• 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.

• Journal of the Korean Society for information Management
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• v.32 no.2
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• pp.131-152
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• 2015

This study described the results of converting RDB to RDF ontology by each of R2RML method and Non-R2RML method. This study measured the size of the converted data, the conversion time per each tuple, and the response speed to queries. The STNet, a structured terminology dictionary based on RDB, was served as a test bed for converting to RDF ontology. As a result of the converted data size, Non-R2RML method appeared to be superior to R2RML method on the number of converted triples, including its expressive diversity. For the conversion time per each tuple, Non-R2RML was a little bit more faster than R2RML, but, for the response speed to queries, both methods showed similar response speed and stable performance since more than 300 numbers of queries. On comprehensive examination it is evaluated that Non-R2RML is the more appropriate to convert the dynamic RDB system, such as the STNet in which new data are steadily accumulated, data transformation very often occurred, and relationships between data continuously changed.

• 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.

• 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 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.

• The Journal of Society for e-Business Studies
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• v.8 no.3
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• pp.1-16
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• 2003

XML(Extensible Markup Language) is an emerging standard for data representation and exchange in e-commerce and internet-based information. However, to realize this potential, it is necessary to be able to extract structured data from XML documents and store it in a database, as well as to generate XML documents from data extracted from a database. Although many DBMS vendors are scrambling to extend their products to handle XML, there is a need for a lightweight, DBMS and platform-independent XML middleware as well. In this paper we describe such a XML2RDB middleware, that solves the following problems . generating relational schema from XML DTDs for storage of XML documents, importing data from XML documents into relational tables, creating XML documents according to a XMLQL(XML Query Language) from data extracted from a database.

• Journal of the Korea Society for Simulation
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• v.15 no.3
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• pp.115-124
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• 2006

XML is rapidly becoming technologies for information exchange and representation. It causes many research issues such as semantic modeling methods, security, conversion far interoperability with other models, and so on. Especially, the most important issue for its practical application is how to achieve the interoperability between XML model and relational model. Until now, many suggestions have been proposed to achieve it. However several problems still remain. Most of all, the exiting methods do not consider implicit referential integrity relations, and it causes incorrect data delivery. One method to do this has been proposed with the restriction where one semantic is defined as only one same name in a given database. In real database world, this restriction cannot provide the application and extensibility. This paper proposes a noble conversion (RDB-to-XML) algorithm based on the similarity checking technique. The key point of our method is how to find implicit referential integrity relations between different field names presenting one same semantic. To resolve it, we define an enhanced implicity referentiai integrity relations extraction algorithm based on a widely used ontology, WordNet. The proposed conversion algorithm is more practical than the previous-similar approach.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.113-116
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• 2001

XML (extensible Markup Language) is a flexible way to create common information formats and share both the format and the data on the World Wide Web, intranets, and elsewhere. A document type definition (DTD) is a specific definition of the rules of the Standard Generalized Markup Language. A relational database management system (RDBMS) is a program that lets you create, update, and administer a relational database. An RDBMS takes Structured Query Language (SQL) statements entered by a user or contained in an application program and creates, updates, or provides access to the database. This paper has been studied a method of mappings from XML DTD to RDB schemas based on object model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.271-275
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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)

• 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.