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Apache IoTDB is a project initiated by Prof. Jianmin Wang's team in the School of Software at Tsinghua University. [1] In 2011, the team chose to use open source NoSQL technology instead of Oracle for a project with mass machine data management, and noticed the insufficiency of NoSQL in the industrial internet of things (IIoT) scenarios.
In statistics, the order of integration, denoted I(d), of a time series is a summary statistic, which reports the minimum number of differences required to obtain a covariance-stationary series. Integration of order d
Release early, release often (also known as ship early, ship often, or time-based releases, and sometimes abbreviated RERO) is a software development philosophy that emphasizes the importance of early and frequent releases in creating a tight feedback loop between developers and testers or users, contrary to a feature-based release strategy.
Time series datasets can also have fewer relationships between data entries in different tables and don't require indefinite storage of entries. [6] The unique properties of time series datasets mean that time series databases can provide significant improvements in storage space and performance over general purpose databases. [ 6 ]
IoTDB: data store for managing large amounts of time series data in industrial applications; Jackrabbit: implementation of the Java Content Repository API; James: Java email and news server; jclouds: open source multi-cloud toolkit for the Java platform; Jena is an open source Semantic Web framework for Java
Cointegration is a statistical property of a collection (X 1, X 2, ..., X k) of time series variables. First, all of the series must be integrated of order d.Next, if a linear combination of this collection is integrated of order less than d, then the collection is said to be co-integrated.
Release managers are beginning to utilize tools such as application release automation and continuous integration tools to help advance the process of continuous delivery and incorporate a culture of DevOps by automating a task so that it can be done more quickly, reliably, and is repeatable. More software releases have led to increased ...
The original model uses an iterative three-stage modeling approach: Model identification and model selection: making sure that the variables are stationary, identifying seasonality in the dependent series (seasonally differencing it if necessary), and using plots of the autocorrelation (ACF) and partial autocorrelation (PACF) functions of the dependent time series to decide which (if any ...