对象—关系型DBMS的研究与开发

文章结合不断涌现的新的数据库应用领域的特殊需求及数据库理论的研究趋势和作者民设计对象一关系数据库管理系统内核中的一些体会,论述对象－关系型ＤＢＭＳ这一当前研究热点的研究现状、难点、开发方法及应用前景。     

DOL: 一个演绎对象库语言

演绎对象数据库是对象数据模型和演绎数据库集合的产物.它集成演绎数据库的查询能力和对象数据库的强大建模能力.DOL(deductive object base language)是作者设计的一种演绎对象库语言,它支持类、类层次、继承、集合、部分集、方法及重载和否定.文章着重研究继承、重载和复杂结构化值的交互关系.定义了压缩操作子和重载操作子.基于这两个操作子,定义了与经典逻辑程序类似的直接后承操作子,并研究其不定点性质.     

基于面向对象数据库的对象类泛化方法研究

本文对面向对象数据库中对象成员、对象及类的泛化方法进行了研究。以object数据库中一个任务相关对象集为例，用基于维的属性泛化方法对其进行泛化并生成了相应的对象立方体。     

基于面向对象的关系型数据库模型在工程数据库系统中的应用

分析了对象—关系型数据库模型的特点,给出了一种典型的对象—关系型数据库结构模型,并对其在三类工程数据库中的应用作了一些研究。     

基于对象关系模型的HLA仿真数据库设计

为克服传统的HLA仿真数据库基于关系数据库开发、数据对象的结构及相互间关系需在设计阶段指定、一旦开发完成、存在数据模式难以修改和扩展等缺点,基于HLA仿真数据按面向对象设计的特点,研究了利用对象关系数据库技术开发结构可灵活扩展的HLA仿真数据库的问题,提出了基于HLA对象模型设计、实现、管理和使用可扩展的对象关系型仿真数据库的技术方法.应用于作战仿真领域的实际系统中,结构可扩展的对象关系型HLA仿真数据库在使用和维护上更加灵活、方便,适于HLA仿真领域的高级数据库应用.     

基于JDBC的SQL Server数据库访问技术

JDBC是为各种常用数据库提供无缝联接的技术。本文详细介绍了在JAVA环境下使用SQL语言对SQL Server2000数据库进行访问的技术。并结合实例对JDBC-ODBC桥接器、Connection对象、Statement对象、ResultSet对象在编程中的应用进行了深入的探讨及研究。     

应用ADO.NET访问数据库

该文介绍了数据库开发的一般过程,针对面向对象程序设计的数据库访问技术。数据库访问的各个对象及对象的属性、方法、及使用。     

一种在数据库系统中描述对象的新方法

根据对象的特点及面向对象系统分析、设计及实现的需要,提出了一种在数据库中用关系表（木文称该表为关系对象表）来描述、表示对象的方法。     

简述面向对象数据库中对象的存取

本文介绍了面向对象数据库的概念、基本特征和类型等,重点阐述了在ORACLE8对象关系数据库及开源面向对象数据库DB4OBJECT中数据对象的存取,便于开发者使用面向对象数据库,从而缩短面向对象数据库中对象存取的时间,提高工作效率。     

SCADA系统中可视数据库工具的设计

介绍ＳＣＡＤＡ系统中可视数据库工具开发的概念和实现方法,介绍图形对象与工程数据库的关联及显示方法,通过一可视数据库工具的设计实例,叙述了工控图形对象与数据库关联的程序设计技术。     

基于对象的关系数据库管理系统的设计与实现

对象关系数据库技术是在继承关系数据库技术的基础上,增加面向对象特性,可满足GIS等应用领域的需求而发展起来的一种新型数据库技术,它既具有管理复杂数据的能力,又能提供强有力的查询功能。本文基于面向对象的关系数据库管理系统．提出直接在对象关系数据库中存储矢量地理时空数据的方法,实现了空间数据、时间数据和属性数据的一体化管理．     

基于对象的关系数据库管理系统的设计与实现

对象关系数据库技术是在继承关系数据库技术的基础上,增加面向对象特性,可满足GIS等应用领域的需求而发展起来的一种新型数据库技术,它既具有管理复杂数据的能力,又能提供强有力的查询功能。本文基于面向对象的关系数据库管理系统,提出直接在对象关系数据库中存储矢量地理时空数据的方法,实现了空间数据、时间数据和属性数据的一体化管理。     

ARAXA: Storing and managing Active XML documents

Active XML (AXML) documents combine extensional XML data with intentional data defined through Web service calls. The dynamic properties of these documents pose challenges to both storage and data materialization techniques. In this paper, we present ARAXA, a non-intrusive approach to store and manage AXML documents. We also define a methodology to materialize AXML documents at query time. The storage approach of ARAXA is based on plain relational tables and user-defined functions of Object-Relational DBMS to trigger the service calls. By using a DBMS we benefit from efficient storage tools and query optimization. Approaches without DBMS support have to process XML in main memory or provide for virtual memory solutions. One of the main advantages of ARAXA is that AXML documents do not need to be loaded into main memory at query processing time. This is crucial when dealing with large documents. The experimental results with ARAXA prototype show that our approach is scalable and capable of dealing with large AXML documents.     

基于PostgreSQL数据库的ERP系统的技术实现

介绍了对象关系型数据库PostgreSQL的强大功能及其作为后台DBMS在ERP系统中应用的优越性。给出了该系统的具体实施方案以及数据库在应用和管理中应该注意的几个方面 ,同时对如何保证数据安全也做了专门的讨论     

对象关系数据库管理系统体系结构的研究

In last years, with the Object-Oriented Programming popular and the field of database application extend-ed,many DBMS manufacturers integrate the object model into traditional Relational Database Management Systems (RDBMSs)one after another ,in order to meet the needs for complex engineering applications. This paper firstly givesa total remark on the reason for the prevalence of the Object-Relational Database Systems (ORDBMs)and on the ben-efit of ORDBMSs comparing with RDBMSs and Object Oriented Database Management Systems(OODBMSs) ,then in-troduces an approach for ORDBMS architecture which integrates the Java class library into ORDBMS metadata repos-itory.     

基于对象关系数据库的地理时空数据组织

分析了现有GIS数据的管理方法,指出在现有条件下利用对象关系数据库管理空间数据的优越性。在详细分析了OracleSpatial进行空间数据管理机制的基础上,针对性地提出直接在对象关系数据库中存储矢量地理时空数据的方法。以土地利用变更的时空数据组织为例,在OracleSpatial中实现了空间数据、时间数据和属性数据的一体化管理。     

Informix under CONTROL: Online Query Processing

The goal of the CONTROL project at Berkeley is to develop systems for interactive analysis of large data sets. We focus on systems that provide users with iteratively refining answers to requests and online control of processing, thereby tightening the loop in the data analysis process. This paper presents the database-centric subproject of CONTROL: a complete online query processing facility, implemented in a commercial Object-Relational DBMS from Informix. We describe the algorithms at the core of the system, and detail the end-to-end issues required to bring the algorithms together and deliver a complete system.     

Analyzing the Effects of Hyperthreading on the Performance of Data Management Systems

As information processing applications take greater roles in our everyday life, database management systems (DBMSs) are growing in importance. DBMSs have traditionally exhibited poor cache performance and large memory footprints, therefore performing only at a fraction of their ideal execution and exhibiting low processor utilization. Previous research has studied the memory system of DBMSs on research-based simultaneous multithreading (SMT) processors. Recently, several differences have been noted between the real hyper-threaded architecture implemented by the Intel Pentium 4 and the earlier SMT research architectures. This paper characterizes the performance of a prototype open-source DBMS running TPC-equivalent benchmark queries on an Intel Pentium 4 Hyper-Threading processor. We use hardware counters provided by the Pentium 4 to evaluate the micro-architecture and study the memory system behavior of each query running on the DBMS. Our results show a performance improvement of up to 1.16 in TPC-C-equivalent and 1.26 in TPC-H-equivalent queries due to hyperthreading.     

一种面向空间数据库矢量数据的授权模型与实现方法

空间数据库的广泛应用给人们的生活带来极大便利的同时,也带来了严重的安全威胁.空间应用要求授权系统支持灵活的细粒度授权策略以及否定策略,提供高效的授权实现技术.针对这些安全需求,提出一种基于谓词的矢量数据授权模型,并依据空间数据库管理系统在实现上的特征,采用谓词改写的方法实现对矢量数据的有效授权.和现有工作相比,该模型利用授权谓词表示授权区域,具有更灵活的表达能力,且支持否定授权;所提出的谓词改写的方式不仅避免授权判定时额外增加的一次空间查询,而且可以保证与空间数据库管理系统的低耦合度,还有利于空间谓词的优化,减少空间谓词的冗余.实验证明,该授权模型和实现方法能够满足空间应用的安全需求,实现对空间数据库矢量数据的访问控制和有效授权.     

PARADISE: Big data analytics using the DBMS tightly integrated with the distributed file system

There has been a lot of research on MapReduce for big data analytics. This new class of systems sacrifices DBMS functionality such as query languages, schemas, or indexes in order to maximize scalability and parallelism. However, as high functionality of the DBMS is considered important for big data analytics as well, there have been a lot of efforts to support DBMS functionality in MapReduce. HadoopDB is the only work that directly utilizes the DBMS for big data analytics in the MapReduce framework, taking advantage of both the DBMS and MapReduce. However, HadoopDB does not support sharability for the entire data since it stores the data into multiple nodes in a shared-nothing manner—i.e., it partitions a job into multiple tasks where each task is assigned to a fragment of data. Due to this limitation, HadoopDB cannot effectively process queries that require internode communication. That is, HadoopDB needs to re-load the entire data to process some queries (e.g., 2-way joins) or cannot support some complex queries (e.g., 3-way joins). In this paper, we propose a new notion of the DFS-integrated DBMS where a DBMS is tightly integrated with the distributed file system (DFS). By using the DFS-integrated DBMS, we can obtain sharability of the entire data. That is, a DBMS process in the system can access any data since multiple DBMSs are run on an integrated storage system in the DFS. To process big data analytics in parallel, our approach use the MapReduce framework on top of a DFS-integrated DBMS. We call this framework PARADISE. In PARADISE, we employ a job splitting method that logically splits a job based on the predicate in the integrated storage system. This contrasts with physical splitting in HadoopDB. We also propose the notion of locality mapping for further optimization of logical splitting. We show that PARADISE effectively overcomes the drawbacks of HadoopDB by identifying the following strengths. (1) It has a significantly faster (by up to 6.41 times) amortized query processing performance since it obviates the need to re-load data required in HadoopDB. (2) It supports query types more complex than the ones supported by HadoopDB.