并行数据库的研究

本文回顾了并行数据库的发展史和研究现状，并以与分布式数据库（ＤＤＢ）对比的方式，着重介绍并行数据库（ＰＤＢ）的硬件结构、并行数据库管理系统（ＰＤＢＭＳ）的参考模型以及并行数据库中的ｊｏｉｎ运算。     

远程数据库访问系统客户／服务器体系结构模型的设计与实现

远程数据库访问（ＲＤＡ）是ＯＳＩ应用层一个特别应用服务元素（ＳＡＳＥ），是数据库基础标准，是构造分布数据库开放式体系结构的基础．本文遵照国际标准ＲＤＡ模型、服务、协议，研究并给出实现ＲＤＡ客户／服务器体系结构模型、方法与系统原型．     

面向对象数据库中对象的表达和操作

面向对象数据库管理系统是目前数据库发展和研究的热点。本文结合华中理工大学ＣＡＤ中心开发的面向对象数据库管理系统ＧＨＥＯＤＢ对对象的表达和处理作了一定的研究。对面向对象数据模型进行了对象的分类处理并为各种类型的对象设计了在系统内部的表达方式，另外本文还研究了对象的操作策略和在ＧＨＥＯＤＢ内部对对象进行常规处理的方法。     

一种支持群体设计的工程数据库管理系统的结构及实现

计算机支持下的群体合作设计是一种新的ＣＡＤ工作环境，而工程数据库管理系统则是ＣＡＤ／ＤＡＭ／ＣＩＭＳ等统计数据管理的基础。为了支持群体协同设计，工程数据库管理必须研究适应于群体工作的系统结构和工作模式。     

Client/Server环境下ODBC的发展

数据库的研究和应用是当今计算机最活跃的领域之一，但是各种数据库产品之间存在许多差异。开放数据库互连（ＯＤＢＣ）为应用程序独立于数据库产品，提高程序可移植性提供了新的途径。本文主要描述了在Ｃｌｉｅｎｔ／Ｓｅｒｖｅｒ环境下，ＯＤＢＣ体系结构的发展     

一个主存数据库管理系统的设计与实现

硬件的发展是为数据库研究开辟了新课题，随着大容量内存的出现，在内存中存放越来越大的数据库已成为可能，使得主存数据库系统和技术成为现实，本文介绍ＧＫＤ－ＭＭＤＢ主存数据库管理系统的系统结构和主要内部数据结构及实现算法。     

主存数据库中恢复问题的研究与实现

主存数据库ＭＭＤＢ是在应用驱动下，硬件和体系结构的发展，特别是在出现大容量内存的情况下而产生的。由于环境的不同，使ＭＭＤＢ数据库管理系统在实现技术的各个方面均表现出与传统的ＤＲＤＢ不同的特性。本文研究ＭＭＤＢ的恢复技术，并讨论恢复技术在ＥＤＳＴ－ＭＭＤＢ原型系统中的具体实现。     

数据库开发工具POWERBUILDER与ODBC接口

ＯＤＢＣ是个通用数据库接口，ＰｏｗｅｒＢｕｉｌｄｅｒ是数据库开发工具，本文分析ＯＤＢＣ的结构，并描述了用ＰｏｗｅｒＢｕｉｌｄｅｒ通过ＯＤＢＣ接口进行数据库开发的接口设置。     

远程数据库访问（RDA）技术的研究与实现模型

远程数据库访问（ＲＤＡ）技术的研究与实现模型祝中华（中国科学院沈阳自动化研究所９０届硕士生）ＲＤＡ（远程数据库存取）是ＯＳＩ（开放系统互连）应用层的一个特别应用服务元素（ＳＡＳＥ），它是异质分布的数据库系统互连的关键技术。本文对ＯＳＩ环境下ＲＤＡ系绕...     

用Delphi和SQL Server开发Client／Server结构的数据库应用程序

Ｄｅｌｐｈｉ和ＳＱＬ Ｓｅｒｖｅｒ是当今两大数据库应用开发工具。本文介绍了用Ｄｅｌｐｈｉ编写数据库应用程序的方法,并通过实例,描述了从ＳＱＬ Ｓｅｒｖｅｒ的设置,ＢＤＥ的设置、数据库和存储过程的编写到Ｄｅｌｐｈｉ数据库应用程序实现的整个开发过程。     

在数据库中自动发现广义序贯模式

本文将序贯模式的发现从单层（SingleLevel）概念扩展到多层（MultipleLevel）概念．即既允许在同层概念之间，也允许在不同层概念之间发现序贯模式，提出了发现广义序贯模式的自顶向下逐层递进的方法．     

Efficient mining of sequential patterns with time constraints by delimited pattern growth

An active research topic in data mining is the discovery of sequential patterns, which finds all frequent subsequences in a sequence database. The generalized sequential pattern (GSP) algorithm was proposed to solve the mining of sequential patterns with time constraints, such as time gaps and sliding time windows. Recent studies indicate that the pattern-growth methodology could speed up sequence mining. However, the capabilities to mine sequential patterns with time constraints were previously available only within the Apriori framework. Therefore, we propose the DELISP (delimited sequential pattern) approach to provide the capabilities within the pattern-growth methodology. DELISP features in reducing the size of projected databases by bounded and windowed projection techniques. Bounded projection keeps only time-gap valid subsequences and windowed projection saves nonredundant subsequences satisfying the sliding time-window constraint. Furthermore, the delimited growth technique directly generates constraint-satisfactory patterns and speeds up the pattern growing process. The comprehensive experiments conducted show that DELISP has good scalability and outperforms the well-known GSP algorithm in the discovery of sequential patterns with time constraints.     

Sequential Pattern Mining in Multi-Databases via Multiple Alignment

To efficiently find global patterns from a multi-database, information in each local database must first be mined and summarized at the local level. Then only the summarized information is forwarded to the global mining process. However, conventional sequential pattern mining methods based on support cannot summarize the local information and is ineffective for global pattern mining from multiple data sources. In this paper, we present an alternative local mining approach for finding sequential patterns in the local databases of a multi-database. We propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. Approximate sequential patterns can effectively summerize and represent the local databases by identifying the underlying trends in the data. We present a novel algorithm, ApproxMAP, to mine approximate sequential patterns, called consensus patterns, from large sequence databases in two steps. First, sequences are clustered by similarity. Then, consensus patterns are mined directly from each cluster through multiple alignment. We conduct an extensive and systematic performance study over synthetic and real data. The results demonstrate that ApproxMAP is effective and scalable in mining large sequences databases with long patterns. Hence, ApproxMAP can efficiently summarize a local database and reduce the cost for global mining. Furthremore, we present an elegant and uniform model to identify both high vote sequential patterns and exceptional sequential patterns from the collection of these consensus patterns from each local databases.     

序列模式挖掘算法研究

数据挖掘领域一个活跃的研究分支就是序列模式的发现,即在序列数据库中找出所有的频繁子序列。目前的序列模式挖掘方法主要分为两类,一类是候选集生成-测试方法;另一类是模式扩展方法。先介绍序列模式挖掘中的基本概念,然后描述几个重要算法,最后给出性能分析。     

构件使用的序列模式发现研究

提出构件使用序列模式发现的处理过程和算法．将构件使用的序列模式发现分为日志数据向序列数据库的迁移、数据预处理、序列模式算法运算、结果的求精和解释、结果保存5个步骤。构件使用的序列模式发现算法有一个刻面参数,可以有针对性地对某一个感兴趣的刻面在不同概念层进行序列模式发现。     

Mining sequential patterns across multiple sequence databases

In this paper, given a set of sequence databases across multiple domains, we aim at mining multi-domain sequential patterns, where a multi-domain sequential pattern is a sequence of events whose occurrence time is within a pre-defined time window. We first propose algorithm Naive in which multiple sequence databases are joined as one sequence database for utilizing traditional sequential pattern mining algorithms (e.g., PrefixSpan). Due to the nature of join operations, algorithm Naive is costly and is developed for comparison purposes. Thus, we propose two algorithms without any join operations for mining multi-domain sequential patterns. Explicitly, algorithm IndividualMine derives sequential patterns in each domain and then iteratively combines sequential patterns among sequence databases of multiple domains to derive candidate multi-domain sequential patterns. However, not all sequential patterns mined in the sequence database of each domain are able to form multi-domain sequential patterns. To avoid the mining cost incurred in algorithm IndividualMine, algorithm PropagatedMine is developed. Algorithm PropagatedMine first performs one sequential pattern mining from one sequence database. In light of sequential patterns mined, algorithm PropagatedMine propagates sequential patterns mined to other sequence databases. Furthermore, sequential patterns mined are represented as a lattice structure for further reducing the number of sequential patterns to be propagated. In addition, we develop some mechanisms to allow some empty sets in multi-domain sequential patterns. Performance of the proposed algorithms is comparatively analyzed and sensitivity analysis is conducted. Experimental results show that by exploring propagation and lattice structures, algorithm PropagatedMine outperforms algorithm IndividualMine in terms of efficiency (i.e., the execution time).     

挖掘空间关联规则的前缀树算法设计与实现

空间关联规则挖掘是在空间数据库中进行知识发现的一类重要问题．为此提出了挖掘空间关联规则的二阶段策略，通过多轮次单层布尔型关联规则挖掘，自顶向下逐步细化空间谓词的粒度，从而空间谓词的计算量大大减少．同时，设计了一种基于前缀树的单层布尔型关联规则挖掘算法(FPT-Generate)，不需要反复扫描数据库，不产生候选模式集，并在关键优化技术上取得了突破．实验表明，以FPT-Generate为挖掘引擎的空间关联规则发现系统的时间效率与空间可伸缩性远远优于以经典算法Apriori为引擎的系统。     

Discovering fuzzy time-interval sequential patterns in sequence databases.

Given a sequence database and minimum support threshold, the task of sequential pattern mining is to discover the complete set of sequential patterns in databases. From the discovered sequential patterns, we can know what items are frequently brought together and in what order they appear. However, they cannot tell us the time gaps between successive items in patterns. Accordingly, Chen et al. have proposed a generalization of sequential patterns, called time-interval sequential patterns, which reveals not only the order of items, but also the time intervals between successive items. An example of time-interval sequential pattern has a form like (A, I2, B, I1, C), meaning that we buy A first, then after an interval of I2 we buy B, and finally after an interval of I1 we buy C, where I2 and I1 are predetermined time ranges. Although this new type of pattern can alleviate the above concern, it causes the sharp boundary problem. That is, when a time interval is near the boundary of two predetermined time ranges, we either ignore or overemphasize it. Therefore, this paper uses the concept of fuzzy sets to extend the original research so that fuzzy time-interval sequential patterns are discovered from databases. Two efficient algorithms, the fuzzy time interval (FTI)-Apriori algorithm and the FTI-PrefixSpan algorithm, are developed for mining fuzzy time-interval sequential patterns. In our simulation results, we find that the second algorithm outperforms the first one, not only in computing time but also in scalability with respect to various parameters.     

Mining sequential patterns by pattern-growth: the PrefixSpan approach

Sequential pattern mining is an important data mining problem with broad applications. However, it is also a difficult problem since the mining may have to generate or examine a combinatorially explosive number of intermediate subsequences. Most of the previously developed sequential pattern mining methods, such as GSP, explore a candidate generation-and-test approach [R. Agrawal et al. (1994)] to reduce the number of candidates to be examined. However, this approach may not be efficient in mining large sequence databases having numerous patterns and/or long patterns. In this paper, we propose a projection-based, sequential pattern-growth approach for efficient mining of sequential patterns. In this approach, a sequence database is recursively projected into a set of smaller projected databases, and sequential patterns are grown in each projected database by exploring only locally frequent fragments. Based on an initial study of the pattern growth-based sequential pattern mining, FreeSpan [J. Han et al. (2000)], we propose a more efficient method, called PSP, which offers ordered growth and reduced projected databases. To further improve the performance, a pseudoprojection technique is developed in PrefixSpan. A comprehensive performance study shows that PrefixSpan, in most cases, outperforms the a priori-based algorithm GSP, FreeSpan, and SPADE [M. Zaki, (2001)] (a sequential pattern mining algorithm that adopts vertical data format), and PrefixSpan integrated with pseudoprojection is the fastest among all the tested algorithms. Furthermore, this mining methodology can be extended to mining sequential patterns with user-specified constraints. The high promise of the pattern-growth approach may lead to its further extension toward efficient mining of other kinds of frequent patterns, such as frequent substructures.     

A Knowledge Discovery Framework for Civil Infrastructure: A Case Study of the Intelligent Workplace

Large databases are becoming increasingly common in civil infrastructure applications. Although it is relatively simple to specifically query these databases at a low level, more abstract questions like ‘How does the environment affect pavement cracking?’ are difficult to answer with traditional methods. Data mining techniques can provide a solution for learning abstract knowledge from civil infrastruc-ture databases. However, data mining needs to be performed within a systematic process to ensure correct and reproducible results. Many decisions must be made during this process, making it difficult for novice analysts to apply data mining techniques thoroughly. This paper presents an application of a knowledge discovery process to data collected for an ‘intelligent’ building. The knowledge discovery process is illustrated and explained through this case study. Additionally, we discuss the importance of this case study in the context of a research effort to develop an interactive guide for the knowledge discovery process.