• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.1-6
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• 2019

Recently, big data and artificial intelligence (AI) based on communication systems have become one of the hottest issues in the technology sector, and methods of analyzing big data using AI approaches are now considered essential. This paper presents diverse paradigms to subjects which deal with diverse research areas, such as image segmentation, fingerprint matching, human tracking techniques, malware distribution networks, methods of intrusion detection, digital image watermarking, wireless sensor networks, probabilistic neural networks, query processing of encrypted data, the semantic web, decision-making, software engineering, and so on.

• Journal of Information Technology Services
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• v.17 no.2
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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.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1445-1447
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• 2012

팀으로 구성된 개발 조직은 주어진 시간 안에 가용할 수 있는 인력으로 고객에게 전달될 최종 산출물인 소프트웨어를 개발한다. 개발과 통합을 거친 소프트웨어는 테스트를 거치며 발생되는 결함에 대해 수정 조치한다. 하지만 결함 수정과 병행하여 초기 요구사항은 변경되고 추가되어 개발되는 소프트웨어에 반영되야 함으로 팀에서는 메일, 전화, 또는 게시판 방식으로 결함 사항을 알리고 최종 소프트웨어를 개발해 나간다. 본 논문은 이러한 환경에 결함추적도구를 사용하여 결함 발견 시 등록하고, 해결된 소프트웨어의 버전을 관리하기 위한 방법에 대해 오픈소스 기반의 형상관리도구와 결함추적도구를 활용한 환경을 연구해 나가고자 한다.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.47-57
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• 2013

Big data analysis needs a new tool for decision making in view of data volume, speed, and variety. Many global IT enterprises are announcing a variety of Big data products with easy to use, best functionality, and modeling capability. Big data packages are defined as a solution represented by analytic tools, infrastructures, platforms including hardware and software. They can acquire, store, analyze, and visualize Big data. There are many types of products with various and complex functionalities. Because of inherent characteristics of Big data, selecting a best Big data package requires expertise and an appropriate decision making method, comparing the selection problem of other software packages. The objective of this paper is to suggest a decision making method for selecting a Big data package. We compare their characteristics and functionalities through literature reviews and suggest selection criteria. In order to evaluate the feasibility of adopting packages, we develop two Analytic Hierarchy Process(AHP) models where the goal node of a model consists of costs and benefits and the other consists of selection criteria. We show a numerical example how the best package is evaluated by combining the two models.

• ETRI Journal
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• v.34 no.2
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• pp.292-295
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• 2012

Beyond the simple keyword matching methods in contextual advertising, we propose a rich contextual matching (CM) model adopting a classification method for topic targeting and a query expansion method for semantic ad matching. This letter reports on an investigation into the empirical effects of the CM model by comparing the click-through rates (CTRs) of two practical online news advertising systems. Based on the evaluation results from over 100 million impressions, we prove that the average CTR of our proposed model outperforms that of a traditional model.

• International Journal of Contents
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• v.13 no.1
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• pp.38-44
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• 2017

Increased use of software and complexity of software functions, as well as shortened software quality evaluation periods, have increased the importance and necessity for automation of software testing. Automating software testing by using machine learning not only minimizes errors in manual testing, but also allows a speedier evaluation. Research on machine learning in automated software testing has so far focused on solving special problems with algorithms, leading to difficulties for the software developers and testers, in applying machine learning to software testing automation. This paper, proposes a new machine learning framework for software testing automation through related studies. To maximize the performance of software testing, we analyzed and categorized the machine learning algorithms applicable to each software test phase, including the diverse data that can be used in the algorithms. We believe that our framework allows software developers or testers to choose a machine learning algorithm suitable for their purpose.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.630-644
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• 2021

Educational data analysis is attracting increasing attention with the rise of the big data industry. The amounts and types of learning data available are increasing steadily, and the information technology required to analyze these data continues to develop. The early identification of potential dropout students is very important; education is important in terms of social movement and social achievement. Here, we analyze educational data and generate predictive models for student dropout using logistic regression, a decision tree, a naïve Bayes method, and a multilayer perceptron. The multilayer perceptron model using independent variables selected via the variance analysis showed better performance than the other models. In addition, we experimentally found that not only grades but also extracurricular activities were important in terms of preventing student dropout.

• Informatization Policy
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• v.24 no.3
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• pp.42-66
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• 2017

This study investigated the ecological change of public software industry, comparing the pre and post structure of industry network based on the application of the regulation restricting large company participation in public software market. For this purpose, we used big data of the software market from Korea Public Procurement Service and used the SNA(Social Network Analysis) methodology which is being actively used in the area of social science recently. Finally, we highlighted the contribution of open public data. By analyzing order and contract data of the public software industry for 3 years - from 2013 to 2015 - we found out two main things. First, we observed that Power Law distribution had been going on in the public software industry, regardless of the external impact of regulation. Second, despite the existence of such Power Law distribution, we also observed the ecological change of industry structure from year to year. We presented the implication of such findings and discussed the advantage of open public data as the original motivator of this study.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.290-296
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• 2022

Big Data is a data analysis technology empowered by late advances in innovations and engineering. In any case, big data involves a colossal responsibility of equipment and handling assets, making reception expenses of big data innovation restrictive to little and medium estimated organizations. Cloud computing offers the guarantee of big data execution to little and medium measured organizations. Big Data preparing is performed through a programming worldview known as MapReduce. Normally, execution of the MapReduce worldview requires organized joined stockpiling and equal preparing. The computing needs of MapReduce writing computer programs are frequently past what little and medium measured business can submit. Cloud computing is on-request network admittance to computing assets, given by an external element. Normal arrangement models for cloud computing incorporate platform as a service (PaaS), software as a service (SaaS), framework as a service (IaaS), and equipment as a service (HaaS).