• Proceedings of the Korean Society for Information Management Conference
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• 2004.08a
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• pp.209-212
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• 2004

이글은 인터넷기반 정보시스템에서 XML의 존재 층위를 나누고 각 층위에 적합한 XML과 RDB의 매핑 모델을 제안했다. 그리고 자료생성 층위에 주목하여 XML과 RDB의 매핑 모델을 설계하고 그 절차에 대해 논의했다. 이 연구에서 제안한 XML과 RDB 매핑 모델은 DTD 구조에 유연하며 플랫폼 독립적인 점에서 정보시스템 구축의 비용을 저렴하게 한다.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.15-22
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• 2020

The 4th industrial revolution and the rapid change in the data environment revealed technical limitations in the existing relational database(RDB). As a new analysis method for unstructured data in all fields such as IDC/finance/insurance, interest in graph database(GDB) technology is increasing. The graph database is an efficient technique for expressing interlocked data and analyzing associations in a wide range of networks. This study extended the existing RDB to the GDB model and applied machine learning algorithms (pattern recognition, clustering, path distance, core extraction) to detect new abnormal signs. As a result of the performance analysis, it was confirmed that the performance of abnormal behavior(about 180 times or more) was greatly improved, and that it was possible to extract an abnormal symptom pattern after 5 steps that could not be analyzed by RDB.

• Journal of Convergence for Information Technology
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• v.10 no.7
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• pp.41-48
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• 2020

Recently, the spreading business of AMI (Advanced Metering Infrastructure) remote metering systems in various regions of the country has been activated, and it provides various metering functions such as two-way communication and security plan functions for power demand management. Current AMI system is difficult to analyze based on the existing RDB(Relational Database) due to the increase in the size of new internal IoT devices and networks. This study proposes a new GDB(Graph Database) based failure analysis method that utilizes existing RDB data. It analyzes the correlation of new failure patterns through accumulated data such as internal thresholds and status values. As a result of GDB-based simulation, it was confirmed that RDB can predict to a new obstacle pattern that was difficult to analyze.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.353-359
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• 2005

In this research, we propose a Unified Fuzzy rule-based knowledge Inference Systems (UFIS) to help the expert in cosmetic brand detection. Users' preferred cosmetic product detection is very important in the level of CRM. To this purpose, many corporations trying to develop an efficient data mining tool. In this study, we develop a prototype fuzzy rule detection and inference system. The framework used in this development is mainly based on two different mechanisms such as fuzzy rule extraction and RDB (Relational DB)-based fuzzy rule inference. First, fuzzy clustering and fuzzy rule extraction deal with the presence of the knowledge in data base and its value is presented with a value between 0 -1. Second, RDB and SQL (Structured Query Language)-based fuzzy rule inference mechanism provide more flexibility in knowledge management than conventional non-fuzzy value-based KMS (Knowledge Management Systems).

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.23-34
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• 2020

In this paper, we proposes a method to automatically construct SHACL schemas for RDF knowledge graphs(KGs) generated by Direct Mapping(DM). DM and SHACL are all W3C recommendations. DM consists of rules to transform the data in an RDB into an RDF graph. SHACL is a language to describe and validate the structure of RDF graphs. The proposed method automatically translates the integrity constraints as well as the structure information in an RDB schema into SHACL. Thus, our SHACL schemas are able to check integrity instead of RDBMSs. This is a consideration to assure database consistency even when RDBs are served as virtual RDF KGs. We tested our results on 24 DM test cases, published by W3C. It was shown that they are effective in describing and validating RDF KGs.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.159-162
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• 2007

Semantic Structuralization of data, not Semantic Web base on Ontology but software engineering on many points, can be applied to trace of state or requirements usefully. But it is required much effort that a man converts RDB(Relational DataBase) into Ontology. Hence this study propose rules to convert RDB into Ontology description language OWL automatically.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.6
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• pp.1102-1106
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• 2015

To provide social security and for cooperative smart camera context awareness processing, each camera stores and exchange context data. For a specific event, measured values with other context data is stored RDB. RDB is transformed to ontology RDF file and is used for context reasoning. Interoperability between smart cameras conforms to ONVIF and constitutes intelligent surveillance system. To guarantee the confidentiality and integrity, securiy techniques are adopted. Security overhead between agents is analyzed in the prototype system implemented.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11c
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• pp.1485-1488
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• 2003

XML이 인터넷상에서 데이터의 교환 수단으로 널리 사용됨에 따라, 데이터를 처리하고 관리하는 다양한 데이터 모델의 DBMS에서 이를 수용하고 관리하는 도구를 제공하고 있다. XML이 데이터의 교환 수단으로 사용되는 가장 큰 이유는 자기 기술 문서화의 기능과 동적인 확장성을 가지고 있기 때문인데, 이를 통해 서로 다른 언어나 플랫폼에서 다른 형식의 데이터를 자신의 시스템의 맞게 변환할 수 있다. 문제는 XML 문서의 데이터를, 문서의 구조와 상관없이 자신이 사용하고 있는 데이터 모델로 저장하고 관리하려고 한다는 점이다. 이로 인해 데이터 관리상의 비용은 증가하고, DBMS의 성능은 저하된다. 본 논문에서는 XML 문서를 관리할 데이터 모델을 RDB와 ORDB로 한정하고, XML 문서가 지니는 구조적 특성에 따라 그에 맞는 데이터 모델을 제시한다. 고려되는 XML 문서의 구조적 특성은 평면 구조의 XML 문서와 계층 구조의 XML 문서이다.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.111-114
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• 2004

Management of human knowledge is an interesting concept that has attracted the attention of philosophers for thousands of years. Artificial intelligence and knowledge engineering has provided some degree of rigor to the study of knowledge systems and expert systems(ES) re able to use knowledge to solve the problems and answer questions. Therefore, the process of conceptualization and inference of knowledge are fundamental problem solving activities and hence, are essential activities for solving the problem of software ES construction Especially, the access to relevant, up-to-date and reliable knowledge is very important task in the daily work of physicians and nurses. In this study, we propose the conceptualization and inference mechanism for implicit knowledge management in medical diagnosis area. To this purpose, we combined the dynamic knowledge map(KM) and relational database(RDB) into a dynamic knowledge map(DKM). A graphical user-interface of DKM allows the conceptualization of the implicit knowledge of medical experts. After the conceptualization of implicit knowledge, we developed an RDB-based inference mechanism and prototype software ES to access and retrieve the implicit knowledge stored in RDB. Our proposed system allows the fast comfortable access to relevant knowledge fitting to the demands of the current task.