• The KIPS Transactions:PartD
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• v.13D no.1 s.104
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• pp.1-10
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• 2006

This paper introduces the development XPERT(XML Query Processing Engine using Relational Technologies) which is based on relational model. In our system we have used a decomposed approach to store XML files in relational tables. XML queries are translated to SQLs according to the table schema, and then they are sent to the relational DBMS to get the results back. Our translation scheme produces AST(Abstract Syntax Tree) by analyzing XQuery expressions at first. And on traversing AST proper SQLs are generated. Translated SQLs can reduce the number of joins by using path information and utilize dewey number to preserve document originated orderings among compoments in XML. In addition we propose the efficient algorithms of XPath and XQuery translation. And finally we show the implementation of our prototype system for the functional evaluations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.645-651
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• 2019

At a time when large amounts of data are being poured out, there are many changes in software architecture or data storage patterns because of the nature of the data being written, rather more read-intensive than writing. Accordingly, in this paper, the query model of Command Query Responsibility Segmentation (CQRS) pattern separating the responsibilities of commands and queries is used to implement an efficient high-capacity data lookup system in users' requirements. This paper uses the 2018 temperature, humidity and precipitation data of the Korea Meteorological Administration Open API to store about 2.3 billion data suitable for RDBMS (PostgreSQL) and NoSQL (MongoDB). It also compares and analyzes the performance of systems with CQRS pattern applied from the perspective of the web server (Web Server) implemented and systems without CQRS pattern, the storage structure performance of each database, and the performance corresponding to the data processing characteristics.

• Convergence Security Journal
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• v.8 no.4
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• pp.173-181
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• 2008

Recently, Worm epidemic models have been developed in response to the cyber threats posed by worms in order to analyze their propagation and predict their spread. Some of the most important ones involve mathematical model techniques such as Epidemic(SI), KM (Kermack-MeKendrick), Two-Factor and AAWP(Analytical Active Worm Propagation). However, most models have several inherent limitations. For instance, they target worms that employ random scanning in the network such as CodeRed worm and it was able to be applied to the specified threats. Therefore, we propose the probabilistic of worm propagation based on the Markov Chain, which can be applied to cyber threats such as Mass SQL Injection worm. Using the proposed method in this paper, we can predict the occurrence probability and occurrence frequency for each threats in the entire system.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.262-264
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• 2002

데이터웨어하우스는 대용량의 데이터를 대상으로 복잡한 분석을 수행하거나 전략적 의사 결정을 위해 사용되기 때문에 신속한 질의 응답을 위한 성능 관리가 무엇보다 중요하다. 데이터웨어하우스는 일반 운영계 시스템과는 다른 목적과 특성을 가지기 때문에 그에 적합한 성능 모니터링 방법이 필요하다. 본 연구에서는 오라클 데이터베이스를 위한 OLTP(On-Line Transaction Processing)용 국산 성능 모니터링 도구인 DBMax를 데이터웨어하우스 환경에 적용할 수 있도록 기능을 확장한다. 우선 기존의 운영계 시스템과는 구분되는 데이터웨어하우스의 특성을 토대로 오라클9i에서 제공하고 있는 성능 항상 기법인 실체화된 뷰(MV: Materialized View)와 EH(Data Extraction, Transformation and Loading) 화장 기능을 모니터링하기 위한 요구사항을 분석하고 이를 지원하기 위한 DBMax 확장 방안을 설계 및 구현한다. 실체화된 뷰와 ETL 확장 기능을 위해 제공되는 오라클9i의 다양한 스키마 객체에 대한 정보 및 성능 관련 지표를 제시하여 SQL 튜닝 기능을 지원하며 사후 분석을 위한 DBMax의 로그 파일에서 의미 있는 SQL문을 추출하여 잠재적으로 유용한 실체화된 뷰를 권고할 수 있도록 기능을 확장한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1106-1109
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• 2015

빅데이터 시대가 도래함에 따라, 방대한 양의 데이터 분석을 위한 데이터베이스 관리시스템의 중요성이 높아지고 있다. 일반 사용자는, 대표적인 데이터베이스 질의 문법인 SQL을 알지 못하기 때문에, GUI 기반 DB 액세스 툴을 사용해야 한다. 그러나 현재 개발된 GUI 기반 데이터베이스 관리 시스템은 제한적인 기능만을 제공하며, 복잡한 질의를 위해서는 사용자가 SQL 기반의 질의를 직접 입력해야하는 문제점이 존재한다. 따라서 본 논문에서는, 데이터 분석을 위한 다양한 질의를 지원하는 GUI 기반 DB 액세스 인터페이스를 제안한다.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.564-571
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• 2007

One of the methods for integrating XML data in distributed environments is using XML view. User can query toward distributed local XML views by using global XQuery queries in XQuery which is a standard query language for searching XML data. The global XQuery queries naturally contain join operations because of integrating and searching distributed heterogeneous data. Since join operations are generally expensive for processing a query, its processing technique is very important for efficient processing of global XQuery queries. Therefore there are some studies on the efficient processing of join operations and one of these studies is that selects minimum join cost by estimating a join selectivity. In case of SQL, there are already some researches for estimating a join selectivity and join cost of global SQL queries. However we can not apply their methods for estimating the selectivity of join operations in SQL queries into XQuery queries because of the structural difference between relational data and XML data. Therefore this paper proposes a method for estimating a selectivity of join operations in XQuery queries using the information of XML views. Our contribution is three threefold. First, we define the difference point for estimating join selectivity between SQL and XQuery. Second, we estimate join selectivity in XQuery queries by referring XML views. Third, we evaluate our estimating method.

• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.198-208
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• 2021

ArangoDB is a NoSQL database system that has been popularly utilized in many applications for storing large amounts of data. In order to apply a new NoSQL database system such as ArangoDB, to real work environments we need a benchmarking system that can evaluate its performance. In this paper, we design and implement a ArangoDB based benchmarking system that measures a kernel level performance well as an application level performance. We partially modify YCSB to measure the performance of a NoSQL database system in the cluster environment. We also define three real-world workload types by analyzing the existing materials. We prove the feasibility of the proposed system through the benchmarking of three workload types. We derive available workloads in ArangoDB and show that performance at the kernel layer as well as the application layer can be visualized through benchmarking of three workload types. It is expected that applicability and risk reviews will be possible through benchmarking of this system in environments that need to transfer data from the existing database engine to ArangoDB.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.407-416
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• 2003

As the usage of database systems dramatically increases and the amount of data pouring into them is massive, the performance administration techniques for using database systems effectively are getting more important. Especially in data warehouses, the performance management is much more significant mainly because of large volume of data and complex queries. The objectives and characteristics of data warehouses are different from those of other operational systems so adequate techniques for performance monitoring and tuning are needed. In this paper we extend functionalities of the DBMax, a performance administration tool for Oracle database systems, to apply it to data warehouse systems. First we analyze requirements based on summary management and ETL functions they are supported for data warehouse performance improvement in Oracle 9i. Then, we design architecture for extending DBMax functionalities and implement it. In specifics, we support SQL tuning by providing details of schema objects for summary management and ETL processes and statistics information. Also we provide new function that advises useful materialized views on workload extracted from DBMax log files and analyze usage of existing materialized views.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.5
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• pp.420-427
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• 2013

Recently, for secure software development, static analysis tools have been used mostly to analyze the source code of the software and identify software weaknesses caused of vulnerabilities. In order to select the optimal static analysis tool, both weaknesses rules and analysis capabilities of the tool are important factors. Therefore, in this paper we propose the test codes developed for evaluating the rules and analysis capabilities of the tools. The test codes to involve 43 weaknesses such as SQL injection etc. can be used to evaluate the adequacy of the rules and analysis capabilities of the tools.

• KIISE Transactions on Computing Practices
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• v.22 no.2
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• pp.77-82
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• 2016

This paper presents the development of the Korean medicine symptom diagnosis system. In the Korean medicine symptom diagnosis system, the patient explains their symptoms and an oriental doctor makes a diagnosis based on the symptoms. Natural language processing is required to make a diagnosis automatically through the patients' reports of symptoms. We use morphological analysis to get understandable information from the natural language itself. We developed a diagnosis system that consists of NoSQL document-oriented databases-MongoDB. NoSQL has better performance at unstructured and semi-structured data, rather than using Relational Databases. We collect patient symptom reports in MongoDB to refine difficult medical terminology and provide understandable terminology to patients.