中国宗教思想研究的新进展——《中国宗教思想通论》平议

《中国宗教思想通论》是中国宗教思想研究领域的最新力作,是四川大学道教与宗教文化研究所詹石窗教授主持的国家社会科学基金项目“中国宗教思想的历史发展研究“的最终研究成果,该著共63万字,2010年入选“国家社会科学基金优秀成果文库“,2011年3月由人民出版社出版

Selection strategy of materialized views in data warehouse

为了提高决策支持和OLAP查询的响应效率,数据仓库多采用物化视图的思想。因此,物化视图的选择策略是数据仓库研究的重要问题之一。其目标是选出一组存储、维护代价与查询代价的总和为最小的物化视图。提出一个以MVPP(mul-ti-view processing plan)为视图选择的搜索空间的物化视图选择新算法——VSMF(views selection base on multi-factor)算法。该算法在存储空间约束下同时实现多查询最优化和视图维护最优化。A set of materialized views are stored in the data warehouse for the purpose of efficiently implementing decision-support or OLAP queries.The selection of materialized views is one of the most important issues in the data warehouse development.The goal is to select an appropriate set of views so that the total cost of storage,maintenance and query is minimized.A new algorithm named VSMF(views selection base on multi-factor) algorithm using multi-view processing plan structure as search space is proposed,which solve the problem considering both multi-query optimization and the maintenance process optimization under the storage space constrain.福建省自然科学基金项目(A0310008);; 福建省重点科技基金项目(2003H043

应用ODS 技术解决电子政务系统数据一致性问题

讨论了ODS 技术在电子政务系统中的应用. 将ODS 引入到电子政务系统中形成更为合理的DB2ODS2DW3 层结构,并通过ODS 记录系统和参考表的使用进行全局联机事务处理,使各业务数据库内容可以实时更新,保持数 据的一致性. 从根本上解决密切相关的业务数据库数据不一致的问题

Updating Algorithm for Association Rules Based on Fully Mining Incremental Transactions

目前已提出了许多快速的关联规则增量更新挖掘算法,但是它们在处理对新增事务敏感的问题时,往往会丢失一些重要规则。为此,文章提出了一种新的挖掘增量更新后的数据库中频繁项集的算法EUFIA(Entirety Update Frequent Itemsets Algorithm),该算法先对新增事务数据分区,然后快速扫描各分区,能全面有效地挖掘出其中的频繁项集,且不丢失重要规则。同时,最多只扫描1次原数据库也能获得更新后事务数据库的全局频繁项集。研究表明,该算法具有很好的可测量性。Incremental Association rules Mining is an important content of data mining technology.This study proposes a new algorithm,called the Entirety Update Frequent Itemsets Algorithm(EUFIA)for efficiently incrementally mining association rules from large transaction database.Rather than rescanning the original database for some new generated frequent itemsets,EUFIA partitions the incremental database logically according to unit time interval,then accumulates the occurrence counts of new generated frequent itemsets and deletes infrequent itemsets obviously by backward method.Thus,EUFIA can discover newly generated frequent itemsets more efficiently and need rescan the original database only once to get overall frequent itemsets in the final database if necessary.EUFIA has good scalability in our simulation.国家自然科学基金项目(50474033);; 福建省自然科学基金项目(A0310008);; 福建省高新技术研究开放计划重点项目(2003H043

球烯配合物的研究进展

以C60为代表的球烯与球烯类的化合物是近年来化学、物理和材料等学科领域的研究热点，本文综述了三类球烯配合物的合成、结构与性能，着重介绍其最新研究进展。国家自然科学基

Dynamic Selection of Materialized Views of Multi-Dimensional Data with a Multi-Users and Multi-Windows Method

多维视图动态维护是数据仓库领域当前研究的一个热点 随着数据仓库的普及 ,将有越来越多的各种类型的用户使用OLAP工具满足各自特定的分析需求 现有的各种视图选择方法没有考虑不同类型用户的特点 ,从而存在一些缺陷 提出多用户多窗口方法 ,充分考虑用户的差异 ,利用单个用户在查询时的相对有规律性 ,为用户划分级别 ,并为每个用户设置相应级别的用户视图窗口 ,从而合理地利用了系统有限的资源 ,提高了查询响应速度 ,也保证了特殊用户对查询性能的特殊需求 ;给出了相关的定义和MUMW算法 ,并阐述了多用户多窗口方法的优点。Dynamic selection of materialized views of multi-dimensional data is one of the most researched aspects in the field of data warehouse; With the increasing use of data warehouse, there will be accordingly more and more different kinds of users making use of OLAP tools to complete their analytical work; The existing met hods being used to select views do not take into ccount the characteristic of various kinds of users, and therefore have some defects; A multi-users and multi-windows method is presented here, which considerst he disparity among various users and makes use of the rule of the users’queries1 In the method,all users are divided into three group swith different grade and each user is accordingly endowed with user view window of certain grade. Such met hod leads to the reasonable use of the limited space resource and also speeds up the response of query, which stipulates satisfying some special needs of certain user; Some related conceptions and MUMW algorithm are also put forward here, and at the same time , the advantages of this method are described.福建省自然科学基金项目(A0310008);福建省高新技术研究开放计划重点项目(2003H043

基于替换概率的闪存数据库缓冲区替换算法

闪存具有和传统磁盘不同的特性，包括写前擦除、异地更新、读写延迟非对称等.传统的面向磁盘的缓冲区替换算法无法在闪存数据库系统中获得较好的性能.文中提出了一种新的面向闪存数据库的缓冲区替换算法——APB-LRU，其特点：(1)该算法将缓冲区分为冷区和热区，用来捕获数据访问频度，前者用于存放只访问过一次的数据页，后者用于存放至少访问过两次的数据页；(2)采用了其它研究所没有的概率替换机制，即以较大的概率替换冷区中的干净页，以较小的概率替换冷区中的脏页，从而避免了冷脏页长期驻留缓冲区的情况，提高了命中率，获得了较好的整体性能；(3)设计了冷、热区比例动态变化机制，可以根据工作负载的变化动态调整冷、热区所占缓冲区的比例，从而使得替换算法在不同的负载模式下都可以取得较好的性能.基于不同测试数据集的大量实验结果表明，APB-LRU算法具有比其它已有的算法更好的性能.厦门大学基础创新科研基金(中央高校基本科研业务费专项资金)(2011121049,2012121030);国家自然科学基金(61001013,61102136,61202012);福建省自然科学基金(2011J05156,2011J05158,2013J05099)资

Dynamic Selection of Materialized Views of Multi Dimensional Data with a Multi-Users and Multi Windows Method

多维视图动态维护是数据仓库领域当前研究的一个热点 随着数据仓库的普及 ,将有越来越多的各种类型的用户使用OLAP工具满足各自特定的分析需求 现有的各种视图选择方法没有考虑不同类型用户的特点 ,从而存在一些缺陷 提出多用户多窗口方法 ,充分考虑用户的差异 ,利用单个用户在查询时的相对有规律性 ,为用户划分级别 ,并为每个用户设置相应级别的用户视图窗口 ,从而合理地利用了系统有限的资源 ,提高了查询响应速度 ,也保证了特殊用户对查询性能的特殊需求 ;给出了相关的定义和MUMW算法 ,并阐述了多用户多窗口方法的优点Dynamic selection of materialized views of multi dimensional data is one of the most researched aspects in the field of data warehouse; With the increasing use of data warehouse, there will be accordingly more and more different kinds of users making use of OLAP tools to complete their analytical work; The existing methods being used to select views do not take into account the characteristic of various kinds of users, and therefore have some defects; A multi users and multi windows method is presented here, which considers the disparity among various users and makes use of the rule of the users' queries In the method, all users are divided into three groups with different grade and each user is accordingly endowed with user view window of certain grade Such method leads to the reasonable use of the limited space resource and also speeds up the response of query, which stipulates satisfying some special needs of certain user; Some related conceptions and MUMW algorithm are also put forward here, and at the same time, the advantages of this method are described福建省自然科学基金项目 (A0 3 10 0 0 8);; 福建省高新技术研究开放计划重点项目 ( 2 0 0 3H0 43