基于ADO数据构形的VB动态分层报表

利用ADO数据构形技术可以对多表进行联合查询,生成具有清晰层次结构的分层记录集,为多种可视化控件和分层报表提供数据源。概述了数据构形技术的主要功能,通过简单的示例介绍了如何构建SHAPE命令并生成动态分层记录集,讨论了采用多种方法生成动态分层报表的关键技术。     

基于Excel的Web报表的设计与实现

采用分层技术,把Excel报表生成的HTML代码分离成数据层和表结构层,通过自定义的HTML标签标识数据层与表结构层的对应关系、位置、属性等信息;在服务器端,编写相应的自定义HTML标签的操作代码,实现报表填报、报表自动计算汇总、报表输出等功能。通过在开滦集团纪检监察管理系统中的运用,基于Excel的Web报表是实现报表快速开发、运用的非常优秀、可行的方法。     

VB程序设计——数据表通用打印程序

本文介绍在Visual Basic程序中,利用Visual Basic操作Microsoft Excel,通过一个可携带记录集参数的过程,实现对结构不同、标题不同的各类数据表的打印;克服了Visual Basic报表设计功能的局限性,解决了在程序运行中,动态生成的各种结构不同的记录集对应的数据表的打印输出问题;统一并简化了Visual Basic应用程序对各类不同结构数据表的设计及打印的实现方法。     

MFC中实现ADO对多种数据类型动态记录集的绑定

在MFC中用DAO进行本地数据库记录集绑定，极大地提高了数据的访问的效率，但在访问过程数据库方面却显笨拙，ADO作为一种通用数据访问对象模型，已成为数据访问的一种趋势。文中结合实际开发经验，详细地阐述了在MFC中如何使用ADO数据对象模型，来实现远程数据访问中由多种数据类型所构成的动态记录集的绑定。     

ASP.NET中实现DataGrid控件动态绑定数据的方法

本文总结了ASP．NET中DataGrid控件绑定数据的方法；找出DataGrid控件动态生成报表的不足之处；通过改进绑定方法，使DataGrid控件可以生成动态显示数据、实时改变交互方式的Web报表。     

基于动态标签的关系抽取方法

针对远程监督数据集的关系抽取研究方法存在着大量标签噪声的问题,提出了一种作用于分层注意力机制关系抽取模型的动态标签方法。首先，提出了一种根据关系类别相似性生成动态标签的概念。由于相同的关系标签包含相似的特征信息，计算特征信息的关系类别相似性有助于生成与特征信息相对应的动态标签。其次，利用动态标签方法的评分函数来评价远程监督标签是否为噪声，以决定是否需要生成新的标签代替远程监督标签，通过调整远程监督标签来抑制标签噪声对模型的影响。最后，根据动态标签来更新分层注意力机制以关注有效实例，重新学习每个有效实例的重要性，进一步抽取关键的关系特征信息。实验结果表明，相较于原始的分层注意力机制关系抽取模型，所提方法在Micro和Macro分数上分别有1.3个百分点和1.9个百分点的提升，实现了噪声标签的动态纠正，提升了模型的关系抽取能力。     

会计对账系统设计与实现

介绍了对账系统的设计思想和技术难点,详细分析了提取Excel中的数据放入记录集,并就记录集的定义、记录集和数组、记录集和列表之间的数据传递及合并做了深入的探讨.     

在Delphi中如何选择字段动态生成Excel报表

利用Delphi设计界面，实现将数据库系统中的数据，根据用户选择字段，动态生成Excel报表，实现了打印部分数据的目的。     

分层强化学习中的动态分层方法研究

分层强化学习中现有的自动分层方法均是在对状态空间进行一定程度探测之后一次性生成层次结构,不充分探测不能保证求解质量,过度探测则影响学习速度,为了克服学习算法性能高度依赖于状态空间探测程度这个问题,本文提出一种动态分层方法,该方法将免疫聚类及二次应答机制融入Sutton提出的Option分层强化学习框架,能对Option状态空间进行动态调整,并沿着学习轨迹动态生成Option内部策略,以二维有障碍栅格空间内两点间最短路径规划为学习任务进行了仿真实验,结果表明,动态分层方法对状态空间探测程度的依赖性很小,动态分层方法更适用于解决大规模强化学习问题.     

VBA技术在动态电子表格中的应用

Excel是制作电子表格的优秀工具,如果用VBA（Visual Basic for Application)技术中的OLE Automation来完成报表的制作,并将数据库的数据输出到报表,便可生成动态的报表。     

ASP查询Web数据库记录的分页显示技术与实现

论述了ASP开发Web数据库时，控制网络数据流量进行分页浏览输出的方法及分页浏览使用的主要技术，介绍了RECORDSET对象与分页显示有关的技术与基础，以实例介绍了数据记录分页显示的关键技术，并给出了设计思想和具体程序设计。     

分层聚类算法在文本挖掘中的应用

随着Internet技术的高速发展,如何从海量的Web信息中快速而有效地获得所需信息也就成为一项重要课题,而数据挖掘技术是解决这一难题的有效办法。其中数据挖掘中的聚类方法是用来发现数据分布的一项重要方法。本文首先阐述了Web挖掘的有关理论,然后针对Web挖掘中的分层聚类法进行了较为详细的论述,最后使用该算法并结合改进的特征权值计算方法和文本相似度的计算方法,建立了训练文本库。     

Symbolic hierarchical analysis in currency markets: An application to contagion in currency crises

In this paper we introduce a new method to describe dynamical patterns of the real exchange rate co-movements time series and to analyze contagion in currency crisis. The method combines the tools of symbolic time series analysis with the nearest neighbor single linkage clustering algorithm. Data symbolization allows us obtaining a metric distance between two different time series that is used to construct an ultrametric distance. By analyzing the data of various countries, we derive a hierarchical organization, constructing minimal-spanning and hierarchical trees. From these trees we detect different clusters of countries according to their proximity. We show that this methodology permits us to construct a structural and dynamic topology that is useful to study interdependence and contagion effects among financial time series.     

SHMA:一种云平台的监控框架

由于云平台架构的复杂性和工作负载的不可预测性,云监控对保证云平台的高可用性有重要意义,云平台上资源的动态性、多样性以及资源规模巨大给云平台监控带来了一定的困难。研究并提出一种面向云平台的可扩展多层次监控框架SHMA,SHMA采用微服务架构构建监控系统中可扩展的各个相互独立的服务组件,实现对云平台不同层次的应用服务、中间件和基础设施资源的监控。通过对部署并运行在CloudStack基础设施云平台上的某综合减灾空间信息服务应用系统进行案例分析,验证了该监控框架的有效性。     

Shortest-linkage-based parallel hierarchical clustering on main-belt moving objects of the solar system

Data clustering is an important data preparation process in many scientific analysis researches. In astronomy, although the distributed environments and modern observation techniques enable users to collect and access huge amounts of data, the corresponding clustering process may become very costly. One of the challenges is that the sequential clustering algorithms, that can be applied to cluster hundreds of thousand main-belt asteroids to reason about the origins of the main-belt asteroids, may not be used in the distributed environment directly. Therefore, this study focuses on the problem of parallelizing the traditional hierarchical agglomerative clustering algorithm using shortest-linkage. We propose a new parallel hierarchical agglomerative clustering algorithm based on the master–worker model. The master process divides the whole computation into several small tasks, and distributes the tasks to the worker processes for parallel processing. Then, the master process merges the results from the worker processes to form a hierarchical data structure. The proposed algorithm uses a pruning threshold to reduce the execution time and the storage requirement during the computation. It also supports fast incremental update that merges new data items into a constructed hierarchical tree in seconds, given a tree of about 550,000 data items. To evaluate the performance of our algorithm, this study has conducted several experiments using the MPCORB dataset and a dataset from the DVO database. The results confirm the efficiency of our proposed methodology. Compared with prior similar studies, the proposed algorithm is more flexible and practical in the problem of distributed hierarchical agglomerative clustering.     

Two-level hierarchical Z-buffer with compression technique for 3D graphics hardware

The hierarchical Z-buffer is application-invisible and more efficient than the traditional Z-buffer for quickly rejecting hidden geometries. But there are construction and management issues associated with integrating a hierarchical Z-buffer into current graphics hardware. Here we present a two-level hierarchical Z-buffer algorithm, and provide solutions to these issues. Simulation results show that the bandwidth can be reduced by up to 35%. Moreover we propose a dynamic bi-level HZ-buffer compression technique that reduces the buffer size up by to 40%, and for which there is little performance degradation.     

基于邻域粗糙集的大规模层次分类在线流特征选择

在分类学习任务中,数据的类标记空间存在层次化结构,特征空间伴随着未知性和演化性.因此,文中提出面向大规模层次分类学习的在线流特征选择框架.定义面向层次化结构数据的邻域粗糙模型,基于特征相关性进行重要特征动态选择.最后,基于特征冗余性进行鉴别冗余动态特征.实验验证文中算法的有效性.     

An efficient subspace sampling framework for high-dimensional data reduction, selectivity estimation, and nearest-neighbor search

Data reduction can improve the storage, transfer time, and processing requirements of very large data sets. One of the challenges of designing effective data reduction techniques is to be able to preserve the ability to use the reduced format directly for a wide range of database and data mining applications. We propose the novel idea of hierarchical subspace sampling in order to create a reduced representation of the data. The method is naturally able to estimate the local implicit dimensionalities of each point very effectively and, thereby, create a variable dimensionality reduced representation of the data. Such a technique is very adaptive about adjusting its representation depending upon the behavior of the immediate locality of a data point. An important property of the subspace sampling technique is that the overall efficiency of compression improves with increasing database size. Because of its sampling approach, the procedure is extremely fast and scales linearly both with data set size and dimensionality. We propose new and effective solutions to problems such as selectivity estimation and approximate nearest-neighbor search. These are achieved by utilizing the locality specific subspace characteristics of the data which are revealed by the subspace sampling technique.