持久对象的Refsort-loops连接算法

对象关系模型中,基于引用导航的对象连接效率不高,鉴于此,文章提出Refsort-loops连接算法:通过缓存关联对象的引用,并依照重新排序后的引用序列查询关联对象。该算法可以避免对位于同一数据块上不同记录的重复读取,并给出了性能分析公式;通过模拟实验证明了基于该算法的对象连接运算可以减少磁盘的IO次数,节省磁盘的访问时间。     

基于对象访问层优化JDO的持久对象查询

JDO是Java环境中一种面向对象持久存储技术,通过在其持久实现的业务层与持久层之间增加对象访问层,对于业务查询可通过该层中索引对象解析成对对象D的查询,以减少对象处理时间和磁盘IO次数,并通过模拟实验分析了不同过滤因子对查询效率的影响,证明了本方案在索引因子较小的情况下能节省磁盘访问时间.     

基于状态与关联日志的业务对象关联模型

构造良好的业务模型是实现企业信息系统快速重构以适应业务变化的关键。传统的业务建模方法对业务对象之间存在的复杂关联关系无法有效解决，对系统的可扩展性、二次开发效率、追溯性等方面有较大影响。为了解决此问题，提出了一种基于日志的业务对象关联模型，将对象间关联关系与业务对象清晰分离开来，重点探讨了业务对象间数值关联方式，采用简单操作来处理对象内部业务逻辑，采用复合操作来处理对象间关联关系，并将运行时的对象状态与关联信息分离出来形成日志，进而实现基于日志的业务追溯。     

概念格对象渐减维护与关联规则更新

研究概念格对象渐减维护与关联规则更新符合动态环境下概念格应用的需求。提出了对象渐减时概念的更新原则和概念间关系调整方法,并在其基础上设计了概念格对象渐减维护算法;采用了内涵缩减来获得概念蕴含的关联规则,从父子概念内涵差集的变化中发现了对象渐减时的内涵缩减更新规律;获得了对象渐减时的关联规则更新方法。     

使用ADO Stream对象实现文件上传

介绍了将文件上传到数据库及服务器端磁盘的方法，着重讨论了使用ADOStream对象实现图像文件和文本文件无组件上传的基本方法。用此中控制上传文件的类型及大小，并可实现多文件上传。     

基于对象的文件系统I/O分析与评测

目前基于对象的存储设备中多采用通用的文件系统如EXT2、EXT3等进行管理,但面临着对海量数据的高效存储、管理问题.基于对象的文件系统实现了对象按大小在磁盘区域中组织管理,并采用了Hash、最坏适应等机制,提高磁盘利用率的同时显著提升了磁盘吞吐率.对EXT2、EXT3、基于对象的文件系统进行了L/O性能测试及分析.实验表明,基于对象的文件系统的I/O性能是EXT2、EXT3文件系统的2到3倍.     

FSO对象模型在VB中的应用

本文介绍了如何使用FSO对象模型实现磁盘文件的I／O操作的方法和技巧。     

NHibernate对象关联映射

一、介绍 在面向对象开发中,运行时的数据的关联是通过对象之间的引用来实现的.而静态数据的关联是通过表间的外键关联来实现的.而对于一个ORMapping的框架来说,管理对象之间的关联和表间的关系是框架的核心部分.最常见的对象之间的关联是1对多的关联,比如父/子关联,这类关联的数据存储主要是采用主从表的方式来实现,还有一种关联的是多对多的关联,这种关联的数据存储需要将一个n:m关联分解为两个1:n的关联来实现.     

Turbo Pascal中多态对象的管理方法

由于程序运行中，对象实例表现为动态的特性，有效而灵活地对对象进行组织和管理，实现多态对象的输入、删除、复制、存盘及从磁盘加载功能，具有重要的实践意义。本文以图形对象的动态管理为例，说明了在Ｔｕｒｂｏ Ｐａｓｃａｌ中实现这些功能的方法。     

对象语义理论和行为约束推理

本文基于时序模型观点建立对象语义理论，将对象定义为对象操作，对象属性和对象踪迹的集合，并由此给出对象继承，对象复合等概念的语义解释。对象类型是满足一组对象约束的同类对象的集合。有两类不同的对象约束：状态约束表示对象属性之间的关联，而时序约束则表示事件操作之间的时序关联。文章最后用例子表明对象约束推理应用。     

Low-complexity fuzzy relational clustering algorithms for Webmining

This paper presents new algorithms-fuzzy c-medoids (FCMdd) and robust fuzzy c-medoids (RFCMdd)-for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total fuzzy dissimilarity within each cluster is minimized. A comparison of FCMdd with the well-known relational fuzzy c-means algorithm (RFCM) shows that FCMdd is more efficient. We present several applications of these algorithms to Web mining, including Web document clustering, snippet clustering, and Web access log analysis     

使用ADO实现关系数据库访问层

在使用面向对象方法开发关系数据库应用系统时，人们希望对业务逻辑层开发人员隐藏在关系数据库中存取对象的细节，数据库访问层模式就是解决这个问题的一种设计模式。数据库访问层可以以多种方式实现，以ADO数据访问技术为基础，描述了一个关系数据库访问层的实现方案。     

DrawCAD: using deductive object‐relational databases in CAD

Computer‐aided design (CAD) involves the use of computers in the various stages of engineering design. CAD has large volumes of data with complex structures that need to be stored and managed effectively and properly. Database systems provide general purpose programs that can be used to access and manipulate large amounts of data stored in the database. They also provide an independence between the program accessing data and the database. It is therefore important to use database systems to store CAD data in the most efficient and effective manner for easy retrieval and better management. Graphical objects can be created, in CAD, by reusing previously created objects. The data of these objects have references to the other objects they contain. Deductive object‐relational databases not only provide direct support for the effective storage and efficient access to large amounts of data with complex structures on disk, but also perform the inferences and computations to obtain the complete data of graphical objects that reuse other objects. They should be able to play a major role in CAD systems. This is the idea behind the development of the DrawCAD system. DrawCAD is a CAD system built on top of the Relationlog object‐relational deductive database system. It facilitates the creation of graphical objects by reusing previously created objects. The DrawCAD system illustrates how CAD systems can be developed, using database systems to store and manage data and also perform the inferences and computations that are normally performed by the application program. Copyright © 2003 John Wiley & Sons, Ltd.     

An effective approach to vertical partitioning for physical designof relational databases

Vertical partitioning can be used to enhance the performance of relational database systems by reducing the number of disk accesses. The authors identify the key parameters for capturing the behavior of an access plan and propose a two-step methodology consisting of a query analysis step to estimate the parameters and a binary partitioning step which can be applied recursively. The partitioning uses an integer linear programming technique to minimize the number of disk accesses. Significant performance benefit would be achieved for join if the partitioned (inner) relation could fit into the memory buffer under the inner-outer loop join method, or if the partitioned relation could fit into the sort buffer under the sort-merge join method, but not the original relation. For cases where a segment scan or a cluster index scan is used, vertical partitioning of the relation with the algorithm described is still often found to lead to substantial performance improvement     

Organization of relational models for scene analysis

Relational models are commonly used in scene analysis systems. Most such systems are experimental and deal with only a small number of models. Unknown objects to be analyzed are usually sequentially compared to each model. In this paper, we present some ideas for organizing a large database of relational models. We define a simple relational distance measure, prove it is a metric, and using this measure, describe two organizational/access methods: clustering and binary search trees. We illustrate these methods with a set of randomly generated graphs.     

关系数据的中心权重模糊聚类算法

k中心点算法仅仅用一个点去代表整个类显然是不足的,这必然会影响聚类结果的准确性。因此提出了一种关系数据的中心权重模糊聚类算法,在该算法中给每一个属于这个类的对象赋予一个中心权重以此来表示其作为这个类的代表对象的可能性程度,这种机制使类中的多个对象来代表整个类而不是利用类中的一个对象来代表整个类。实验结果表明,该算法能更好地发现数据集中潜在的内部结构及对象之间的关系,得到每个聚类结果更加准确的描述。     

A relational database machine with large semiconductor disk and hardware relational algebra processor

This paper describes the basic concepts, design and implementation decisions, standpoints and significance of the database machine Delta in the scope of Japan’s Fifth Generation Computer Project. Delta is planned to be operational in 1985 for researchers’ use as a backend database machine for logic programming software development. Delta is basically a relational database machine system. It combines hardware facilities for efficient relational database operations, which are typically represented by relational algebra, and software which deals with hardware control and actual database management requirements. Notable features include attribute-based internal schema in accordance with the characteristics found in the relation access from logic programming environment. This is also useful for the hardware relational algebra manipulation algorithm based on merge-sorting of attributes by hardware and a large capacity Semiconductor Disk for fast access to databases. Various implementation decisions of database management requirements are made in this novel system configuration, which will be meaningful to give an example for constructing a hardware and software combination of a relational database machine. Delta is in the stage between detailed design and implementation.     

Efficient disk-based K-means clustering for relational databases

K-means is one of the most popular clustering algorithms. We introduce an efficient disk-based implementation of K-means. The proposed algorithm is designed to work inside a relational database management system. It can cluster large data sets having very high dimensionality. In general, it only requires three scans over the data set. It is optimized to perform heavy disk I/O and its memory requirements are low. Its parameters are easy to set. An extensive experimental section evaluates quality of results and performance. The proposed algorithm is compared against the Standard K-means algorithm as well as the Scalable K-means algorithm.     

A novel approach to adaptive relational association rule mining

The paper focuses on the adaptive relational association rule mining problem. Relational association rules represent a particular type of association rules which describe frequent relations that occur between the features characterizing the instances within a data set. We aim at re-mining an object set, previously mined, when the feature set characterizing the objects increases. An adaptive relational association rule method, based on the discovery of interesting relational association rules, is proposed. This method, called ARARM (Adaptive Relational Association Rule Mining) adapts the set of rules that was established by mining the data before the feature set changed, preserving the completeness. We aim to reach the result more efficiently than running the mining algorithm again from scratch on the feature-extended object set. Experiments testing the method's performance on several case studies are also reported. The obtained results highlight the efficiency of the ARARM method and confirm the potential of our proposal.