• Spatial Information Research
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• v.13 no.2 s.33
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• pp.119-128
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• 2005

Most spatial data handled in GIS is two-dimensional. These two-dimensional data is established by selecting 2D aspects form 3D, or by projecting 3D onto 2D space. During this conversion, without user's intention, data are abstracted and omitted. This unwanted data loss causes disadvantages such as restrictingof the range of data application and describing inaccurate real world. Recently, three dimensional data is getting wide interests and demands. One of the examplesis Database Management System which can store and manage three dimensional spatial data. However, this DBMS does not support spatial query which is the essence of the database management system. So, various studies are needed in this field. This research designs spatial relationship that is defined in space database standard using the three-dimension space model. The spatial data model, which is used in this research, is the one defined in OGC for GMS3, and designing tool is DE-9IM based on Point-Set Topology blow as the best method for topological operation.

• Spatial Information Research
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• v.19 no.1
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• pp.107-116
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• 2011

Due to development of environment of wireless Internet, location based services on the basis of spatial data have been increased such as real time traffic information as well as CNS(Car Navigation System) to provide mobile user with route guidance to the destination. However, the current application adopting the file-based system has limitation of managing and storing the huge amount of spatial data. In order to supplement this challenge, research which is capable of managing large amounts of spatial data based on embedded database system is surely demanded. For this reason, this study aims to suggest the lossless compression technique by using the hybrid approach of BytePacking and Lempel-Ziv which can be applicable in DBMS so as to save a mass spatial data efficiently. We apply the proposed compression technique to actual the Seoul and Inchcon metropolitan area and compared the existing method with suggested one using the same data through analyzing the query processing duration until the reconstruction. As a result of comparison, we have come to the conclusion that suggested technique is far more performance on spatial data demanding high location accuracy than the previous techniques.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.135-145
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• 2011

These days, the research of a sensor data management system for USN based real-time monitoring application is active thanks to the development and diffusion of sensor technology. The sensor data is rapidly changeable, continuous and massive row level data. However, end user is only interested in high level data. So, it is essential to effectively process the row level data which is changeable, continuous and massive. In this paper, we propose a sensor data management system with multi-analytical query function using OLAP and anomaly detection function using learning based classifier. In the experimental section, we show that our system is valid through the some experimental scenarios. For the this, we use a sensor data generator implemented by ourselves.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.2
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• pp.175-181
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• 2006

Wireless sensor network forms the self-organization network, and transfers the information among sensor nodes that have computing technology ability and wireless communication ability. The recent sensor network is study for low-power, micro, low cost of node is proceeded. Recently, the research of application services in wireless sensor networks is proceeded. Therefore, in this paper, we design the prototype of the real-time information service that support a user the information of temperature, illumination etc. And, we implement the alarm application service fer the disaster prevention on Internet base on IPv4/IPv6. We develop the module of the extract information using the query processing based on TinyOS, and the module of the server's database using MySQL data base management system and JDBC. Additionally, we develop the client module that receive the real-time sensing data using ODBC in Internet based on IPv4/IPv6.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.181-188
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• 2021

Research and importance of legal services applied with AI so that it can be easily understood and predictable in difficult legal fields is increasing. In this study, based on the decision of the Tax Tribunal in the field of tax law, a model was built through self-learning through information collection and data processing, and the prediction results were answered to the user's query and the accuracy was verified. The proposed model collects information on tax decisions and extracts useful data through web crawling, and generates word vectors by applying Word2Vec's Fast Text algorithm to the optimized output through NLP. 11,103 cases of information were collected and classified from 2017 to 2019, and verified with 70% accuracy. It can be useful in various legal systems and prior research to be more efficient application.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.179-186
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• 2021

We have been developed the smart tourism app and the Instagram and YouTube contents to provide personalized tourism information and travel product information to individual tourists. In this paper, we develop a rule-based smart tourism chatbot with the khaiii (Kakao Hangul Analyzer III) morphological analyzer and Neo4J graph database. In the proposed chatbot system, we use a morpheme analyzer, a proper noun dictionary including tourist destination names, and a general noun dictionary including containing frequently used words in tourist information search to understand the intention of the user's question. The tourism knowledge base built using the Neo4J graph database provides adequate answers to tourists' questions. In this paper, the nodes of Neo4J are Area based on tourist destination address, Contents with property of tourist information, and Service including service attribute data frequently used for search. A Neo4J query is created based on the result of analyzing the intention of a tourist's question with the property of nodes and relationships in Neo4J database. An answer to the question is made by searching in the tourism knowledge base. In this paper, we create the tourism knowledge base using more than 1300 Jeju tourism information used in the smart tourism app. We plan to develop a multilingual smart tour chatbot using the named entity recognition (NER), intention classification using conditional random field(CRF), and transfer learning using the pretrained language models.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.2
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• pp.31-38
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• 2009

To provide the privacy of a keyword, a public key encryption with keyword search(PEKS) firstly was propsed by Boneh et al. The PEKS scheme enables that an email sender sends an encrypted email with receiver's public key to an email server and a server can obtain the relation between the given encrypted email and an encrypted query generated by a receiver. In this email system, we easily consider the situation that a user sends the one identical encrypted email to multi-receiver like as group e-mail. Hwang and Lee proposed a searchable public key encryption considering multi-receivers. To reduce the size of transmission data and the server's computation is important issue in multi-receiver setting. In this paper, we propose an efficient searchable public key encryption for multi-receiver (mPEKS) which is more efficient and reduces the server's pairing computation.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.581-592
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• 2022

Recently, as the requirements for using big data have increased, interest in dataset search technology needed for data analysis is also growing. Although it is necessary to proactively utilize metadata, unlike conventional text search, research on such dataset search systems has not been actively carried out. In this paper, we propose a new dataset-tailored search system that indexes metadata of datasets and performs dataset search based on metadata indices. The ranking given to the dataset search results from a newly devised algorithm that reflects the unique characteristics of the dataset. The system provides the capability to search for additional datasets which correlate with the dataset searched by the user-submitted query so that multiple datasets needed for analysis can be found at once.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.33-43
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• 2022

Labeled graphs are used to represent entities, their relationships, and their structures in real data such as knowledge graphs and protein interactions. With the rapid development of IT and the explosive increase in data, there has been a need for a subgraph matching technology to provide information that the user is interested in. In this paper, we propose an approximate Top-k labeled subgraph matching scheme that considers the semantic similarity of labels and the difference in graph structure. The proposed scheme utilizes a learning model using FastText in order to consider the semantic similarity of a label. In addition, the label similarity graph(LSG) is used for approximate subgraph matching by calculating similarity values between labels in advance. Through the LSG, we can resolve the limitations of the existing schemes that subgraph expansion is possible only if the labels match exactly. It supports structural similarity for a query graph by performing searches up to 2-hop. Based on the similarity value, we provide k subgraph matching results. We conduct various performance evaluations in order to show the superiority of the proposed scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3182-3203
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• 2023

With the growing adoption of cloud-based technologies, maintaining the privacy and security of cloud data has become a pressing issue. Privacy-preserving encryption schemes are a promising approach for achieving cloud data security, but they require careful design and implementation to be effective. The integrated approach to cloud data security that we suggest in this work uses CogniGate: the orchestrated permissions protocol, index trees, blockchain key management, and unique Opacus encryption. Opacus encryption is a novel homomorphic encryption scheme that enables computation on encrypted data, making it a powerful tool for cloud data security. CogniGate Protocol enables more flexibility and control over access to cloud data by allowing for fine-grained limitations on access depending on user parameters. Index trees provide an efficient data structure for storing and retrieving encrypted data, while blockchain key management ensures the secure and decentralized storage of encryption keys. Performance evaluation focuses on key aspects, including computation cost for the data owner, computation cost for data sharers, the average time cost of index construction, query consumption for data providers, and time cost in key generation. The results highlight that the integrated approach safeguards cloud data while preserving privacy, maintaining usability, and demonstrating high performance. In addition, we explore the role of differential privacy in our integrated approach, showing how it can be used to further enhance privacy protection without compromising performance. We also discuss the key management challenges associated with our approach and propose a novel blockchain-based key management system that leverages smart contracts and consensus mechanisms to ensure the secure and decentralized storage of encryption keys.