Earticle

Internet accessibility has been growing due to the diffusion of smartphones in today's society. Therefore, people can generate data anywhere and are confronted with the challenge that they should process a large amount of data. Since the appearance of relational database management system (RDBMS), most of the recent information systems are built by utilizing it. RDBMS uses foreign-keys to avoid data duplication. The transactions in the database use attributes, such as atomicity, consistency, isolation, durability (ACID), which ensures that data integrity and processing results are stably managed. The characteristic of RDBMS is that there is high data reliability. However, this results in performance degradation. Meanwhile, from among these information systems, some systems only require high-performance rather than high reliability. In this case, if we only consider performance, the use of NoSQL provides many advantages. It is possible to reduce the maintenance cost of the information system that continues to increase in the use of open source software based NoSQL. And has a huge advantage that is easy to use NoSQL. Therefore, in this study, we prove that the leverage of NoSQL will ensure high performance than RDBMS by applying NoSQL to database systems that implement RDBMS.

Engineering or humanities data are stored in databases and are often used for search services. While the latest deep-learning technologies, such like BART and BERT, are utilized for data analysis, humanities data still rely on traditional databases. Representative analysis methods include n-gram and lexical statistical extraction. However, when using a database, performance limitation is often imposed on the result calculations. This study presents an experimental process using MariaDB on a PC, which is easily accessible in a laboratory, to analyze the impact of the database on data analysis performance. The findings highlight the fact that the database becomes a bottleneck when analyzing large-scale text data, particularly over hundreds of thousands of records. To address this issue, a method was proposed to provide real-time humanities data analysis web services by leveraging the open source database, with a focus on the Seungjeongwon-Ilgy, one of the largest datasets in the humanities fields.

This study evaluates the performance characteristics of five OCI compliant container runtimes—runc, crun, youki, gVisor (runsc), and Kata Containers—through CPU, memory, and database benchmarks executed on an identical host environment. Sysbench CPU tests show that runc, crun, youki, and gVisor deliver nearly identical performance, while Kata Containers exhibits significantly lower throughput due to its virtualization-based architecture. Memory and PostgreSQL pgbench results highlight clearer differences: native runtimes achieve the highest throughput and lowest latency, gVisor shows moderate degradation due to system call mediation in user space, and Kata Containers demonstrates the greatest performance loss because of guest-to-host transitions and virtio based processing. Overall, the findings provide practical guidance for selecting container runtimes based on workload requirements, showing that native runtimes are optimal for latency-sensitive database services, while gVisor and Kata Containers are suitable for environments prioritizing stronger isolation at the cost of reduced performance.

By analyzing the development of performance tourism commodities, performance tourism facilities, and the analysis of overseas tourists, this paper understands the problems and sustainability of Korean wave performance tourism. The results show that the Korean idols have a positive attraction to the fans. They love the Korean wave content and will actively come to South Korea in the case of sufficient cost, time, and performance travel information. Tourism marketers need to analyze the aesthetic details of fans in various countries to attract their attention to the performance travel information. Based on Korean traditional beauty, works dedicated to creating famous art products also need to communicate with overseas professional audiences, expand the influence of Korean performing arts, let potential overseas tourists recognize more excellent performance works, and let potential tourists identify that performing arts are also tourism products.

본 연구는 민속춤을 본래의 연행맥락에 위치ㆍ연결시킨 데이터베이스 체계 구축 을 위한 분류 체계도를 만들기 위해 마련되었다. 연구자는 1차로 민속춤을 무속춤, 농 악춤, 탈춤, 소리춤, 허튼춤 및 모방춤 총 5가지 종목으로 나누어 각각의 민속춤에 대 한 연행맥락을 연결하고자 했다. 민속춤들은 저마다 다른 성격을 가지고 연행되었기 때문에 연행맥락의 기반 또한 이에 맞게 설정해주어야 하는데, 무속춤은 연행성격ㆍ농악춤은 연행시기ㆍ탈춤은 연행주제ㆍ소리춤은 연행시기 및 연희자ㆍ허튼춤 및 모방춤 은 연행지역 및 연희자로 맥락의 출발 지점을 설정하였다. 2차로는 춤과 좀 더 직접적 인 세부 요소(춤 설명, 무구(舞具), 무복(舞服), 무악(舞樂))를 연결했다. 이로써 각각 의 민속춤이 본래 어떤 목적에서 연행하게 된 것인지 알 수 있고, 이후 하위맥락으로 연결된 춤 또한 예술적ㆍ기능적인 측면 중심보다는 본래의 연행 의도와 역할 및 의미 를 감지하면서 파악할 수 있게 되었다고 본다. 또한 같은 종목 간의 춤 비교를 쉽게 할 수 있어 민속춤의 지역적 특성도 이해하기 편리해졌다고 할 수 있다.

This study seeks to make a classification chart due to build a database in which folk dance is placed and connected in its original Performance context. Firstly, the researcher divided folk dance into five events, including shamanism, Nongak (Instrumental music of peasants), mask dance, Sori dance, Heoteun dance and cover dance, to connect their respective feast context to folk dance. Since each of folk dances was performed in different characters, it is important to set the basis of Performance context accordingly. So shamanistic dance was set for feast character, Nongak dance for its period of time, mask dance for its theme, Sori dance for Performance period of time and performer, and finally, Heoteun dance and cover dance for Performance region and performer, as to the starting point of context. Secondly, dance was connected to more immediate, detailed elements (dance description, dancing tools, dancing costume and dance music. This would tell you about by what design each of folk dances originally became Performance and then would allow grasping dances connected in lower context by appreciating the original intent, role and meaning of Performance, not simply by focusing on artistic and functional aspects. Besides, this attempt would make it convenient to understand the regional characteristics of folk dances by enabling to compare between the same events of dance easily.

현재 병렬 데이타베이스 컴퓨터가 광범위하고 성공적으로 활용되고 있다. 이의 구조로는 주기억 장치와 디스크를 공유하지 않는 구조, 두가지를 모두 공유하는 구조, 디스크만을 공유하는 구조, 그리고 절충형 구조 등의 네가지 구조가 있다. 이 논문에서는 데이타베이스 컴퓨터 구조의 성능을 비교 분석하기 위하여 데이타베이스 컴퓨터 구조를 추상적인 모형으로 정의하고, 각각의 모형에 대하여 절충형 해쉬 조인 연산의 수행시간을 수식화한 성능식을 구하여 여러 가지 데이타베이스 컴퓨터 구조 모형의 수행시간을 비교 분석한다.

The parallel database machine approach is currently widely and successfully used. There are four major architectures which are used in this approach: shared-nothing architecture, shared-evertying architecture, shared-disk architecture, and hybrid architecture. In this paper, we use an analytical model to evaluate the performance of these database machine architectures. We define an abstract model for each type of database machine design to obtain performance equatons describing the execution times with respect to the hybrid hash join poeration. Using the performance equations, we evaluate the execution times of the various database machine design models.

데이터는 살아 움직이고 있다. 10년 전만 해도 10GB 정도의 데이터라면 대용량 데이터라고 불리던 시절이 있었다. 하지만 지금은 10TB보다 큰 데이터베이스도 흔하다. 결국, 대용량 데이터베이스(VLDB)의 시대가 개막된 것이다. VLDB로 변한 데이터베이스에는 백업, 복구, 관리와 같은 문제점이 있지만 그 중에서도 성능 문제를 빼놓을 수 없다. 데이터베이스에 많은 데이터가 있고 그렇게 많은 데이터 중에서 필요한 몇 건의 데이터만 추출하는 것이 쉬운 일이 아니다. 과거에는 데이터가 적었기 때문에 이러한 것이 큰 문제가 아니었지만 이제는 VLDB가 되면서 성능 최적화는 일상적이고도 중요한 이슈가 되었다. 따라서 VLDB가 된 데이터베이스나 VLDB로 변하고 있는 데이터베이스에서 성능 관리를 하고 최적화할 수 있는 전문 기술이 필요하다.

락은 멀티프로세서 환경에서 공유 데이터에 대한 접근을 안전하게 하는 잘 알려진 일반적인 방법이다. 1960년대에 상호 배제가 소개된 후에 많은 스핀락 알고리즘이 제안되었고 운영체제나 데이터베이스 시스템에 사용되어 왔다. 이 연구에서 고성능 멀티코어 시스템 상에서 락 알고리즘이 데이터베이스 시스템에 미치는 영향을 측정하였다. 평가를 위해 그 동안 멀티코어 상에서 성능 개선을 위해 재구조화된 최신 MySQL 5.6 및 MySQL에 탑재된 InnoDB 엔진을 사용하였다. InnoDB의 스핀락 함수를 수정하여 다양한 락 알고리즘들을 구현하였고 구현된 락 알고리즘들을 멀티코어 환경에서 평가하였다.

A lock is a general and popular way of serializing accesses to shared data in multiprocessor environments. After the mutual exclusion was first introduced in 1960s, many spinlock algorithms have been proposed and deployed to real systems such as operating systems and (transactional) database systems. In this study, we measure impacts of a lock mechanism on a database system under various CPU configurations using a high-end multicore system. For the evaluation, we use the most up-to-date version of MySQL (version 5.6) with InnoDB engine, which has been substantially re-architected to improve scalability on multicore machines. We changed the original spinlock function of InnoDB to evaluate various spinlock mechanisms on multicore machines.

메인 메모리 DBMS는 구동과 동시에 디스크에 존재하는 테이블의 내용이 메모리로 모두 올라가는 구조로 운영된다. 그러나, 메인메모리 DMBS도 디스크 파일시스템을 사용하여 데이터를 저장하고 트랜잭션 로그파일을 파일 시스템에 유지하여야 하기 때문에 CPU가 메모리에 접근하여 처리하는 속도에는 한계가 있게 마련이다. 본 논문에서는 메인메모리 DBMS인 Altibase 시스템에서 구현된 기술들이 디스크기반 DBMS인 Sybase와 얼마나 차이가 있는지를 어플리케이션 측면에서 성능분석을 통하여 평가하였다. 메인메모리 DBMS의 어플리케이션 성능이 디스크기반 DBMS에서의 어플리케이션 성능과 비교하면, 메인 메모리 DBMS의 성능이 축구승무패 단일게임에서는 1.24배 ~ 3.36배, 축구승무패와 축구 스페셜 두 게임에 동시 발매시 1.29배 ~ 7.9배의 성능향상이 있었으며, 발매 트랜잭션의 응답시간 시험결과 1.78배 ~ 6.09배의 빠른 응답시간을 보였다.

The main memory DBMS is operated which the contents of the table that resides on a disk at the same time as the drive is in the memory. However, because the main memory DBMS stores the data and transaction log file using the disk file system, there are a limit to the speed at which the CPU accesses the memory. In this paper, I evaluated the performance through analysis of the application side difference the technology that has been implemented in Altibase system of main memory DBMS and Sybase of disk-based DBMS. When the application performance of main memory DBMS is in comparison with the disk-based DBMS, the performance of main memory DBMS was outperformed 1.24~3.36 times in the single soccer game, and was outperformed 1.29~7.9 times in the soccer game / special soccer. The result of sale transaction response time showed a fast response time of 1.78 ~ 6.09 times.

데이터베이스는 중요한 정보를 저장·관리하는 핵심 IT 자산이며, 이를 보호하기 위한 대표적인 기술 중 하나가 암호화 기 술이다. 본 연구에서는 AES, RSA, 그리고 두 방식을 결합한 하이브리드 암호 시스템을 구현하고, 데이터베이스(MySQL, SQLite)와 데이터 유형(텍스트, 이미지)에 따른 암·복호화 성능을 비교 및 분석하였다. 특히, 텍스트 데이터셋(FIFA, Top5) 및 이미지 데이터셋(GTSRB, Chest_xray)을 사용하여 데이터 특성과 크기에 따른 성능 차이를 실험적으로 평가하였다. 실험 결 과, AES는 모든 조건에서 가장 짧은 처리 시간과 낮은 메모리 사용량을 보였으며, 하이브리드 방식은 중간 수준, RSA는 가장 낮은 성능을 나타냈다. 데이터 유형별로는 텍스트가 이미지보다 일관되게 빠르고 경량이었고, 데이터베이스별로는 SQLite가 MySQL보다 전반적으로 우수했다. 이러한 경향은 알고리즘 구조, 데이터 처리 과정의 복잡성, DBMS의 동작 방식 차이에 기 인한다. 본 연구의 성능 비교 결과는 데이터베이스 보안 기능 구현 시 효율적인 암호화 방식 선택을 위한 근거를 제공하며, 제 안한 하이브리드 방식은 보안성과 성능의 균형을 위한 실질적 참고 자료가 될 수 있다.

A database is a core IT asset for storing and managing critical information, and encryption is one of the representative technologies for its protection. This study implements AES, RSA, and a hybrid cryptosystem combining the two methods and compares and analyzes their encryption and decryption performance across different databases (MySQL and SQLite) and data types (text and image). In particular, the performance differences according to data characteristics and sizes were experimentally evaluated using text datasets (FIFA, Top5) and image datasets (GTSRB, Chest_xray). Experimental results show that AES consistently achieved the shortest processing time and lowest memory usage under all conditions, the hybrid approach demonstrated intermediate performance, and RSA exhibited the lowest performance. Text processing was consistently faster and lighter than image processing, whereas SQLite outperformed MySQL. These trends can be attributed to differences in the algorithm structure, complexity of data processing, and operational mechanisms of the DBMSs. The performance comparison results of this study provide a basis for selecting efficient encryption methods when implementing database security features, and the proposed hybrid approach can serve as a practical reference for balancing the security and performance.

SQLite는 모바일 장치, 내장형 장치 등 소형기기 또는 규모가 작은 환경에 적합하여 스마트 폰 앱, 내장형 소프트웨어 등에 널리 사용되는 오픈소스 데이터베이스 관리 시스템이다. 이 SQLite 는 소스 코드의 크기가 작아 소형 시스템에서도 잘 동작한다는 장점이 있지만, 반면에 제공하는 기 능에 여러 가지 제한이 있다는 문제점이 있다. 이런 제한 중의 하나로 SQLite은 트랜잭션을 데이터 베이스 단위로 동시성 제어를 하고 있어 여러 트랜잭션들이 함께 실행될 경우 성능이 낮아진다. 이 논문에서는 SQLite를 내장형 데이터베이스 시스템의 특성을 유지하면서도 트랜잭션의 동시성 제어 성능을 높이는 기능을 구현하였다. 이를 위해 락(lock) 관리 및 제어 작업에 관련된 오버헤드를 줄이 면서 동시성 제어를 할 수 있는 경량 락킹 기법(Very Lightweight Locking, VLL)을 활용하여 SQLite 데이터베이스 관리 시스템의 동시성제어 시스템을 개선하였다. 또 SQLite의 동세성 제어 단 위를 테이블 단위로 낮춰 트랜잭션의 동시성 제어 성능을 높이도록 구현하였다. 이 논문에서는 이렇 게 개발한 동시성 제어 기법의 구현 내용을 소개하고, 실험을 통해 구현한 동시성 제어 기법의 성능 을 비교.평가하였다.

SQLite is an open source database management system which has been widely used in smartphone apps and embedded software, because it is suitable for small-size environments such as mobile and embedded devices. While SQLite has the advantage running well on small embedded systems, it has several restrictions on its features. Of these restrictions, it uses the database unit as its locking granularity thus it suffers from poor performance when controling transactions concurrently. This paper implemented a new concurrency control mechanism of SQLite in order to provide with better performance of the concurrency control, with reserving the properties of embedded DBMSs. To achieve this goal, we adopt the Very Lightweight Locking (VLL) which can reduce the overhead of lock management and concurrent transaction control, and we extended the concurrency control method of SQLite based on the VLL method. We also implemented the concurrency control method with lesser locking granularity of the table unit in order to provide better performance. This paper describes the implementation details of the newly developed concurrency control mechanism and it also presents the performance evaluation of the method.

학술지 논문의 효율적인 관리 및 검색을 위한 주요 요소인 키워드는 통제키워드와 비통제키워드로 구분할 수 있다. 그러나 현재 국내 데이터베이스에서 대부분의 학술지 논문에는 비통제키워드인 저자키워드만이 부여되어 있을 뿐, 망라적인 탐색을 돕는 통제키워드로서 디스크립터는 제공되지 않고 있다. 이 연구에서는 해외 데이터베이스의 학술지 논문에 부여된 통제키워드를 학습한 분류기를 사용하여, 국내 학술지 논문에 디스크립터를 자동 할당하는 실험을 수행하였다. 그 결과, 국외 데이터베이스의 디스크립터 학습을 통해 영문 초록이 있는 국내 학술지 논문에 통제키워드를 자동 할당할 수 있는 가능성을 확인하였다. 또한, 다양한 분류기 및 분류기 결합을 통하여 이러한 디스크립터 자동 할당의 성능 향상을 모색하였다.

As a major factor for efficient management and retrieval of the articles in databases, keywords are classified into uncontrolled keywords and controlled keywords. Most of Korean scholarly databases fail to provide controlled vocabularies to indexing research articles which help users to retrieve relevant papers exhaustively. In this paper, we carried out automatic descriptor assignment experiments to Korean articles using automatic classifiers learned with descriptors in international database. The results of the experiments show that the classifier learning with descriptors in international database can potentially offer controlled vocabularies to Korean scholarly articles having English s. Also, we sought to improve the performance of automatic descriptor assignment using various classifiers and combination of them.

본 논문에서는 현재 운영되는 수강신청 시스템에서의 데이터베이스 성능을 향상시키기 위하여 SQL문 분석을 하였다. 수강신청 업무와 관련된 트랜잭션들에서 사용 중인 SQL문장들을 실행계획을 통하여 현행 데이터베이스 성능 을 측정하였으며, SQL 분석을 통하여 보완한 SQL문장들이 성능이 향상된 결과를 확인하였다. 전반적으로 실행계획 분석을 통하여 수강신청 데이터베이스 시스템의 성능을 향상시켰으며, 수강신청 SQL 의 일부 개선방법을 시험결과로 보였다. 개선된 방법은 데이터베이스 튜닝 작업을 통하여 수강신청과 관련된 테이블들과 인덱스 테이블들을 재조정하 고, SQL의 기능을 활용함으로서 성능이 향상된 수강신청 데이터베이스 시스템으로 진화한 최적화된 시스템을 구현할 수 있게 되었다. 제안된 방법으로 재조정된 수강신청 시스템은 이전에 운영하던 수강신청시스템에 비하여 성능적 측 면에서 우수한 결과를 나타내었으며, 통합 성능 시험 결과 1.8배 ~ 18배의 응답시간 단축을 가져왔다.

In this paper, SQL statements are analyzed to improve database performance in the current course registration system. The performance of the current database was measured through the execution plan of the SQL statements used in the transactions related to the course registration. Through the SQL analysis, the complemented SQL statements confirmed the improved performance. Overall, the performance of the course registration database system was improved through the analysis of the execution plan, and some improvement methods of the course registration SQL were shown as test results. The improved method is to reorganize the tables and index tables related to the course registration through database tuning, and utilize the SQL function to implement an optimized system that has evolved into a course database system with improved performance. The enrollment system re-adjusted by the proposed method showed excellent results in terms of performance compared to the previous enrollment system, and the integrated performance test result reduced the response time by 1.8 to 18 times.

With the ever-increasing complexity and variety of database workload, database application system has been imposed on higher and higher performance requirements. Database system consists of software and hardware. And the factors that affect database performance are uncertain. In order to tackle the issue of database system for SQL server, this paper proposes hierarchical queuing network model for performance prediction, and a model is established for both software resources and hardware resources, the nested resources are linearized by hierarchical calling, thus finding out the main factors for system performance bottleneck, and system performance is adjusted and optimized accordingly. The performance tuning algorithm for SQL server database based on the hierarchical queuing network is presented in detail. And TPC-C benchmark is adopted for simulation. Experimental results show the proposed method achieves 16.8% performance increase on average, and TPS is improved by 40% compared to previous method.

This paper is written for intelligent CCTV (Closed-Circuit Television) which has been important in the aspect of industrial value and social security these days. Because the intelligent CCTV can identify behavior and object automatically, and it has been used extensively. With spread use, the performance of the intelligent CCTV has been important. But a standard method to measure the performance of the intelligent CCTV is absence, therefore this paper is described about methods for implementing and measuring of the intelligent CCTV performance.

In this paper we have presented a loss-less compression technique namely H-HIBASE (compression enhancement of HIBASE technique using Huffman Coding). Due to disk based compression, H-HIBASE supports very large database with acceptable storage volume. Insertion, deletion and update mechanisms on the architecture have been presented and analyzed. The architecture executes query directly on compressed data and it is capable of executing all types of SQL queries. The experimental evaluation has been performed with synthetic and real data. The experimental result has been compared with DHIBASE and widely used Oracle database. We have evaluated the storage performance in comparison with DHIBASE and Oracle database. The storage performance that has been achieved in H-HIBASE is 25 to 40 percent better than the Oracle database for real and synthetic data. The query performance that has been achieved in H-HIBASE is 10 to 25 percent better than that of DHIBASE.

Cassandra database system is one of the universal databases. To achieve high performance, we should allocate memory space rationally according to actual demands. Otherwise, it will influence reading and writing performance. Actually, we always allocate memory space according to experience and repeated attempts which usually won’t give us the best answer. To solve this problem, firstly we analyze the reading and writing processing of the Cassandra database and find out the corresponding memory space which will influence system performance. Secondly, we build up a relationship model between system performance and memory allocation and name it as The Memory Model of Reading and Writing Performance. We have already applied the relationship model to real database servers to guide memory allocation and performance evaluation. Simulation results show that this memory model could well describe the quantization relationship of memory space and system performance.

In this paper we have proposed a modified PSO based classification model for multidimensional real dataset and we have studied the results of experiment by implementing particle swarm optimization in classification. Evaluation of the performance of PSO based classifier has been made by considering several variation of parameters of the standard particle swarm optimizer. Here we have used a multidimensional real cancer dataset for classification using PSO to study the behavior of PSO parameters and to observe the accuracy of classification of PSO based classifier in different iterations. Extensive simulation has been carried out using UCI data, on which classification is done using our proposed algorithm. We have also explored the possible influence of variants of PSO on accuracy of classification. The obtained results indicate that particle swarm optimization is an effective technique for classification and can be used successfully on more demanding problem domain.