In Data Hub, is one of three main modules of Wizata with AI Lab and Control Panel. With it, you can connect, label and organize your production data to make them ready for AI analysis. It is the starting point to start using Wizata to build AI solutions!

Connect your data

Within Wizata you should connect live stream data or at least import your historical data to start some analysis. There are multiple solutions and strategies to achieve that and it will depends on your own requirements and architecture. See how you can connect your data to Wizata

Data Point

Data Point defines properties, or "metadata" of your time-series and are used to exchange and keep critical business information about them :

• e.g. defining that a specific time-series is "temperature" in "Celsius degrees" with a normal range of "10 to 20 degrees" ...
• e.g. that a specific time-series is the result of logical calculation and not a raw measurement"

Those information are critical to fuel data scientists and models with knowledge that could help achieve the goals. Learn more about Data Points and how to Updating your datapoint metadata.

Twins

Twin defines logical or physical concept : e.g. machine, area, assets, equipment, controller, ... You create and define twin , enrich them with properties and useful information like data points and then link data points to them.

Twin can be defined manually within Wizata through the user interface or you can automate creation of the twin from your data acquisition solution and with Python through Data Science API.

Events & Batches

With time-series data you can natively and easily query and store data based on time dimension. While being very useful it is not sufficient to apply to all cases, within Wizata you can also query and store data corresponding to events.

Events & Batches solution allow you to associate names or IDs to data. It is useful for many cases such as e.g. :

• Track batches over different assets during process.
• Label anomalies or incidents.
• Annotate time and data relevant to train a model.

Query

A Query formalizes the process of retrieving data from the time-series database using constraints such as digital twin, time, aggregation, and/or filtering. Wizata offers an unified query solution to extract data from it based on pandas python dataframe library and therefore optimized for data science application.

Edge Devices

An Edge is a set of modules that can be deployed on physical or cloud devices to inter-connect Wizata with another network or solution. Its primary function is to connect various data sources, such as OPC-UA or AMQP, and execute AI solutions locally.

Edge Devices can be registered and fully configured directly through the platform interface.

Data Explorer

the Data Explorer enables you to set specific conditions on your charts, making it simpler to spot outliers or verify assumptions drawn from the data.

Furthermore, it offers tools to create and customize data sources directly within the platform, ensuring you have full control over how your data is visualized and analyzed.