• Journal of the Korea Academia-Industrial cooperation Society
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• v.6 no.3
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• pp.265-269
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• 2005

A fuzzy query language, called FSQL, in the relational databases is introduced in this paper. In general, database systems have query systems which are able to retrieve and manipulate precise data. However, such queries are hard to operate on the real world applications since their queries are often imprecise or incomplete. Recently, considerable attention has been given to research dealing with vagueness of the query in relational database systems. In this paper we have suggested an effective method of accepting vagueness of the query in data processing. The syntax of FSQL is formally defined with EBNF, and an interpreter of FSQL has been implemented as a prototype.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.11-20
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• 2010

XML view is proposed to integrate between XML data and heterogeneous data over distributed environment and global XML view is used to search distributed heterogeneous data. At this time, standard query language for user is XQuery and the method for processing global XQuery queries over distributed environment is one of the new research topics. One of the basic and simple methods to process distributed SQL queries is that generates local queries for processing a global query and constructs the result of the global query from the results of the local queries. However, the syntax of XQuery differs from SQL because the XQuery contains some special expressions like FOR clauses for querying to semi-structured data, of course, FOR clauses are not used in SQL. Therefore, there are some problems to adopt the method for processing global SQL queries for generating local XQuery queries. This paper defines some problems when generates local XQuery queries for processing global XQuery queries and proposes a method for generating local XQuery queries considered these problems. Also we implement and evaluate a Global XQuery Processor which uses our method.

• 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)

• 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).

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.49-52
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• 2003

In this research, we propose a mechanism to develop an inference engine and expert systems based on relational database and SQL (structured query language). Generally, former researchers had tried to develop an expert systems based on text-oriented knowledge base and backward/forward (chaining) inference engine. In these researches, however, the speed of inference was remained as a tackling point in the development of agile expert systems. Especially, the forward inference needs more times than backward inference. In addition, the size of knowledge base, complicate knowledge expression method, expansibility of knowledge base, and hierarchies among rules are the critical limitations to develop an expert systems. To overcome the limitations in speed of inference and expansibility of knowledge base, we proposed a relational database-oriented knowledge base and forward inference engine. Therefore, our proposed mechanism could manipulate the huge size of knowledge base efficiently, and inference with the large scaled knowledge base in a short time. To this purpose, we designed and developed an SQL-based forward inference engine using relational database. In the implementation process, we also developed a prototype expert system and presented a real-world validation data set collected from medical diagnosis field.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.278-283
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• 2008

As the usefulness of internet is kept changing more productively with web 1.0, web 2.0 usage of XML is also increasing very rapidly. In XML environment the most critical function is the ability of effective retrieval of useful information from XML repository. That makes the W3C XQuery more popular XQuery has very complicated structure as a query language due to the semi_structured nature of XML. FLOWOR, which stand for, let. where, order by, return, is the most commonly used expression in XQuery. In this paper we suggest the methods to handle XQuery FLWOR on relational environments. We also analyze and evaluate our approach to prove its correctness.

• 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 Korea Contents Association Conference
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• 2004.11a
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• pp.442-447
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• 2004

XML is normalized text form that is designed to transmit structured document in web as that propose in W3C (World Wide Web Consortium) in 1996. Function that this can overcome HTML's limit that use in existing in Internet and user define new tag to HTML by way to solve SGML's complexity added. There is many efforts to use storing this XML document in RDBMS but to relation style DB because XML document is tree structure structurally data SQL and perfect disaster caused by things that is language to ask a question accomplish XQuery that so it is W3C's XML standard query appear. After store XML informations including space information to RDBMS in this paper, Spatial XQuery through converter that is Sqatial XQuery2SQL through Spatial operator, Spatial function SQL of by Sqatial XQuery2SQL conversion algorithm that draw information in RDBMS after change embody wish to.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.2
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• pp.94-99
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• 2016

Application layer attacks have for years posed an ever-serious threat to network security, since they always come after a technically legitimate connection has been established. In recent years, cyber criminals have turned to fully exploiting the web as a medium of communication to launch a variety of forbidden or illicit activities by spreading malicious automated software (auto-ware) such as adware, spyware, or bots. When this malicious auto-ware infects a network, it will act like a robot, mimic normal behavior of web access, and bypass the network firewall or intrusion detection system. Besides that, in a private and large network, with huge Hypertext Transfer Protocol (HTTP) traffic generated each day, communication behavior identification and classification of auto-ware is a challenge. In this paper, based on a previous study, analysis of auto-ware communication behavior, and with the addition of new features, a method for classification of HTTP auto-ware communication is proposed. For that, a Not Only Structured Query Language (NoSQL) database is applied to handle large volumes of unstructured HTTP requests captured every day. The method is tested with real HTTP traffic data collected through a proxy server of a private network, providing good results in the classification and detection of suspicious auto-ware web access.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.743-748
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• 2003

In this research, we propose a mechanism to develop an inference engine and expert systems based on relational database (RDB) and SQL (structured query language). Generally, former researchers had tried to develop an expert systems based on text-oriented knowledge base and backward/forward (chaining) inference engine. In these researches, however, the speed of inference was remained as a tackling point in the development of agile expert systems. Especially, the forward inference needs more times than backward inference. In addition, the size of knowledge base, complicate knowledge expression method, expansibility of knowledge base, and hierarchies among rules are the critical limitations to develop an expert system. To overcome the limitations in speed of inference and expansibility of knowledge base, we proposed a relational database-oriented knowledge base and forward inference engine. Therefore, our proposed mechanism could manipulate the huge size of knowledge base efficiently. and inference with the large scaled knowledge base in a short time. To this purpose, we designed and developed an SQL-based forward inference engine using relational database. In the implementation process, we also developed a prototype expert system and presented a real-world validation data set collected from medical diagnosis field.