logic series comparison

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Oracle Logic Structure Diagram-Tutorial
在Oracle数据库中,逻辑结构示意图展示了数据库的组成部分。以下是各部分的说明: Database Blocks:数据库块是数据库的基本存储单位,存储数据的基本单元。 Tablespace:表空间是数据库逻辑存储结构的集合,包含多个数据文件。 Next Extent 5 MB:扩展是表空间中数据文件的物理增长单位,当前为5MB。 Segment 20 MB:段是由一组连续的数据库块组成的逻辑存储单位,当前段大小为20MB。 Initial Extent 15 MB:初始扩展是表空间创建时的初始空间分配量,此处为15MB。
SAPNetweaver_vs_Oracle_Comparison
Oracle的资料,结论当然是Oracle胜出了。
Top NoSQL Time Series Databases Overview
Time Series Database (TSDB) is a database system specifically designed for efficiently storing, managing, and processing time series data. This type of data typically involves numerical values associated with specific timestamps, commonly found in monitoring, IoT, financial transactions, and operational analytics. This article explores several key NoSQL time series databases, including InfluxDB, ScyllaDB, CrateDB, and Riak TS, as well as Apache Druid, highlighting their characteristics and application scenarios. 1. InfluxDB InfluxDB, developed by InfluxData, is an open-source time series database designed for real-time analysis and big data. It features high write performance and low-latency query capabilities, supporting complex time series data queries. InfluxDB is particularly suited for handling data from sensors, logs, metrics, and is widely used in monitoring systems, IoT applications, and real-time analysis scenarios. 2. ScyllaDB ScyllaDB is a high-performance distributed database based on Apache Cassandra. It offers higher throughput and lower latency than native Cassandra. Its optimized time series data processing capabilities make it ideal for real-time applications such as monitoring and log analysis. ScyllaDB supports multi-data center deployments to ensure high availability and consistency of data. 3. CrateDB CrateDB is a column-oriented distributed SQL database that can handle large-scale time series data. It provides a SQL interface, making time series data operations more familiar to traditional database users. CrateDB is suitable for projects that require rapid analysis of large amounts of time series data and prefer using SQL for querying. 4. Riak TS Developed by Basho Technologies, Riak TS is a NoSQL solution focused on time series data. It inherits the core features of Riak, such as high availability and scalability. Riak TS is suitable for applications that need to store and retrieve time series data in a distributed environment, such as recording equipment status in the telecommunications or energy industries. 5. Apache Druid Although Druid is not a traditional NoSQL database, it is a columnar data store designed for real-time analytics. Druid is renowned for its excellent Online Analytical Processing (OLAP) performance and low-latency query capabilities, making it suitable for big data real-time analysis and business intelligence applications. These databases each have their strengths. InfluxDB and Druid excel in real-time analytics, ScyllaDB and CrateDB offer powerful distributed processing capabilities, while Riak TS specializes in distributed storage and retrieval. Developers should consider data scale, performance requirements, query complexity, SQL support, and team expertise when choosing a solution.
Matlab Development Robot Target Tracking Control Using Fuzzy Logic
Matlab Development: Robot Target Tracking Control Using Fuzzy Logic. This project involves using fuzzy logic with MatlabhW2K16 to develop a two-degree-of-freedom robotic arm for precise target tracking using image processing techniques.
Octave Fuzzy Logic 工具箱 0.4.5 修复版
该工具箱解决了安装 Octave 时出现的代码问题,可直接安装使用。
Acycle Time Series Analysis Software for Research and Education
Acycle: Acycle是一个用于研究和教育的时间序列分析软件,提供强大的分析工具和用户友好的界面,适合学术研究和教学使用。
P6880_112000_MSWIN_Linux_Version_Comparison
p6880_112000_MSWIN-x86-64.zip VERSION: 11.2.0.3.22p6880_112000_Linux-x86-64.zip VERSION: 11.2.0.3.23
Fill Missing Data in Time Series Using NaN in MATLAB
该代码有助于填补时间序列数据中的空白。为此,它需要一个缺少日期和时间的 DateTime 数组以及具有相应缺失值的 测量数组。它将检查日期数组中缺少的日期,并为测量数组中的相应日期填充 NaN,这将有助于获取连续的时间序列数据。
Finding Main Harmonics in Time Series Data with Periods Function
Periods是一个函数,其目的是找到时间序列数据的主要谐波分量。该函数获取时间序列中主要谐波分量的周期、幅度和滞后相位。它基于循环下降的周期性回归方法,包括统计显著性检验。上述功能非常易于使用,并不需要用户完全理解时间序列理论或大量输入,但足够灵活以承担更复杂的任务,例如预测。此外,根据先前的知识,可以轻松地包括或排除特定时期。González-Rodríguez, E.等人提供了有关如何使用该功能的参考资料和更详细的信息;(2015)时间序列中周期的提取和建模的计算方法。开放统计杂志,5, 604-617。http://dx.doi.org/10.4236/ojs.2015.56062。Periods在MATLAB 2013a版本及后续版本上进行了测试。任何问题/意见都可以通过电子邮件发送至egonzale@cice
Comparative Analysis of Stock Price Series Similarity Between China and Japan
在本论文中,我们将时间序列数据挖掘的方法应用到中日证券市场的比较问题中,并在聚类分析中定义新的函数以判别最优的分类数。我们发现:在指数收盘价时间序列比较方面,中日两个证券市场的确存在一定的相似性,但中国市场的短期波动要大于日本市场。因此,如果将日本证券市场的发展历史作为中国证券市场的事件库,不足以描述和预测中国证券市场的走势。同时,在中国证券市场上,深证成指比上证综指的短期波动幅度更大,具有更多的高频噪声。