• Asia pacific journal of information systems
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• 제30권2호
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• pp.397-419
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• 2020

Business analytics is a management tool for achieving significant business performance improvements. Many organizations fail to or only partially achieve their business objectives and goals from business analytics. Business analytics adoption is a multi-stage complex activity consisting of evaluation, adoption, and assimilation stages. Several research papers have been published in the field of business analytics, but the research on multi-stage BA adoption is fewer in number. This study contributes to the scant literature on the multi-stage adoption model by identifying the critical themes for evaluation, adoption, and assimilation stages of business analytics. This study uses the thematic content analysis of peer-reviewed published academic papers as a research technique to explore the key themes of business analytics adoption. This study links the critical themes with the popular theoretical foundations: Resource-Based View (RBV), Dynamic Capabilities, Diffusion of Innovations, and Technology-Organizational-Environmental (TOE) framework. The study identifies twelve major factors categorized into three key themes: organizational characteristics, innovation characteristics, and environmental characteristics. The main organizational factors are top management support, organization data environment, centralized analytics structure, perceived cost, employee skills, and data-based decision making culture. The major innovation characteristics are perceived benefits, complexity, and compatibility, and information technology assets. The environmental factors influencing BA adoption stages are competition and industry pressure. A conceptual framework for the multi-stage BA adoption model is proposed in this study. The findings of this study can assist the practicing managers in developing a stage-wise operational strategy for business analytics adoption. Future research can also attempt to validate the conceptual model proposed in this study.

• 한국IT서비스학회지
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• 제17권2호
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• pp.207-218
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• 2018

Recently, it becomes a big trend in the banking industry to apply a big data analytics technique to extract essential knowledge from their customer database. Such a trend is based on the capability to analyze the big data with powerful analytics software and recognize the value of big data analysis results. However, there exits still a need for more systematic theory and mechanism about how to adopt a big data analytics approach in the banking industry. Especially, there is no study proposing a practical case study in which big data analytics is successfully accomplished from the marketing perspective. Therefore, this study aims to analyze a target marketing case in the banking industry from the view of big data analytics. Target database is a big data in which about 3.5 million customers and their transaction records have been stored for 3 years. Practical implications are derived from the marketing perspective. We address detailed processes and related field test results. It proved critical for the big data analysts to consider a sense of Veracity and Value, in addition to traditional Big Data's 3V (Volume, Velocity, and Variety), so that more significant business meanings may be extracted from the big data results.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1516-1529
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• 2016

While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.

• 한국빅데이터학회지
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• 제3권2호
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• pp.123-130
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• 2018

산업 및 학계에서 빅데이터 분석 기술에 대한 활용 사례와 범위가 증가하면서, 이와 함께 빅데이터 분석 전문가에 대한 기업체들의 수요도 늘고 있다. 이러한 추세에 맞게 대학교들은 새로운 빅데이터 분석 교육과정들을 개발하여 수년 전부터 빅데이터 분석 전문가 양성을 위한 교육과정들을 제공하기 시작하였다. 본 연구에서는 9개 국내 대학, 20개 해외 대학의 빅데이터 분석 관련 석사과정 커리큘럼을 조사하였다. 국내 대학 프로그램과 해외 대학 프로그램을 비교한 결과, 한 학교 프로그램 당 평균 과목수는 국내 대학 프로그램이 더 많으나, 과목의 다양성 측면에서는 더 부족한 것으로 나타났다.