Choose update mechanism¶
In this step, you can select between two different update mechanisms.
|"Fixed Range"||This mechanism ensures one-time data extraction from MindSphere to Visual Explorer.|
|"Continuous Update"||This mechanism ensures ongoing synchronization between MindSphere data and the data source.|
Update mechanisms and user permissions
While creating or updating a new data source a copy of MindSphere user permissions is also created in the backend. Access rights on assets are checked and transferred to the newly created data source.
- For a data source with the update mechanism "Fixed Range", these access rights will not be updated.
- For a data source with the update mechanism "Continuous Update", these access rights are identified with every data source update.
For the higher update frequencies (less than 30 minutes) there are additional restrictions in place that are evaluated during the update process. It limits the amount of data to keep latency low. For particular use cases, the limitation is too conservative. Visual Explorer provides ways to override these values:
- Click "Override limitation".
- Enter the custom limitation (must be a number).
- Proceed with the configuration of the data source.
Tableau® aggregates and processes data in the background. This may lead to Tableau® Server being unresponsive. Also, for higher update frequencies, a higher limitation may delay the data processing. It will extend the time to finish the data processing.
Update mechanism "Fixed Range"¶
In this update mechanism, you can select a fixed start and end date. This should be the time frame for the data in the data source.
The following screenshot displays the configuration for a single update with a fixed start and end date.
Update mechanism "Continuous Update"¶
This update mechanism ensures a continuous synchronization of MindSphere data with the data source.
The following screenshot displays a periodic update where the start and end dates are related to the update time.
Usecases for periodic updates
- The periodic update allows to configure a window around the update time. It is commonly used to create a report of last 30 days.
- If a window can not span into future, then an application that has the ability can compare the report of the desired values with the actual values for example, there are some projection or trends in MindSphere that are the part of stored data
Use aggregated time series data¶
By activating the corresponding checkbox, you can select to use aggregated time series data instead of raw data.
This can be especially useful to reduce the amount of data points. Example, for large time ranges or assets with highly frequent data. The time range is then divided into intervals.
For each interval and variable, the time series data is described by several statistical values like average, sum, count, minimum and maximum. For more information, refer IoT Time Series Aggregates Service.
According to the chosen time range, you can select from the different interval lengths.
The following interval lengths are supported:
- 2 minutes
- 1 hour
- 1 day
- 1 week
- 1 month
- The structure of aggregated time series data sources is totally different from regular IoT data sources. That means, after activating aggregated time series for an existing data source, you might have to rebuild workbooks that are based on it.
- With already pre-aggregated data in your data sources, be cautious with the use of aggregations on top of that in Visual Explorer workbooks. Some combinations make no sense and some may lead to wrong conclusions.
- For instance, creating an average of interval averages will not be equal to the overall average, if the number of data points is varying between intervals.
Any questions left?
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