Community Discussions

As per the 3D Config feature, it should be good to have a Canvas Config option to configure the colouring for a quick highlight of the contextualization results in a Canvas for P&ID or any “plan” documents like isometrics loaded in a Canvas.In the configuration option we should be able to select attributes/data linked to the OCR captures, then in Canvas we should have a button to select and highlight all the OCR captures contextualized to PI-Tags, 3D tags, WOrkorders, SECE equipments…This option is of importance, it will avoid to check one by one each equipment in a P&ID, it will accelerate the completion of the contextualizations workflows, but not only… it’s also of importance for operations when preparing an intervention.

Due to the limit on the number or Views/revisions, we need to deprecate and delete old revisions on a regular basis (in periods with heavy data model development). For Atlas agents and Canvas’ we can find the View versions via the API, allowing us to identify users depending on models that are deprecated. However, with Charts I cannot find any way of programmatically finding this.Is there a hidden API somewhere that allows me to inspect what the different Charts are relying on? If not,. this is an important functionality for governance of data models and needs to be added. 

If you in Charts add a timeseries that is contextualised to a CogniteAsset, you will get the “P&ID” link icon. By clicking on it you will enter search mode for that specific asset, showing the File links as default. This is a nice workaround for a “Find related data”, also allowing you to add CogniteActivities.The instance is shown as a CogniteAsset, and it obviously use the inspection endpoint since you are allowed to change between different views for this instance. However, it doesn’t show the View that is the CogniteAsset extension. The consequence of this is that I’m not able to see neither CogniteFiles nor CogniteActivities as their extension (my CogniteAsset extension has reverse direct relation to the CogniteFile and CogniteActivity extensions). This seriously limits me when filtering on important properties since they only exist in the extension.  A workaround is to go directly to “+ Add data → Add time series”, and then select my CogniteAsset extension when I search, but

When asking the embedded Atlas AI Agent to eg show the difference between two timeseries, it display both timeseries in a chart inside the Chat window. However, both lines get the same color, and there is no legend, so unless you know the average value for those timeseries you do not know which is which. Also, there is no y-axis, so the only way of knowing the value is to hover over the different lines.Legend and axis would help a lot on the user-friendliness  

Currently, monitoring alert emails cannot be customized beyond a certain point: the email subject is always “An alert was created” and the body of the email cannot be customized to be more user-friendly for readers, etc. I would like more customization for the alert emails.

Example: The filter shows options in English, while in the list view they are displayed in Norwegian. For example, the filter shows “Ready”, whereas in the view it shows “Klar”.

Hello,With the current WITSML extractor configuration, we can only whitelist the mnemonics that we want to extract. Would it be possible to also support a blacklist configuration for mnemonics?In our case, the complete list of mnemonics to extract is not known beforehand, which makes maintaining a whitelist challenging.Thank you. 

When listing instances of a view and using the to_pandas method, is there a simple way to use the data types defined on the container properties and use these to cast to similar python data types? Or could this be an option of the python SDK?In the classical CDF data types, this is implemented so that for example a date field and timestamp fields have the correct python data type. The data modelling instances will be either floats or string. It would be very convenient if the underlying container data type can be used so that you do not have to manually cast values in the pandas dataframe.

ContextWe currently run several custom Python extractors built on cognite-extractor-utils. For those, cognite-extractor-utils lets us configure a metrics.cognite block that periodically pushes the extractor's Prometheusmetrics (Counter/Gauge) directly to CDF Time Series — this gives us consistent,queryable observability (row counts, run duration, throughput, failure counts, etc.)across all our extractors, without any extra tooling.ProblemThe PI Extractor and the DB Extractor (official Cognite binaries) already exposePrometheus metrics internally (e.g. pi_extractor_timeseries_states,pi_extractor_stream_iterations, pi_extractor_pi_data_pipe_events,pi_extractor_streamer_data_points, pi_extractor_event_queue_size,pi_extractor_pi_data_loss, pi_extractor_pi_connection_time,pi_extractor_pi_connections, extractor_utils_cdf_datapoints, …), but today they canonly be exposed via a local Prometheus scrape endpoint (metrics.server) or a PushGateway (metrics.push-gateways). There is no equivalent of

Today it is possible to specify the unit of a property of a View, enabling unit conversion on consumption (via the API). However, the CDF UI is currently not showing this unit. It means that we either have to create a dedicated “property_UOM”, or we have to add the unit into the @name in the View description. It would be preferable if the UI could show the unit, and also support the unit conversion. 

The closest to that solution as for now would be adding a timeseries on the canvas, then you have an option to Open in charts and then choose a new one or existing one. This is too cumbersome.See picture below of suggested solution directly in canvas: 

When you add new assets or files in Canvas, the data is added to an area of the Canvas where there are no objects, and the focus moves to that location.While this is great in that it ensures the added data doesn’t interfere with the existing Canvas layout, the following sequence of events occurs every time, which can be cumbersome.① Add new data② The data is added to an area without any objects, and the screen focus automatically shifts there (since the location where the data is added is usually quite far from the main screen, the focus also shifts significantly away from the main screen)③ Move the added data to where you actually want to place it (because the focus from step ② is significantly away from the main screen, you have to move the data a long way to position it, which is quite a hassle)Therefore, is there a way to add data to a location that makes step ③ as easy as possible for the user?

Currently data modelling in CDF only support “soft deletes”, which in day to day operations works well and support the necessary sync features. However we have scenarios where we need to delete vast amounts of instances, ideally in a very short timeframe.During testing we sometimes need to delete and recreate a data model We are currently migrating data models and have two living in parallel for a time, eventually we will delete the old one, which has more than 50 million instances, with the current limit of 10 million soft deletes this would 15 days due to soft delete retention period. Cleanup of old test data Decommissioning of data models (or oil rigs for that matter)This is an ongoing issue in one of test environments where we have a need to do a full wipe of all instances within the CDF project.The feature I am looking for is a way to skip the soft delete default, and simply do a hard delete right away. In the scenarios I’ve listed the features of soft deletes are not needed as we

Views that extends CogniteActivity can be included as an object in Charts, shown as a colored overlay in the timeseries. However, it seems it is a forced filter on assets that are included in the Chart. I can remove the default startTime and endTime filter, but not the asset filter. It is a very likely scenario that I want to add a notification or workorder for an asset that doesn’t contain the timeseries I’m interested in(sensor is upstream). It also might be the case that the asset with the notification/workorder do not have a timeseries at all (it resides further down in the hierarchy).It can be helpful to add a preconfigured asset filter based on the assets indirectly included via the timeseries, but it is an absolute requirement that I can remove that filter. Right now, if the asset with the notification do not have any timeseries I have not found a workaround allowing me to add the notifications I want.   

The “Scheduled” feature, currently in beta, has proven to be user-friendly and well-received.I believe this feature is currently available only to users with both “Checklist admin” and “Template admin” roles, but would it be possible to make it available exclusively to “Checklist admins”?In our company, we assign permissions based on the following roles, and it is often the people who actually perform the inspections (i.e., “Checklist admins”) who issue checklists from the “Scheduled” tab.Therefore, making this feature available to “Checklist admins” as well would make it even easier to use.Template admin → People who edit templates. Only a few people.Checklist admin → People who actually perform inspections. All InField users.This may differ from the original design intent of this feature, but I would appreciate it if you could consider this as one suggestion.

Currently, when attempting to schedule certain functions—such as moving averages—the results may differ from the normal calculation results verified before the scheduled execution.Additionally, the results of scheduled executions vary depending on the execution cycle.A sample is shown in the screenshot.Although I haven’t been able to verify everything, I suspect that similar issues may occur with other functions that set the window size.Users typically assume that scheduled calculations are performing correctly without being aware of these discrepancies, and as things stand now, there is a risk that they may rely on incorrect results. Therefore, I expect that the correct calculation results will be recorded regardless of the schedule settings.

In Charts, you currently have to set up monitoring jobs on an individual basis.  I’d like the ability to set up a single monitoring job and have it available to apply to multiple charts.

Today there is limited to no API access to Charts, creating several issues.One obvious issue is that we cannot automate the setup of Charts. Eg, when an anomaly detection method detects an issue, we would like to be able to auto-create the Charts with the necessary timeseries, timerange, calculations and overlay anomaly insight (CogniteActivity extension). This is not possible today (only pre-build url with instances and range).But another issue is around the maintainability of Charts. Due to the limit on the number or Views/revisions, we need to deprecate and delete old revisions on a regular basis (in particular in periods with heavy data model development). As far as I have seen, Atlas, Canvas and Chart lock the View version to the latest version (or maybe based on the location filter) during config. For Atlas agents and Canvas’ we can find the View versions via the API, allowing us to identify users depending on models that are deprecated, and also auto-duplicate with the updated v

This might already be reported, but I was not able to find it when I searched.Currently Chart only uses an internal, and very limited, unit conversion. It would be a big step forward if we could also use the native unit conversion in timeseries, which is much richer than what is offered via Charts.Since you might have timeseries with no CogniteUnit, Charts still needs to support unit conversion defined in the UI, as a backup solution 

When calculating daily totals or averages using the Resample function, the calculation seems to be performed based on the 00:00 – 24:00 UTC timeframe. I would like the system to account for time zone differences so that the totals or averages are calculated based on the 00:00 – 24:00 period in Japan Standard Time (UTC+9).

 Do we have a feature in the roadmap to allow for showing annotation to annotation lines (manually added in blue) vs. today just having annotations to file? (I realize there are some DM limitations) See below.  The doc to doc annotations (data) and visualization (screenshots) are currently going from a bounding box around the text in the arrow (source doc) to the document (target doc). The line target destination is the center of the document.We have the reverse connection from the bounding box to the center of the doc.What we need is a connection between the bounding box around the text in one document to the corresponding bounding box in the other document.Example: on the left attachment,The left document L12... 74A-1296 annotation goes to the document 1296 on the right. But it attaches to the center of the doc.And R11 ... -74A-1295 on the right connect to the document 1295, connects to the center of the doc.These 2 connections are in orange.The requirement is to have only one connec

I would like to suggest upgrading the Large Language Model (LLM) used by Canvas AI.Currently, the model appears to struggle with understanding technical documents and often provides incomplete or inaccurate answers to relatively straightforward questions. It also has limited ability to interpret visual content such as graphs, charts, and engineering plots, which are common in technical documentation.An improved LLM with stronger reasoning and multimodal capabilities would significantly enhance the user experience by enabling it to:Provide more accurate and context-aware answers. Better understand engineering and technical terminology. Interpret graphs, charts, tables, and other visual elements within documents. Generate more comprehensive and reliable document summaries. Handle complex documents while maintaining context across multiple pages.These improvements would make Canvas AI much more valuable for users working with engineering reports, operational documents, and technical studi

 As a CDF Platform Administrator,I want the ability to configure the PI Extractor to include ptsecurity and datasecurity attributes in the extracted metadata,So that I can use these source-level access control lists (ACLs) to automate and enforce data access security for groups of users in Cognite Data Fusion.  Configuration OptionsGIVEN a standard PI Extractor installation,WHEN looking at the extractor configuration file (e.g., config.yml),THEN there must be a new, optional configuration parameter (e.g., include-internal-security-metadata: true/false or an override setting).GIVEN the configuration parameter is omitted or set to false,WHEN the extractor runs,THEN it must continue to exclude ptsecurity and datasecurity metadata as it currently does (default secure behavior). Extraction BehaviorGIVEN the configuration parameter is set to true,WHEN the extractor performs discovery and extracts Time Series metadata from PI,THEN the ptsecurity and datasecurity attributes must be successfull

 The comment feature in Canvas is really great and we are planning to use it for our shift handovers. We plan to add multiple comments but having to click the comment icon every time to read the content is quite tedious. Since we want the comments to be immediately visible as soon as we open Canvas, we would love to have a feature, like ”pin” comment content.  

Today Canvas cannot show the information stored in a JSONObject. This is unfortunate since method specific information for more general output objects will be stored in the JSONObject to avoid overcrowding the output object. One example is anomaly detection. There will be a large number of different analytics methods detecting anomalies,, each with its own method specific information not relevant for other analysis, but all writing to a common Anomaly View.  As long as the analysis specific information  will not be used for search and filtering, it will be stored on the JSONObject