Analytics

UNION: 并集，查询所有内容，重复的记录去除。示例：SELECT * FROM emp UNION SELECT * FROM emp20; UNION ALL: 并集，查询所有内容，包括重复记录。示例：SELECT * FROM emp UNION ALL SELECT * FROM emp20; INTERSECT: 交集，仅显示两个表中的重复记录。示例：SELECT * FROM emp INTERSECT SELECT * FROM emp20; MINUS: 差集，仅显示一个表中有而另一个表中没有的记录。示例：SELECT * FROM emp MINUS SELECT * FROM emp20;

使用 Spark Streaming 进行实时分析 在当今数据驱动的世界里，实时数据分析变得至关重要。P.K. Gupta 和 Megh Computing 在 #HWCSAIS17 中提出了一种利用 Spark Streaming 结合 FPGA as a Service (FPGAaaS) 的技术来加速实时分析的方法。 Spark Streaming 用于实时分析 Spark Streaming 是 Apache Spark 的一个重要模块，它提供了对实时流数据处理的支持。通过微批处理的方式，Spark Streaming 能够高效地处理大量的流数据，并且能够与 Spark 的核心功能（如 SQL、MLlib 等）无缝集成。这使得 Spark Streaming 成为处理实时数据流的理想选择。- ETL (Extract, Transform, Load)：Spark Streaming 支持从多种来源提取数据，进行转换处理后加载到不同的存储系统中。- 数据处理：包括清洗、聚合、过滤等操作，这些操作可以利用 Spark 的强大计算能力快速完成。- 机器学习 (ML) 和深度学习 (DL)：Spark 的 MLlib 库提供了丰富的机器学习算法，而深度学习则可以通过第三方库如 Deeplearning4j 或 TensorFlow on Spark 实现。 为什么使用 FPGA：低延迟和高吞吐量 现场可编程门阵列 (FPGA) 是一种可编程集成电路，其特点是可以根据特定的应用需求进行定制化设计。FPGA 在处理高速数据流时表现出色，特别是在需要低延迟和高吞吐量的场景下。- 内联处理：FPGA 可以直接对接网络接口卡 (NIC)，实现数据的内联处理。这种架构可以显著减少数据传输延迟，并提高处理效率。- 卸载处理：将一些计算密集型任务卸载到 FPGA 上执行，从而减轻 CPU 的负担并提升整体系统的性能。 使用 FPGA 加速器面临的挑战 尽管 FPGA 提供了诸多优势，但在实际应用中也会遇到一些挑战：- 开发难度：相比于传统的软件开发，FPGA 的开发过程更为复杂，需要专门的知识和工具支持。- 调试困难：FPGA 中的错误定位和调试比传统软件更加困难。- 资源限制：FPGA 资源有限，需要合理规划资源分配以避免瓶颈。 Megh 平台 Megh Computing 提出了相关解决方案。

作为数据科学认证的一部分，我完成了UCLA Extension Predictive Analytics课程的最终项目。在这个项目中，我使用了Tableau创建了视觉化效果，并使用R进行了统计分析。分析的数据集来自于葡萄牙实时议会选举结果，每10分钟收集一次数据，涵盖了各个区域和政党的投票情况，包括总选票、空白票和废票的数目和百分比。我还探索了机器学习模型在预测选民投票率方面的潜力。这些数据来自于UC Irvine机器学习存储库。更多信息可以在其网站上找到。

MATLAB导入Excel代码可靠性分析 这是MATLAB代码的集合，系统地将基于csv的事件日志导入标准格式分析基准指标，以纵向跟踪在役舰队的绩效。根据时间段和感兴趣的系统选择，导出为用户友好的Excel格式。

Big data technology has become a hot research topic in the field of education, focusing on analyzing large amounts of educational data collected to improve teaching methods and enhance education quality. Among educational big data, learning analytics is particularly important, as it helps teachers understand students' learning progress and implement personalized teaching, thus promoting teaching reform. In higher education, the application of big data-based learning analytics technology can monitor students' learning processes. By analyzing students' behavioral patterns during the learning process, teachers can gain a more intuitive understanding of each student's performance. This technology provides a series of insights such as 'who is learning', 'what is being learned', and 'how well students are learning', which is crucial for ensuring educational quality. Data collection is the first step in big data learning analytics, which involves utilizing various technical means to gather data from different sources. In the context of online education, the primary source of data is students' online behavior during the learning process. This data includes but is not limited to, video viewing patterns, discussion board participation scores, assignment scores, exam results, and forum interaction scores. These data need to be collected using appropriate tools such as web crawlers written in Python or by calling data through API interfaces. Once the data is collected, the next step is data preprocessing. This stage involves cleaning the data, removing unreliable data points like test accounts and extreme outliers. The goal of preprocessing is to ensure the accuracy of subsequent analysis, structure the data for easy storage, and prepare it for analysis. Data analysis is the core part of learning analytics and primarily includes statistical analysis and visualization, clustering analysis, predictive analytics, association rule mining, and text mining. These methods help teachers gain deeper insights into students' behavioral patterns, learning habits, and performance trends. Statistical analysis and visualization transform data into charts and graphs for intuitive representation of students' learning progress. Clustering analysis groups students by learning habits or grades, while predictive analytics forecasts students' future performance based on historical data. Association rule mining focuses on identifying relationships between students' behaviors, and text mining analyzes content from discussion boards to understand students' learning attitudes and thought processes. The application and development of big data in education holds great potential. With the rapid growth of global data, educational big data is gradually becoming a field of focus both domestically and internationally, offering significant value in education. In practical projects, the application of learning analytics has already shown results. For example, a research project mentioned in the article uses the 'C Programming 1' course on a MOOC platform to analyze students' learning behavior data combined with performance data to help teachers better understand students' progress and offer reasonable teaching suggestions. The application of big data in education, particularly in learning analytics on MOOC platforms, is becoming a key driver of educational reform.

档提供使用 MATLAB 调用 COTOHA API 进行自然语言处理的代码示例。COTOHA API 是 NTT 集团开发的，专为日语提供高级自然语言处理功能。本示例演示了如何使用 MATLAB 和 Text Analytics Toolbox 从 COTOHA API 中提取关键信息，包括： 解析文本并提取关键信息 识别文本中的关键字和实体 生成语音合成的音频文件 总结文本 本示例代码依赖于以下步骤： 获取安全令牌 使用 Text Analytics Toolbox 处理文本 调用 COTOHA API MATLAB 代码和详细的说明可在提供的 GitHub 存储库中找到。

深入了解转换和数据库级互动，确保使用Storm处理的消息可靠性。实施策略以解决实时数据处理的挑战，加载数据集，构建查询，并使用Spark SQL进行推荐。