Embedded Database Systems Research Articles

RETRACTED ARTICLE: Application of embedded network system and distributed database in children’s psychological education

In embedded systems, embedded database systems are widely used as carriers for storing, managing, and accessing data. In internal database applications, the data associated with the system may be available in different database systems in different locations. How to determine the exchange of data and the consistency of the data and how to verify the integrity of these databases have become increasingly prominent issues in the development of systems and applications. The embedded database distribution system is an integral part of the embedded database system. Distributed database is an organic combination of computer network and database system. The rapid development of communication and network technology has promoted the development of distributed databases, but at the same time, it has also increased the complexity of the security issues of distributed databases. How to effectively ensure the security of distributed databases is a key issue that needs to be solved urgently in distributed database applications. On this basis, it also analyzes relevant data and conducts research. This article introduces the embryonic form of children’s mental health education—the development of PCT services—and focuses on the development of current mobile resources and terminal children’s mental health education. Implementing the mental health education application program, after recognizing the needs of children, we designed a mental health education platform based on the promotion of information dissemination through the use of ubiquitous learning theories and integration of current learning trends. Use the now popular micro-communication method to be embedded in the system, thereby increasing the usability of the system. The application of wireless local area network improves the comfort of platform applications and provides users with a variety of communication methods, accompanied by strong security and practical services.

An Embedded Cloud Database Service Method for Distributed Industry Monitoring

Current client/server or multiagent based embedded database systems are hard to match the quality of service of distributed industry monitoring. To address the issues, an embedded cloud database service (ECDBS) method is proposed. First, an ECDBS framework is constructed, and a dual-timing transaction control (DT2C) method is proposed to increase the real-time performance and stableness of transaction processing. Then, a cloud computing middleware subsystem is developed to perform dynamical device management and real-time DB query, and a distributed network sniff-timing scheduling (DNS2) algorithm is proposed to improve the efficiency of ECDBS system. Finally, the numerical/industrial experiment results show that the DT2C method can improve the real-time performance of transaction processing in a condition that guaranteed the accessing stability, and the data transfer rate can reach more than 20 MB/s; the DNS2 algorithm can perform the better throughput and time delay, and decrease the power consumption; compared with the MySQL and Berkeley DB, the total transaction processing time can decrease 45.5% and 36.7%, respectively.

Application of Improved B+-Trees Algorithm in Embedded Database

B+-trees algorithm is a high efficient and common-used data organization algorithm in database system. But B+-trees algorithm has a disadvantage of low storage utilization. This disadvantage has a bad effect especially on embedded database systems, which have limit storage. To overcome this drawback, this paper presents an improved B+-trees algorithm, and applies it to an embedded database-Berkeley DB. Experimental results show that the improved B+-trees algorithm can attain higher storage utilization at the expense of more execution time than the conventional B+-trees algorithm.

An Efficient Buffer Scheme for Flash-based Databases

Most embedded database systems are built on a two-level memory hierarchy, a RAM buffer on top of flash memory. Both kinds of memories have limited capacity, thus, how to efficiently utilize them is critical for embedded systems with resource restrictions. Different from magnetic hard disk, flash memory has speed asymmetry in reads and writes, i.e., random reads are over an order of magnitude faster than random writes. μ-tree is the state-of-theart index supporting efficient random updates on the flash memory. However, we found that the traditional page based buffer scheme(LRU-P for short) under-utilized the RAM space. To improve the utilization on the memory resources and the query performance, we propose a novel buffer scheme called LRU-N – a node-based LRU policy for the RAM buffer. In addition, to increase the utilization of the flash memory, we also present a k-partitioning strategy for μ-tree. LRU-N uses a page address swizzling scheme to exploit the co-related locality of accessing multiple tree nodes in the μ-tree. The k-partitioning strategy determines an optimal k value for space efficiency by allocating more flash memory to the leaf nodes and guarantees sufficient space for tree growth. Our experiments conducted on both simulated data sets of various distributions and real-world data sets show that LRU-N can significantly improve buffer hit rate up to 65% for the best cases over the page-based scheme LRU-P; even with small buffer size, LRU-N can also achieve rather good performance. And our proposed k-partitioning strategy can effectively reduce the index size up to 33% over μ-tree.

임베디드 데이터베이스 시스템을 위한 블록 단위 스키핑 기법

일반적으로 데이터베이스 시스템에서의 질의 수행은 대부분의 경우 빠른 응답시간과 더 적은 메모리 사용량을 장점으로 가지는 파이프라이닝 기법으로 이루어진다. 이 때, 질의 수행 계획(QEP)의 각각의 연산 노드들은 Open(), Next(), Close() 함수들을 지원하는 iterator의 인터페이스를 가진다. 그런데, 플래시 메모리 기반의 휴대용 기기들을 위한 임베디드 데이터베이스 시스템에서는 iterator의 Next() 함수뿐만 아니라, 현재 레코드의 이전 레코드를 리턴해주는 Previous()와 같은 함수를 필요로 하는 경우가 많다. 이는 임베디드 환경의 경우 각각의 프로그램이 사용할 수 있는 메모리의 양이 제한적이므로, 사용자가 이전 레코드를 요청하는 경우, 결과 레코드 커서가 현재 레코드를 기준으로 이전 레코드를 다시 가져와야 하기 때문이다. 본 논문에서는 이러한 임베디드 데이터베이스 시스템의 질의 수행 시 각각의 연산 노드들이 Next() 함수뿐만 아니라 Previous() 함수를 블록 단위로 지원할 수 있도록 새롭게 설계 구현하는 과정에서 발생하는 방향 전환 문제를 소개하고 이를 해결하기 위한 블록 단위 스키핑 기법을 제안한다. Today, most of all the query processors in the world generally use the 'Pipelining' method to acquire fast response time (first record latency) and less memory usage. Each of the operator nodes in the Query Execution Plan (QEP) provides Open(), Next(), and Close() functions for their interface to facilitate the iterator mechanism. However, the embedded database systems for the mobile devices, based on the FLASH memory, usually require a function like Previous(), which returns the previous records from current position. It is because that, in the embedded environment, the mobile devices cannot fully provide it main memory to store all the query results. So, whenever needed the previously read records the user (program) should re-fetch the previous records using the Previous() function: the BACKWARD data fetch. In this paper, I introduce the 'Direction Switching Problem' caused by the Previous() function and suggest 'Block-wise Skipping' method to fully utilize the benefits of the block-based data transfer mechanism, which is widely accepted by most of the today's relational database management systems.