• Information and Communications Magazine
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• v.31 no.11
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• pp.35-45
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• 2014

주성분 분석 기법(PCA)는 가장 널리 사용되는 데이터 차원 감소 (dimensionality reduction) 기법으로 알려져 있다. 하지만 데이터에 이상점 (outlier)가 존재하는 환경에서는 성능이 크게 저하된다는 단점을 가지고 있다. Rank-Sparsity(Robust PCA) 기법은 주어진 행렬을 low-rank 행렬과 저밀도(sparse)행렬의 합으로 분해하는 방식으로, 이상점이 많은 환경에서 PCA기법을 효과적으로 대체할 수 있는 알고리즘으로 알려져 있다. 본 고에서는 RPCA 기법을 간략히 소개하고, 그의 적용분야, 및 알고리즘에 관한 연구들을 대해서 알아본다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.107-117
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• 2020

With the recent development of autonomous vehicles, various sensors installed in vehicles have become common, and big data generated from those sensors is increasingly being used in the transportation field. In this study, we proposed a grid index method to efficiently process real-time vehicle sensing big data and public data such as road weather. The applicability and effect of the proposed grid space division method and grid ID generation method were analyzed. We created virtual data based on DTG data and mapped to the road link based on coordinates. As a result of analyzing the data processing speed in grid index method, the data processing performance improved by more than 2,400 times compared to the existing link unit processing method. In addition, in order to analyze the efficiency of the proposed technology, the virtually generated data was mapped and visualized.

• Proceedings of the Korean Society for Information Management Conference
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• 2014.08a
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• pp.17-20
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• 2014

본 연구는 동시출현 단어분석 기법을 이용하여 최근 전세계적으로 많은 주목을 받고 있는 빅데이터(Big Data) 관련 연구 동향과 연구 영역을 분석하는 것을 목적으로 한다. 이를 위하여 인용색인데이터베이스인 Web of Science SCIE(Science Citation Index Expanded)에서 분석 대상 논문을 수집하였다. 논문 수집을 위한 검색식은 은 Title(논문 제목), Abstract(초록), Author Keywords(저자 키워드), Keywords $Plus^{(R)}$의 네 가지 필드를 동시에 검색하는 주제어(topic)가 "big data"를 포함하고 있는 논문 563편을 대상으로 동시출현단어 분석을 수행하였다.

• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.53-54
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• 2016

빅데이터란 대용량 데이터 활용 및 분석을 통해 가치 있는 정보를 추출하고, 이를 바탕으로 대응 방안 도출 또는 변화를 예측하는 기술을 의미한다. 그리고 빅데이터 분석에 활용되는 데이터인 페이스북과 같은 소셜 데이터, 유전자 발현과 같은 바이오 데이터, 항공망과 같은 지리정보 데이터들은 대용량 네트워크로 구성되어 있다. 네트워크 클러스터링은 서로 유사한 특성을 갖는 네트워크 내의 데이터들을 동일한 클러스터로 묶는 기법으로 네트워크 데이터를 분석하고 그 특성을 파악하는데 폭넓게 사용된다. 최근 빅데이터가 다양한 분야에서 활용되면서 방대한 양의 네트워크 데이터가 생성되고 있고, 이에 따라서 대용량 네트워크 데이터를 효율적으로 처리하는 클러스터링 기법의 중요성이 증가하고 있다. MCL(Markov Clustering) 알고리즘은 플로우 기반 무감독(unsupervised) 클러스터링 알고리즘으로 확장성이 우수해 다양한 분야에서 활용되고 있다. 하지만, MCL은 대용량 네트워크에 대해서는 많은 클러스터링 연산을 요구하며 너무 많은 클러스터를 생성하는 문제를 갖는다. 본 논문에서는 네트워크 압축을 기반으로 한 클러스터링 알고리즘을 제안함으로써 MCL보다 클러스터링 속도와 정확도를 향상시켰다. 또한, 희소행렬을 효율적으로 저장하는 CSC(Compressed Sparse Column) 자료구조와 MapReduce 기법을 제안한 클러스터링 알고리즘에 적용함으로써 대용량 네트워크에 대한 클러스터링 속도를 향상시켰다.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.163-170
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• 2021

As a big data is being used in various industries, big data market is expanding from hardware to infrastructure software to service software. Especially it is expanding into a huge platform market that provides applications for holistic and intuitive visualizations such as big data meaning interpretation understandability, and analysis results. Demand for big data extraction and analysis using social media such as SNS is very active not only for companies but also for individuals. However despite such high demand for the collection and analysis of social media data for user trend analysis and marketing, there is a lack of research to address the difficulty of dynamic interlocking and the complexity of building and operating software platforms due to the heterogeneity of various social media service interfaces. In this paper, we propose a method for developing a framework to operate the process from collection to extraction and classification of social media data. The proposed framework solves the problem of heterogeneous social media data collection channels through adapter patterns, and improves the accuracy of social topic extraction and classification through semantic association-based extraction techniques and topic association-based classification techniques.

• The Journal of Bigdata
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• v.1 no.2
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• pp.65-78
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• 2016

In the rapid change of business environment, it is crucial that several companies with core competence cooperate together in order to deliver competitive products to the market faster. Thus a lot of companies are participating in supply chains and SCM (Supply Chain Management) become more important. To efficiently manage supply chains, the analysis of data from SCM systems is required. In this paper, we explain how to analyze SCM related data with process mining techniques. After discussing the data requirement for process mining, several process mining techniques for the data analysis are explained. To show the applicability of the techniques, we have performed a case study with a company in South Korea. The case study shows that process mining is useful tool to analyze SCM data. On specifically, an overall process, several performance measures, and social networks can be easily discovered and analyzed with the techniques.

• Journal of Convergence Society for SMB
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• v.6 no.1
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• pp.17-23
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• 2016

Recently, Internet services via various devices including smartphone have become available. Because of the development of ICT, numerous hacking incidents have occurred and most of those attacks turned out to be APT attacks. APT attack means an attack method by which a hacker continues to collect information to achieve his goal, and analyzes the weakness of the target and infects it with malicious code, and being hidden, leaks the data in time. In this paper, we examine the information collection method the APT attackers use to invade the target system in a short time using big data, and we suggest and evaluate the countermeasure to protect against the attack method using big data.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.745-750
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• 2021

In Big data visualization analysis of unstructured text data, raw data is mostly large-capacity, and analysis techniques cannot be applied without cleansing it unstructured. Therefore, from the collected raw data, unnecessary data is removed through the first heuristic cleansing process and Stopwords are removed through the second machine cleansing process. Then, the frequency of the vocabulary is calculated, visualized using the word cloud technique, and key issues are extracted and informationalized, and the results are analyzed. In this study, we propose a new Stopword cleansing technique using an external Stopword set (DB) in Python word cloud, and derive the problems and effectiveness of this technique through practical case analysis. And, through this verification result, the utility of the practical application of word cloud analysis applying the proposed cleansing technique is presented.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.111-119
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• 2014

The cloud environment need resource management method that to enable the big data issue and data analysis technology. Existing resource management uses the limited calculation method, therefore concentrated the resource bias problem. To solve this problem, the resource management requires the learning-based scheduling using resource history information. In this paper, we proposes the ART (Adaptive Resonance Theory)-based adaptive resource management. Our proposed method assigns the job to the suitable method with the resource monitoring and history management in cloud computing environment. The proposed method utilizes the unsupervised learning method. Our goal is to improve the data processing and service stability with the adaptive resource management. The propose method allow the systematic management, and utilize the available resource efficiently.

• Information and Communications Magazine
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• v.31 no.11
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• pp.46-51
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• 2014

최근 정보통신 기술의 발전과 더불어 급성장 중인 소셜 네트워크는 개인 혹은 집단간의 실제 사회적 관계를 네트워크 구조로 반영하고 있다. 소셜 네트워크를의 구조를 보다 정확하게 이해하고 소셜 네트워크 내에서 정보가 전파되는 패턴을 파악하기 위해 소셜 네트워크를 수학적으로 모델링하고, 이를 응용하여 소셜 네트워크 빅 데이터를 분석하는 다양한 연구가 이루어지고 있다. 본고에서는 소셜 네트워크의 구조 분석과 정보 확산 패턴 파악에 관한 주요 연구 사례들을 소개하고, 특히 소셜 빅 데이터 분석과 관련된 연구 주제 및 응용 사례들을 살펴보고자 한다.