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Hello,We’re currently having some issues with pulling our PI data from Cognite since we have several PI tags that are string timseries vs numeric (as in rather than having 0 or 1, the timeseries shows open or closed). It appears that Cognite cannot handle string timeseries because they cannot be aggregated, however some of the logic that’s built in to our devices can only be pulled in this manner. We’d at least need a way for Cognite to convert the data pulled as strings as numeric and maybe auto assigning numeric values to each string event? A bonus would be to have an associated conversion of those numeric values to the associated string from the pullExample:Cognite pulls timseries EXAMPLE.TIME.VALVE.A.STATUS that contains “Open” and “Closed” and auto assigns 0 to “Open” and 1 to “Closed”There’s then an associated ‘conversion’ that states for timeseries EXAMPLE.TIME.VALVE.A.STATUS:0 = “Open”1 = “Closed” then Cognite pulls another timeseries EXAMPLE.TIME.VALVE.B.STATUS and happens to assign 0 to “Closed” and 1 to “Open” for whatever reasonThere’s then an associated ‘conversion’ that states for timeseries EXAMPLE.TIME.VALVE.B.STATUS:0 = “Closed”1 = “Open”
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 studies, where understanding both text and visual information is essential.
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 versions/views. However, with Charts this is not possible. If Charts is supposed to only be a do-my-analysis-and-then-throw-it-away tool, that is ok, but if it is intended to be used for more long term analysis (it does offer functionality for this like calculations and threshold monitoring) we need a way to monitor status and update the views/instances in the Charts on an enterprise level (via API)
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 successfully captured and populated as key-value pairs in the CDF Time Series metadata block.GIVEN the configuration parameter is set to true,WHEN a source PI point has empty or null values for these security fields,THEN the extractor should gracefully skip writing those specific keys to CDF metadata without throwing errors or halting the extraction process.
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.
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).
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
I have a timeseries chart in my Flow dashboard. When I select a time range (and press Apply), I see no change to the time range in the chart
I have Canvas’ with View revisions that has been deleted. If I delete the “object” directly in the Canvas, it works well. However, if I delete the Canvas directly without first deleting the objects, I get stranded nodes (FdmInstanceContainerReference nodes). If I delete a Canvas with object linking to existing View revisions, it works well.Would be nice if this was fixed since getting Canvas’ with outdated revisions is relatively common in a period with heavy data model development (revisions are constantly bumping, and we need to clean old revisions to stay below the view/revision limit). Now we are forces to run cleanup scripts on stranded canvas nodes on a regular basis
The proposal is to get in the Search overview, the location of the contextualized tag in the 3D CAD structure. Such feature will help the user from where the 3D CAD contextualization is coming from in teh 3D CAD structure.Rigth now we do not have this feature, and this should allow us to potentially correct, add, remove some pieces of the 3D CAD structure contextualizationSee the attached illustration...
The layers on a PDF are not currently clickable within CDF. I have an example of a file that was loaded as a PDF with certain layers turned off. When we put this PDF in a canvas and then downloaded the canvas, all the layers were turned on, making it difficult to read. It appears Cognite turned on all internal PDF layers when creating the download.Ask: A - Let’s leave the layer state as is when downloading from a CanvasB - Don't embed the layers and leave them exposed in the resulting PDFC - Have layers viewable in Search PreviewD - (Future) Expose the internal layers in Canvas/preview
As I solution architect I would like to arrange persistently all my categories in my landing “Search page”, in a multilevel (tree) format, with minimal 2 levels, so the “Search page” is not convoluted/big and users still have access to all their categories with minimal clicks. As of today, I see that the only categories that allow multiple levels are the ones that were “inherited” from the old resource types, such as asset, files, time series, activity. Everything else added in the data model will be available view bullets.For PBF we have several data models within the same project, what causes the search page to be very extensive and not user friendly. As a workaround, we created a solution data model and configured a new location to expose a simplified “search page”, but for that we are omitting several of the categories that users would like to see. So if the user wants to access data that is not in this simplified view he/she needs to select the “full” location that has all the data, what is a bad user experience in terms of multiple clicks.. on the other hand if we keep everything exposed it is very hard to find a category and the search page gets convoluted. In the work around we implemented, user can still change location go from the “full” location to the “simplified” location, but it requires multiple clicks, what is not desirable. A better solution would be to allow multilevel configuration for categories (parent/child relationship), so we could groups categories that are related to each other. Also the data would be more organized and friendly from the user point of view. Note: The following it is just and example to show the desirable outcome for a “look & feel” in terms of arranging data, please ignore the “material” reference, once it is not relevant.- Material - Material Group - Material Type - Material Status
Problem Statement: Currently, there is no great way to print comments in industrial canvas (If there is not aware of a good way). Potential Solution Option(s):Anchored to DocumentComment Selection box in the download list (similar to multiple pages) that would allow comments to be turned on that would be anchored to the document based where the user placed it. Comment Pane on right side that shows comments for that page. (Similar to microsoft suite review pane)Separate Comment PageComment Page independent similar to the cover page that could be checked or un-checked that would display on a separate page that would be part of the download. Downside is the comments may not make sense if there are not associated with the drawing.
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”.
Wants the option to print a checklist with selected attributes from InField.
Be able to copy the URL for a given checklist, template, etc. for sharing with a colleague.
Documents that are most important for the technician should be listed first, e.g. P&IDs (XB).
Simple Dashboard to display all the projects running in the organization - Project DashboardDetails of the projects when expanded
When dropping a file from your desktop/drives into Canvas, the file size increases. As one example, it went up 30 times; from 84kb --> 2520kb (without the canvas coverpage). The file size change makes it difficult to download the canvas, as it gets stuck. The end users cannot download the canvas they create and therefore are prone to stop using the platform.
Hi Team,It was observed that List and Search queries have some functional limitations which are handled explicitly by teams. Below are the expectations: Search queries must support pagination, enabling users to retrieve results beyond the current 1000-record limit. Pagination should include a cursor or offset mechanism, allowing seamless navigation through large datasets without data loss or duplication. List queries must support case-insensitive filtering Users need substring search over large datasets, which is not fully supported. Can we provide feature like "Contains" which will allow substring search.Thanks,Rahul
Cognite Data Fusion (CDF) appears to enforce a limit of approximately 200 Azure AD group memberships when resolving permissions for Service Principals. This limitation does not exist in Microsoft Graph, which supports retrieval of all group memberships associated with a user or Service Principal.As organizations scale their authorization models using Azure AD groups, this restriction can prevent access inheritance from functioning as expected and requires manual workarounds that increase operational overhead and governance complexity.Business ContextMany enterprise deployments use Azure AD groups to manage access to assets, plants, data products, and other resources within CDF.A common architecture relies on: Azure AD groups representing access domains. Group Object IDs mapped to corresponding Source IDs in CDF. Service Principals inheriting permissions through group membership. However, when the number of group memberships exceeds the current supported threshold, some memberships are not considered during authorization, resulting in incomplete permission resolution.Current WorkaroundThe current workaround consists of adding Service Principals directly to individual access groups instead of relying on inherited permissions through the existing group structure.While functional, this approach presents several challenges: It does not scale as the number of groups and data products grows. It increases administrative effort and maintenance activities. It complicates access governance and auditing processes. It introduces a higher risk of configuration errors and permission inconsistencies. Problem StatementThe current behavior creates a gap between Microsoft Entra ID (Azure AD) authorization capabilities and CDF authorization behavior.Since Microsoft Graph supports retrieval of all group memberships, the limitation appears to stem from the current implementation within CDF rather than from the underlying identity provider.This can impact organizations that rely on group-based authorization models to manage access at scale.Business ImpactOperational Impact Increased administrative effort for access management. Additional maintenance when new groups, assets, plants, or data products are introduced. Reduced efficiency of centralized identity management practices. Governance Impact Increased complexity in maintaining access-control policies. Reduced effectiveness of group-based authorization strategies. Greater effort required for auditing and access reviews. Security and Compliance Impact Increased reliance on manual permission assignments. Higher risk of access inconsistencies. Potential compliance concerns resulting from non-standard authorization processes. Scalability Impact Reduced scalability of Azure AD group-based authorization models. Growing operational burden as enterprise environments expand. Limitations on adoption of best-practice identity and access management patterns. Requested EnhancementEnhance CDF authorization to support all Azure AD group memberships associated with a Service Principal, or significantly increase the current limit, ensuring alignment with Microsoft Graph capabilities.Possible implementation options include: Removing the current membership limit. Supporting pagination when retrieving group memberships from Microsoft Graph. Supporting complete transitive group membership resolution. Providing configurable limits for enterprise deployments where required. Expected Benefits Improved scalability of enterprise authorization models. Elimination of manual access-management workarounds. Better alignment with Microsoft Entra ID / Azure AD capabilities. Reduced operational, governance, and compliance risks. Simplified lifecycle management for users, groups, and Service Principals.
SummaryWhen building workflows in Cognite Data Fusion to populate views in Data Models, there is often a need for intermediate, curated datasets between raw source data and the final target data model.Today, this intermediate data can be stored in RAW tables, but that requires customers to manage temporary tables, cleanup logic, naming conventions, and lifecycle handling manually. A native, workflow-managed temporary storage layer would make workflow development cleaner, reduce repetitive transformation logic, and simplify the overall implementation. Business ContextWe are using CDF workflows to populate an Asset Hierarchy data model.The source data comes from multiple systems:SAP Functional locations Equipment AVEVA PI Tag metadata The target asset hierarchy data model contains the following views:Site Area Line Equipment System Subsystem TagThe source metadata arrives without the required treatment, standardization, or contextualization. Before writing to the final data model, the data needs to be cleaned, normalized, enriched, and structured according to the target hierarchy.Current ChallengeIn practice, the workflow needs several intermediate transformation steps before writing to the final views.For example, the workflow may need to transform SAP functional locations into a cleaned and standardized structure before deriving sites, areas, and lines.Example flow for Site: tb_functionalLocation -> tb_functionalLocation_curated -> tb_site_curated -> Site view Example flow for Area: tb_functionalLocation -> tb_functionalLocation_curated -> tb_area_curated uses tb_site_curated for contextualization -> Area view Example flow for Line: tb_functionalLocation -> tb_functionalLocation_curated -> tb_line_curated uses tb_area_curated for contextualization -> Line view Example flow for Equipment: tb_equipment -> tb_equipment_curated -> tb_equipment_contextualized uses tb_line_curated / tb_area_curated for hierarchy mapping -> Equipment view Example flow for System and Subsystem: tb_functionalLocation + tb_equipment -> curated functional location and equipment tables -> tb_system_curated -> System view tb_functionalLocation + tb_equipment -> curated functional location and equipment tables -> tb_subsystem_curated -> Subsystem view Example flow for Tag: tb_tag -> tb_tag_curated -> tb_tag_contextualized uses equipment/system/subsystem curated data -> Tag view These intermediate curated tables are useful because they allow the workflow to:Reuse cleaning and standardization logic across multiple transformations. Avoid duplicating the same transformation logic in every step. Avoid using the final data model views as inputs to transformation logic. Keep the workflow logic easier to understand and maintain. Separate raw source data, intermediate workflow state, and final modeled data.However, this intermediate data can be transient. It is only needed while the workflow is running. After the workflow finishes successfully, the data can be deleted. There is also value to optionally allow users to view this intermediate datasets to analyze/debug the quality of the contextualization. Today, we can use RAW tables as intermediate storage, but this creates additional responsibilities for the customer:Creating and maintaining temporary RAW tables. Cleaning intermediate tables before or after each workflow run. Preventing stale intermediate data from being reused accidentally. Managing naming conventions for temporary workflow data. Adding cleanup steps to the workflow. Handling failed workflow runs where temporary data may be left behind. Writing additional code that is not part of the actual business transformation. Product IdeaIntroduce a native workflow-managed temporary storage capability in CDF.This could work as an internal temporary storage layer for workflows and transformations, where intermediate datasets can be written and read by different workflow steps, but their lifecycle is managed by the workflow execution itself.Ideally, this temporary storage would be:Scoped to a workflow or workflow run Usable by transformation steps Automatically cleaned up after successful execution, while still allowing end users to later review intermediate datasets for debugging. Temporarily retained for debugging and/or review Separated from RAW and from the final Data Modeling views Managed by CDF instead of customer-maintained cleanup logic Expected BenefitsThis capability would make workflow-based data modeling pipelines much cleaner and easier to maintain.The main benefits would be:Reduced amount of customer-managed code. Less duplication of transformation logic. Cleaner separation between raw data, temporary workflow state, and final modeled data. Reduced risk of stale intermediate data impacting future workflow runs. Easier debugging and monitoring of workflow execution. More straightforward workflow design for complex contextualization processes. Better support for multi-step data preparation before writing to Data Modeling views.
Example document from the pdf preview in fusion. There is a rotate symbol, but the rotate symbol does not rotate the image, but it resets the view to “fit to full page”. How do I rotate the documents previewed? or do i need to download it and do it an do it in a native app? And if not so, can rotation functionality in the document viewer? and maybe change to icon of the rotate symbol and also maybe remember the rotation of the page in the viewer aswell? cause there are often documents and diagrams combind in a single pdf file?
Hi all,The point is to get the possibility to enlarge the All Categories column in the Search. This should be useful if views names are too long to facilitate the navigation.
When using the "scheduled calculation" function in Charts, there is a situation where you can select either "CDF sign-in credentials" or "CDF Client ID and Client secret" as the authority to perform the calculation. Of these, "CDF Client ID and Client secret" is supposed to be selected in order to perform "scheduled calculation" stably, but general users do not know "CDF Client ID and Client secret", so they cannot use it casually.Therefore, I would like to be able to perform "scheduled calculation" stably even if "CDF sign-in credentials" is used. In that case, there is no situation where you want to use "CDF Client ID and Client secret", so you don't need to select this permission in the first place, and I would like you to be able to perform all "scheduled calculation" with "CDF sign-in credentials".
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