• Journal of Korean Library and Information Science Society
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• v.38 no.3
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• pp.321-334
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• 2007

Marketing techniques developed for private organizations are being widely used in libraries. The primary focus in marketing has shifted to maximizing user perceived value from the perspective of library users. This paper examines library as an industry as a way to establish the basis for library's overall value, applies the R-I-R model and its taxonomy of value in using library and information services, and proposes market orientation approach to systematically plan and coordinate academic library marketing activities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.151-154
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• 2021

Big data analysis is the process of discovering meaningful new correlations, patterns, and trends in large volumes of data stored in data stores and creating new value. Thus, most big data analysis technology methods include data mining, machine learning, natural language processing, and pattern recognition used in existing statistical computer science. Also, using the R language, a big data tool, we can express analysis results through various visualization functions using pre-processing text data. The data used in this study was analyzed for 21 papers in the March 2021 among the journals of the Korea Institute of Information and Communication Engineering. In the final analysis results, the most frequently mentioned keyword was "Data", which ranked first 305 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.326-327
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• 2021

Newly, big data utilization has been widely interested in a wide variety of industrial fields. Big data analysis is the process of discovering meaningful new correlations, patterns, and trends in large volumes of data stored in data stores and creating new value. Thus, most big data analysis technology methods include data mining, machine learning, natural language processing, and pattern recognition used in existing statistical computer science. Also, using the R language, a big data tool, we can express analysis results through various visualization functions using pre-processing text data. The data used in this study were analyzed for 29 papers in a specific journal. In the final analysis results, the most frequently mentioned keyword was "Research", which ranked first 743 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.428-431
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• 2017

기업의 성장성과 기업 주식가치를 매출, 매출원가, 영업이익율 등의 정형데이터와 경제, 경영관련 뉴스 등 비정형 데이터를 토대로 다양한 알고리즘을 활용해 분석하고, 그 결과의 유의성을 검증한다. 주성분회귀분석, 인공신경망, 나이브 베이지안 분류자, 긍/부정 사전분석 모델을 통해 분석된 결과를 검토하여 각 분석모델 별 성능을 확인하고, 기업 성장성 예측을 위해 활용 가능한 모델과 필요한 데이터를 제시한다.

• Management & Information Systems Review
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• v.36 no.1
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• pp.273-290
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• 2017

Internet of Things (IoT) is considered to be the next wave of Information Technology transformation after the Internet has changed the process of doing business. Since the domain of IoT ranging from the sensor technology to service to the users is wide, the structure of the research domain is not delineated clearly. To do that we suggest to use the Technology Stack Model proposed by Porter et al.(2014) to measure the maturity level of IoT in organizations. Based on the Stack Model, for the general understandings of IoT, we do keyword analyses on the academic papers whose major research issue is IoT. It is found that the current status of IoT application from the perspectives of cloud and big data analytics is not active, meaning that the real value of IoT has not been realized. We also examine the cases which deal with the part of cloud process which is crucial for value accrual. Based on these findings, we suggest the future direction of IoT research. We also propose that IT is to value chain what IoT is to the Stack Model to derive value in organizations.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.77-88
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• 2021

Cloud computing is getting a lot of attention by many people and businesses due to IT environmental changes such as the proliferation of smart devices, the increase of digital data, and the cost of IT resources. More individuals use personal cloud computing services for storing and managing information and data. Therefore, this study proposed determinants that are expected to have an influence on evaluating the value of cloud computing based on the value-based adoption model, examining the relationship between the continuous use intention of cloud computing. Results of the study show that usefulness, convenience of information access, extensibility had a positive impact on perceived value while privacy concerns and costs had a negative impact on perceived value. In addition, perceived value was found to have a significant effect on the intention to continue use of cloud computing. Finally, trust was found to have a significant effect on the perceived value and the intention to continue use of cloud computing. The findings are expected to provide useful information for understanding the factors that individual users consider important in the steadily growing cloud computing market.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.73-74
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• 2016

IoT 데이터는 비정형 데이터로 가공되고 분석하였을 때 비로소 가치를 갖기에 전 세계적으로 빅데이터 기술에 관심이 집중되고 있다. IoT 데이터 중 많은 부분을 차치하는 센서 데이터는 수집이 용이하고 활용범위가 넓기 때문에 여러 분야에서 사용되고 있다. 하지만 센서가 정상적으로 작동하지 못한 경우에는 실제와는 다른 값인 이상치를 포함하여 왜곡된 결과가 도출되어 활용할 수 없는 경우가 생긴다. 따라서 본 논문에서는 정확한 결과를 도출하기 위하여 수집된 원자료의 데이터를 분석하기 전에 이상치 탐지 및 제거를 하고자 한다. 또한 점점 늘어나고 있는 대용량 데이터를 신속하게 처리하기 위하여 메모리 접근방식인 스파크를 사용한 분산처리환경에서 이상치 탐지 및 제거하는 것을 제안한다. 맵리듀스 기반의 이상치 탐지 및 제거는 총 4단계로 나누어 구현하였으며 제안한 기법의 성능 평가를 위해 총 3가지 환경에서 비교하여 실험하였다. 실험을 통해 데이터의 용량이 커질수록 분산처리환경에서 스파크를 사용하여 처리하는 방식이 가장 빠를 것 이라는 결과를 얻었다.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.109-115
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• 2009

Many internet companies are holding marketing activity through customer relationship management to satisfy complicated and diversified consumer demands. Use of customer lifetime value as a marketing metric tends to place greater emphasis on customer service and long-term customer satisfaction, rather than on maximizing short term sales. And so many internet companies have been interested in customer lifetime value, which is a primary key for discovery customer values to promote the competitive power in their business fields. In this paper, we apply a life table technique to lifetime analysis of internet site customers by membership pattern and provide the opportunity using revised life tables in several kinds of internet companies.

• Journal of Information Management
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• v.33 no.4
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• pp.107-128
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• 2002

The purpose of this study is developing a information support system for R&D decision making to maximize economic results of the R&D. This system is composed of studying the model of work flow for R&D decision making, analyzing a technology information, connecting with the databases from KISTI and others, and valuing R&D technology on line. Especially in the case of technology valuation, this system is combined with the valuation model which supports knowledge information for helping more objective estimation.