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Traditional process or data historians have been in use now for decades – before the time of the internet of things! Originally designed to collect data from industrial sites they formed a representation of the ‘process’ undertaken on the site, keeping a time-series record in a database locally (sometimes referred to as a site historian).
Eventually businesses became networked, and data was shared with head-office (aka the rise of the enterprise historian). Today industrial businesses still need to retain a record of their ever-increasing volume of data, and thanks to modern technology this data can be viewed and analysed in real-time.
Most time-series database solutions are now accessible on the cloud, however not all solutions are equal. It’s important to understand the design limitations and opportunities so you can make an informed decision when choosing how to store your time-series data. After all, history shows us that once a customer stores their data in one solution, they are some-what tied to that solution for an extended period of time – flaws and all.
When looking for a time-series process historian it’s important to consider cost, speed and reliability as well as future business requirements for in-house advanced analytics and predictive analytics. As computing power and technology have advanced, the traditional process historian has been limited by its complex hardware infrastructure, which can limit storage, capacity and speed.
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The efficient deployment, continuous retraining of models with live data and monitoring of model accuracy falls under the categorisation called MLOps. As businesses have hundreds and even.
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'OPUS, an artistic work, especially on a large scale'
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