• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.433-437
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• 2005

Most research organizations, universities and enterprises manage the transaction informations (customer informations, account informations, transaction results and so on), which is a fundamental item in the part of buying and research capital expenditure for the execution of R&D project or relative works. But because they don't only manage transaction information systematically but also don't put to practical use as sharing knowledge, many researchers duplicate their operations and it is shown a drop in efficiency. Also there are many problems because useful informations are unapproachable in the side of research management. In this paper, we propose the database design, application plan and service method for joint utilize of transaction information. And we prove the proposed method by service which is operated within intranet system. The transaction information shared by the proposed method will be useful knowledge and a major factor of efficiency improvement in research management field.

• Journal of KIISE:Databases
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• v.35 no.6
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• pp.469-480
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• 2008

A shared disk (SD) cluster couples multiple nodes for high performance transaction processing, and all the coupled nodes share a common database at the disk level. To reduce the number of disk accesses, each node caches database pages in its memory buffer. Since a particular page may be cached simultaneously in different nodes, cache consistency should be maintained to ensure that nodes can always access the most recent version of database pages. Most cache consistency schemes proposed in the SD cluster adopted LRU as a buffer replacement algorithm. In this paper, we first present four buffer replacement algorithms that consider the characteristics of the SD cluster. Then we compare the performance of the buffer replacement algorithms. We perform the experiments on a variety of cluster configurations and database workloads. The experiment results show that the proposed algorithms achieve performance improvement up to 5 times of LRU algorithm.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.172-178
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• 1996

Databases are critical to business information systems, and RDBMS is most widely usded for the database system. Normalization has been designed to control various anomalies(insert, update, and delete anomalies). However, normalized databese design does not account for the tradeoffs necessary for the performance. In this research, we develop a model for database desin by denormalization of duplicating attributes in order to reduce frequent join processes. In this model, we consider insert, update, and delete costs. The anomalies are treated by additional disk I/O which is necessary for each insert and update transaction. We propose a branch and bound method for this model, and show considerable cost reduction.

• Asia pacific journal of information systems
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• v.6 no.1
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• pp.165-194
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• 1996

Data distribution is one of the most important steps in distributed database design. This paper proposes a methodology for distributing database by using a relational schema as an input. A design support system called DBDD (Database Distribution Design) is implemented within the framework of this methodology. The methodology consists of three phases such as (i) schema analysis, (ii) fragmentation, and (iii) allocation. In the schema analysis phase, all the table names are acquired from a global relational schema. In the fragmentation phase, fragments are generated according to transaction retrieval patterns. Furthermore, DBDD enhances the design quality by allocating fragments in a progressive manner. A real-life case is illustrated to demonstrate the practical usefulness of the DBDD.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.101-112
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• 1998

Data mining, which is also referred to as knowledge discovery in database, means a process of nontrivial extraction of implicit, previously unknown and potentially useful information (such as knowledge rules, constraints, regularities) from data in databases. The discovered knowledge can be applied to information management, decision making, and many other applications. In this paper, a new data mining problem, discovering sequential patterns, is proposed which is to find all sequential patterns using sampling method. Recognizing that the quantity of database is growing exponentially and transaction database is frequently updated, sampling method is a fast algorithm reducing time and cost while extracting the trend of customer behavior. This method analyzes the fraction of database but can in general lead to results of a very high degree of accuracy. The relaxation factor, as well as the sample size, can be properly adjusted so as to improve the result accuracy while minimizing the corresponding execution time. The superiority of the proposed algorithm will be shown through analyzing accuracy and efficiency by comparing with Apriori All algorithm.

• Proceedings of the Korea Database Society Conference
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• 2002.10a
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• pp.294-320
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• 2002

Client: Open된 Page기반 사용자 인터페이스, Page단위 Transaction. 사용자 인터페이스에 Form과 데이터가 혼합되어 있어 재사용 어려움. Server: 서버 페이지에 복잡성 집중(60~70%)에 따른 개발비용 상승 및 오류 증가. 서버 페이지 프로그램의 경직성으로 인한 유지보수 비용의 증가. 안정적인 퍼포먼스와 보안 수준 달성 미흡 (중략)

• Journal of Internet Computing and Services
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• v.15 no.4
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• pp.125-133
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• 2014

The column-oriented database storage is a very advanced model for large-volume data analysis systems because of its superior I/O performance. Traditional data storages exploit row-oriented storage where the attributes of a record are placed contiguously in hard disk for fast write operations. However, for search-mostly datawarehouse systems, column-oriented storage has become a more proper model because of its superior read performance. Recently, solid state drive using MLC flash memory is largely recognized as the preferred storage media for high-speed data analysis systems. The features of non-volatility, low power consumption, and fast access time for read operations are sufficient grounds to support flash memory as major storage components of modern database servers. However, we need to improve traditional transaction management scheme due to the relatively slow characteristics of column compression and flash operation as compared to RAM memory. In this research, we propose a new scheme called Column-aware Multi-Version Locking (CaMVL) scheme for efficient transaction processing. CaMVL improves transaction performance by using compression lock and multi version reads for efficiently handling slow flash write/erase operation in lock management process. We also propose a simulation model to show the performance of CaMVL. Based on the results of the performance evaluation, we conclude that CaMVL scheme outperforms the traditional scheme.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.803-816
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• 1996

It is possible to prevent deadlock if the priority which is conventionally given to transactions is endowed to data items and the transactions with the highest priority among transactions accessing the data item is allowed to proceed. Based on this observation, we proposed a Data-Priority Based Locking Protocol(DPLP) and evaluated its performance against known locking protocols in Real Time Database Management System(RTDBMS). Transaction inter-arrival time(IAT) was varied to determine success ratio, average-lateness, confict ratio and restart ratio, In these simulations we verified that the proposed DPLP performs better than the other protocols.

• Journal of Korea Multimedia Society
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• v.14 no.10
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• pp.1323-1334
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• 2011

This paper suggests the border based HSFI algorithm to hide sensitive frequent itemsets. Node formation of FP-Tree which is different from the previous one uses the border to minimize the impacts of nonsensitive frequent itemsets in hiding process, including the organization of sensitive and border information, and all transaction as well. As a result of applying HSFI algorithms, it is possible to be the example transaction database, by significantly reducing the lost items, it turns out that HSFI algorithm is more effective than the existing algorithm for maintaining the quality of more improved database.

• Advances in environmental research
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• v.12 no.2
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• pp.113-122
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• 2023

Biometric authentication has become an essential part of modern-day security systems, especially in financial institutions like banks. A face recognition-based ATM is a biometric authentication system, that uses facial recognition technology to verify the identity of bank account holders during ATM transactions. This technology offers a secure and convenient alternative to traditional ATM transactions that rely on PIN numbers for verification. The proposed system captures users' pictures and compares it with the stored image in the bank's database to authenticate the transaction. The technology also offers additional benefits such as reducing the risk of fraud and theft, as well as speeding up the transaction process. However, privacy and data security concerns remain, and it is important for the banking sector to instrument solid security actions to protect customers' personal information. The proposed system consists of two stages: the first stage captures the user's facial image using a camera and performs pre-processing, including face detection and alignment. In the second stage, machine learning algorithms compare the pre-processed image with the stored image in the database. The results demonstrate the feasibility and effectiveness of using face recognition for ATM authentication, which can enhance the security of ATMs and reduce the risk of fraud.