• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.271-278
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• 2022

MyData service integrates and manages user's personal data such as finance, credit, etc, and is expected to provide useful information to the user as personal data in various fields are gradually integrated. Discovering factors that affect the intention to use of MyData service is a very important topic for understanding that service. To this end, in this study, the attributes of the MyData service were derived, and the derived service attributes were grouped together by using factor analysis. As a result, we found four factors such as "convenience", "usefulness", "security", and "control". After that, we established our research model to analyze the causal relationship between these four factors and the intention to use of MyData service. According to the analysis results, among the factors of the MyData service attribute, "convenience", "usefulness", and "control" had a significant effect, and "security" did not have a significant effect. In particular, it was confirmed that "control" had the greatest influence on the intention to use. This study suggests that MyData service provider need to make users recognize they are controlling their data and develop services that provide various benefits to users.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.478-480
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• 2005

센서 네트워크는 다수의 센서 노드들이 센싱된 데이터를 보고하는 형태의 네트워크로 기존 데이터 중심(Data-centric) 통신 모델은 오버헤드와 응답 속도의 저하와 관련된 문제점을 노출하고 있으며, 이를 해결하기 위한 방안으로 속성 기반 네이밍(Attribute-based naming)이 새로운 라우팅 구조로 주목받고 있다. 본 연구에서는 가상 대응체 (Virtual Counterpart) 개념을 센서 네트워크에 적용하여 속성 기반 네이밍을 싱크 노드내의 가상 센서 노드에서 처리해주는 구조를 제안하였다. 기존의 다른 속성 기반 네이밍 연구들과 달리 리얼센서에 대응되는 가상 센서 노드를 싱크 노드에서 운용하고, 리얼 센서의 데이터를 주기적으로 업데이트한 후, 속성 기반 쿼리를 가상 센서 노드가 리얼 센서를 대행하여 처리하는 구조를 설계하였다. 이런 구조를 취함으로써 효율적인 응답 처리와 하부 네트워크에 비종속적인 속성 기반 네이밍이 가능하며, 쿼리의 확장성과 센서들의 결합을 통한 부가적인 기능을 제공할 수 있게 된다.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.94-99
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• 2008

In the pattern classification problem, feature selection is an important technique to improve performance of the classifiers. Particularly, in the case of classifying with a large number of features or variables, the accuracy of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. In this paper we propose a feature selection method using genetic algorithm and information theory. Experimental results show that this method can achieve better performance for pattern recognition problems than conventional ones.

• Journal of Digital Convergence
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• v.17 no.3
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• pp.107-117
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• 2019

In the era of SNS, many people relies on it to express their emotions about various kinds of products and services. Therefore, for the companies eagerly seeking to investigate how their products and services are perceived in the market, emotion mining tasks using dataset from SNSs become important much more than ever. Basically, emotion mining is a branch of sentiment analysis which is based on BOW (bag-of-words) and TF-IDF. However, there are few studies on the emotion mining which adopt feature selection (FS) methods to look for optimal set of features ensuring better results. In this sense, this study aims to propose FS methods to conduct emotion mining tasks more effectively with better outcomes. This study uses Twitter and SemEval2007 dataset for the sake of emotion mining experiments. We applied three FS methods such as CFS (Correlation based FS), IG (Information Gain), and ReliefF. Emotion mining results were obtained from applying the selected features to nine classifiers. When applying DT (decision tree) to Tweet dataset, accuracy increases with CFS, IG, and ReliefF methods. When applying LR (logistic regression) to SemEval2007 dataset, accuracy increases with ReliefF method.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.210-212
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• 1999

공간 데이터베이스 시스템에 관한 연구는 다양한 분야에서 진행되고 있으며 특히 공간 데이터베이스 시스템의 가장 중요한 응용인 지리정보시스템에 대한 연구가 중점적으로 이루어지고 있다. 지리정보시스템에서 이용되는 공간 객체는 Point, Lie, Region 등으로 분류되며 불규칙한 n차원의 가변 길이 속성을 가진다. 이는 또한 공간 객체간의 위상 관계와 위치 정보를 포함하여야 한다. 본 논문에서는 기존의 저장구조를 용이하게 확장 가능할 수 있도록 하는 관계표현을 이용한 공간 객체 데이터 구조를 제안한다. 제안된 구조는 공간 데이터와 속성 데이터를 효율적으로 연결시키며 공간 위상 관계를 유지하고 관리한다. 이는 도한 레이어 구조에서 관리되는 데이터에 의하여 공간 객체 간의 일관성을 유지시킨다.

• Journal of KIISE
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• v.44 no.9
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• pp.966-975
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• 2017

In Internet of Things (IoT), the aim of the nodes (called 'Things') is to exchange information with each other, whereby they gather and share information with each other through self decision-making. Therefore, we cannot apply existing aggregation algorithms of Wireless sensor networks that aim to transmit information to only a sink node or a central server, directly to the IoT environment. In addition, since existing algorithms aggregate information from all sensor nodes, problems can arise including an increasing number of transmissions and increasing transmission delay and energy consumption. In this paper, we propose the clustering and property based data exchange method for energy efficient information sharing. First, the proposed method assigns the properties of each node, including the sensing data and unique resource. The property determines whether the node can respond to the query requested from the other node. Second, a cluster network is constructed considering the location and energy consumption. Finally, the nodes communicate with each other efficiently using the properties. For the performance evaluation, TOSSIM was used to measure the network lifetime and average energy consumption.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.185-190
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• 2022

Open data has a lot of economic value. Not only Korea, but many other countries are doing their best to make various policies and efforts to expand and utilize open data. However, although Korea has a large amount of data, the data is not utilized effectively. Thus, attempts to utilize those data should be made in various industries. In particular, in the fashion industry, exchange and refund problems are the most common due to unpredictable consumers. Better feedback is necessary for service providers to solve this problem. We want to solve it by showing improved images of dissatisfactions along with user reviews including consumer needs. In this paper, user reviews are analyzed on online shopping mall websites to identify consumer needs, and product attributes are defined by utilizing the attributes of K-fashion data. The users' request is defined as a dissatisfaction attribute, and labeling data with the corresponding attribute is searched. The users' request is provided to the service provider in forms of text data or attributes, as well as an image to help improve the product.

• Journal of the Korean Society for information Management
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• v.22 no.4 s.58
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• pp.97-109
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• 2005

To build the consistency among different metadata systems and to increase the interoperability of that systems even among different domains, naming rules and glossaries for the data elements are necessary. This study provides discussion of naming and identification of the data element concept, data element, conceptual domain, value domain, and its meta model. This study also describes example naming conventions based on ontology derived from the combination with object, properties, and representation of data elements. The naming principles and rules described in this study use I-R analysis, DC metadata set, and SHOE 1.0 as an example of the scientific documents. This study would be a guideline to build the naming rules of metadata based on ontology in various domains.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.13-16
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• 2024

온라인상에 인간의 감성을 담은 리뷰 데이터가 꾸준히 축적되어왔다. 이 텍스트 데이터를 분석하고 활용하는 일은 마케팅에 있어서 중요한 자산이 될 것이다. 이와 관련된 Aspect-Based Sentiment Analysis(ABSA) 연구는 한글에 있어서는 데이터 부족을 이유로 거의 선행연구가 없는 실정이다. 본 연구에서는 최근 공개된 데이터 셋을 바탕으로 하여 화장품 도메인에 대한 소비자들의 리뷰 텍스트와 사전 라벨링 된 속성, 감성 극성을 기반으로 ABSA를 진행한다. Klue RoBERTa base 모델을 활용하여 데이터를 학습시키고, Python Kiwipiepy 등으로 전처리한 결과를 대시보드로 시각화하여 분석하기 쉬운 환경을 마련하는 방법을 제시한다.

• Journal of KIISE
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• v.43 no.8
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• pp.933-945
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• 2016

Sharing data by multiple users on the public storage, e.g., the cloud, is considered to be efficient because the cloud provides on-demand computing service at anytime and anywhere. Secure data sharing is achieved by fine-grained access control. Existing symmetric and public key encryption schemes are not suitable for secure data sharing because they support 1-to-1 relationship between a ciphertext and a secret key. Attribute based encryption supports fine-grained access control, however it incurs linearly increasing ciphertexts as the number of attributes increases. Additionally, the decryption process has high computational cost so that it is not applicable in case of resource-constrained environments. In this study, we propose an efficient attribute-based secure data sharing scheme with outsourceable decryption. The proposed scheme guarantees constant-size ciphertexts irrespective of the number of attributes. In case of static attributes, the computation cost to the user is reduced by delegating approximately 95.3% of decryption operations to the more powerful storage systems, whereas 72.3% of decryption operations are outsourced in terms of dynamic attributes.