DW

dw插件是一款便捷的数据库连接工具，能够快速实现数据库链接，操作简单高效。

dw2xls库文件可将数据窗口导出为XLS文件，且格式不变。适用于PB11.2版本。

使用Dreamweaver (DW) 创建新闻发布系统，了解ASP应用开发基础，图文并茂，易于理解。

SQL2008 AdventureWorks2008 DW数据库是专为学习Analysis Service而设计的数据库。

BigData_DW_Real Document Overview The document BigData_DW_Real.docx provides an extensive guide on big data processing architectures, covering both offline and real-time processing architectures. Additionally, it details the requirements overview and architectural design of a big data warehouse project. Big Data Processing Architectures Big data processing architectures are primarily classified into two types: Offline Processing Architecture Utilized for data post-analysis and data mining applications. Technologies: Hive, Map/Reduce, Spark SQL, etc. Advantages: Capable of handling large volumes of data. Disadvantages: Slower processing speed, less sensitive to real-time demands. Real-Time Processing Architecture Suited for real-time monitoring and interactive applications. Technologies: Spark Streaming, Flink. Advantages: High responsiveness for time-sensitive data. Disadvantages: Faster processing but limited to simpler business logic. Big Data Warehouse Project Requirements The big data warehouse project encompasses six key requirements: Daily Active Users: Analysis with hourly trends and daily comparisons. Daily New Users: Analysis with hourly trends and daily comparisons. Daily Transaction Volume: Analysis with hourly trends and daily comparisons. Daily Order Count: Analysis with hourly trends and daily comparisons. Shopping Coupon Risk Warning: Function for identifying potential risks. Flexible User Purchase Analysis: Customizable analysis functionality. Architectural Design for Big Data Warehouse Project Main Project (gmall): Based on Spring Boot. Dependencies: Incorporates Spark, Scala, Log4j, Slf4j, Fastjson, Httpclient. Project Structure: Includes parent project, submodules, and dependencies. Technology Versions:- Spark: 2.1.1- Scala: 2.11.8- Log4j: 1.2.17- Slf4j: 1.7.22- Fastjson: 1.2.47- Httpclient: 4.5.5- Httpmime: 4.3.6- Java: 1.8

微软SQL Server2005 Adventure DW示例数据库，使用标准msi安装程序，确保数据质量，是SQL Server2005联机丛书中的示例数据仓库。

在Matlab中实现了DW检验的代码，并将其移植到PyTorch中的RetinaFace模型中。该模型仅有1.7M大小，支持使用mobilenet0.25或resnet50作为骨干网，以获得不同的性能结果。我们还提供了Mxnet中的官方代码。此外，我们针对移动和边缘设备提供了面向python训练到C++推理的人脸检测器。在WiderFace数据集上的性能测试显示，当使用原始比例秤时，ResNet50达到了95.48%的精度。

在一维水平层状介质中进行直达波的光线追踪，详细信息可参见https://github.com/TcheL/RT1D。