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We would like to have a contains filter option on columns. This would be useful to search on string type columns and see if it contains some substring, to filter the data properly.
Need the ability to filter time series data on "Last Reading" column in search. The field is not Filterable or Sortable. Neither is it configurable in the Search Admin.
Hello, Today if we reference a node in a data model instance to create a relationship between two nodes, the referenced node existance is not validated at insertion level. In order to enforce data quality, do you envision to add a “strict mode” at the DMS level that checks for referenced node existence before creating the reference? i.e:Running this query results in a successful transformation runselect 'test' as externalId, 'test' as space, node_reference('space', 'external_id_that_does_not_exist') as Child, from staging_tableThanks!
Hi,Would it be possible to extend the ReverseDirectRelation concept to populate from a DirectRelation list, and not just a DirectRelation ? Best regards, Olivier
I am using Cognite SDK where I need to count instances present in a given View. I can list all instances but thats heavy response and might be heavy execution backend side as well. I need only total count of instances which if run on cognite side and return in numbers may be lighter and quick as well. It would be good if I can apply some filters as well and get respective count.
Customers using the AI Agent can generate insights in the form of:PNGs (charts, visualizations, cause maps) Tables (structured results)However:There is no supported, documented way for the AI Agent to directly push these outputs into Canvas. Users must manually download images or recreate tables, then upload or rebuild them in Canvas. This breaks workflow continuity and reduces the usability of AI Agent outputs in collaborative analysis.Proposed EnhancementIntroduce a native capability for AI Agents to:1. Push generated PNGs directly into Canvas as embedded visuals.2. Push generated tables into Canvas, ideally as:A Canvas DataGrid (when mature), orA structured table block that preserves rows, columns, and formatting.This could be exposed as:An “Add to Canvas” / “Send to Canvas” action from the AI Agent output. A programmatic API available to agents and workflows.
Inspiration“Context is king in the world of AI.”Across research, publications, and industry discussions, one theme consistently stands out — AI without context lacks true intelligence. To unlock the full potential of Industrial AI, we must ground AI solutions in process context.VisionIntroduce Process-Aware Knowledge Graphs (PAKGs) that integrate process understanding directly into the Cognite Data Fusion (CDF) ecosystem. By capturing and structuring the interconnections, interdependencies, and material flows from Process Flow Diagrams (PFDs), we enable context-driven intelligence for Agentic AI solutions built on Atlas AI and CDF.Core Capabilities System Model Extraction Automatically extract process metadata from P&IDs and PFDs (PDF/Image formats). This removes the dependency on CAD files, which are often unavailable or inconsistent. Process-Aware Knowledge Graph Generation Translate the extracted system model into a Knowledge Graph enriched with process semantics. Represent equipment, process streams, and control loops as nodes and relationships, creating a foundation for process discovery, reasoning, and autonomous insights. Value Proposition Enables Agentic AI systems to reason over process context. Accelerates ROI realization from Cognite solutions by improving AI explainability, traceability, and domain relevance. Lays the groundwork for next-generation Industrial AI applications — from automated root cause analysis to process optimization. AskI propose enhancing CDF to support this capability natively, creating a bridge between engineering documentation and context-aware AI models.
Problem StatementCognite Data Fusion (CDF) offers a powerful suite of tools for industrial data operations, but its adoption remains limited to highly technical users such as data engineers, data scientists, and developers. Today, creating data transformations, writing functions, deploying models, and generating insights in CDF typically requires:Knowledge of Spark SQL for transformations Python programming for custom functions Understanding of data modeling concepts Manual deployment and orchestrationThis steep technical barrier restricts broader usage, particularly among domain experts like production operations engineers, maintenance supervisors, or process owners who possess deep contextual knowledge but lack coding skills. As a result, CDF usage and ROI are throttled by dependence on a small pool of technical resources. VisionEmpower every domain expert to become a CDF power user — without writing a single line of code. Proposed Solution: Cognite vision – AI-Powered No-Code ExperienceIntroduce Cognite VISION, an out-of-the-box AI agent integrated into CDF that uses LLMs to eliminate the need for coding expertise.With VISION, a user can simply ask:"Join sensor data from the compressor with maintenance logs and create a dashboard to predict downtime every 6 hours."VISION handles the rest:Interprets the intent using an LLM Writes Spark SQL transformations behind the scenes Creates and deploys Python functions for processing or inference Builds contextualized data models Schedules pipelines Deploys insights to dashboards or external appsAll within seconds, fully auditable, and explainable for enterprise transparency. Key Features Natural Language Interface: Ask for transformations, models, or dashboards in plain language Automatic Backend Generation: LLMs write code, configure parameters, and deploy pipelines One-Click Deployment: From request to production in a few clicks or a single prompt Insight Builder: Automatically recommends and generates insights based on domain context Governed Execution: Every AI-generated artifact passes through existing governance and logging frameworks
Hi Team,This request is to include time series’ UnitExternalId on UI/front-end under the time series information in the data explorer/search.Currently, it is visible using transformations, APIs or SDK. But someone who is not adept to use transformations, APIs or SDK i.e. the target persona is an SME, displaying the UnitExternalId on the UI is more suited.Thanks,Akash
One of the customer would like to see the preview of 2d plot plants or PIDs in Search just like we see the panel for 3d models.
Is it possible to have a set of pre-built templates for various disciplines or generic use cases, which a new user can directly utilize for preliminary work, until he get trained enough to start building his canvas. Some of the users like to use the industrial canvas like a dashboard and for them having a pre-built template gives an idea of how to use the canvas.
We recently did a backup of all configurations for a solution in both our dev and test project. For this we used `cdf dump`. The command gave us alot of options on what to dump. And for each resource, we needed to provide an id. However most of the resources we used were scoped to a cdf-group.So the tool was built with a group as a basis for fetching all the resource id’s and constructing the `cdf drump` commands. So my feature request is to have an ability to dump all resources scoped to a cdf group with the `cdf dump` command. So you would only specify the name of the cdf-group to base the resource dumping on. This would simplify backup and dumping alot
Today, the PI extractor can only write time series instances to a single target space per deployment, even when the underlying PI server contains data for multiple sites or governance domains. The only practical workaround is to run many PI extractor instances against the same PI server, each with different tag filters and a different target space, which is hard to scale and operate. [Governance & spaces; Multi-space limitation]I would like the PI extractor to support multiple target spaces from a single deployment, where the space is selected per time series based on configurable filters. Typical examples would be routing by tag name prefix or pattern (for example, ABU* to space site-abu, RUW* to space site-ruw), or by PI point attributes / metadata mapped to specific spaces. [Filter-based routing idea; Enterprise scaling concern]. This capability would avoid both the operational overhead of many parallel extractor instances.
Celanese is asking for a UI that they can see data and views and their fields within CDF. They currently use the data modeling instances page as the UI to see the data but as they migrate to S&R, they do not want to lose that functionality all together (i.e. they mentioned a year is too long to have this functionality). While it was mentioned that the streamlit app is available, they would like to have a counterpart UI to what we have in data modeling for S&R.
In our 360 images, the circles indicating shooting positions are quite large, and when multiple markers are displayed, they tend to obstruct the view.I have several ideas, but the most practical and system-friendly suggestion would be to make the “invisible distance” setting stricter.It would be even better if this parameter could be configured on the system side. Currently, I feel like markers are visible from a very long distance.Other ideas include enabling size adjustments for the circles or adding an option to toggle their visibility on and off. However, I believe the first suggestion would be the easiest to implement. Note: Due to internal security reasons, we cannot share 360 plant images online. For illustrations, I’m using a black image during loading. Please imagine your own plant with your mind’s eye! 😊
We would like the ability to share charts publicly (great feature) but also have a feature where i can select the specific people to share the charts with me. There should be a feature called “shared with me” similar to google drive concept.
Scaling the y-axis to show the whole graph rather than scrolling. Needing to scroll to be able to see the x-axis is not very user friendly. If the chart doesn’t fit the frame then the chart should be resized rather needing to scroll. Markus PettersenAker BP - Data Platform Architect
The stamp feature in Canvas is difficult to use from users in Japan. The main reason is that the symbols we commonly use are not available and the shapes are not unfamiliar.This stems from the fact that Japan uses a standard called JIS, which defines the standard symbols used in piping and instrumentation diagrams. (In the U.S., this would be like ASME and ANSI.) “Add JIS-defined symbols” could be a solution, but many other countries also have their own standards.Supporting each of them individually would be impractical.Additionally, different industries outside of oil & gas may require different sets of symbols. Therefore, I would like to suggest adding a feature that allows users to upload their own custom stamps.More specifically, we could upload an image with specified a size to a dataset and use it as a stamp.The reason for specifying a size is that very large images would not be appropriate as stamps. Something around 40×40 px seems reasonable. It would be great if you could consider this.
When creating new timeseries via calculations, the user has to manually input the unit of the resulting timeseries. This can lead to human errors and is also a repetitive task.Aim:Depending on the units of the sources and the calculation function, infer the unit of the resulting timeseries. Potentially ask the user for confirmation. Display the timeseries units within the calculation nodes.Value:Expedite the calculation creation Enhance the user experience through automation Limit data entry errors
Celanese - CriticalThe ability to overlay other transactions on time series data or create time series from transcations is necessary when troubleshooting. For example, i might be analysing several time series tag and i would like to plot work order and notification dates and/or other events on the same plot. This will really help us do better Root Cause Analaysis.
Activities and Operations currently both support the isInApp flag. Would be great to have this implemented for Materials as well. Would be very much helpful in managing the materials that need to be hidden from Maintain.
As a Cognite admin responsible for operating extractors at PBF, I want Cognite extractors and tooling to support CyberArk as a secrets provider (in addition to or instead of Azure Key Vault), so that we can reuse our standardized enterprise secrets management platform without introducing Azure Key Vault as a parallel solution, avoiding architectural disruption and policy exceptions. CyberArk: https://docs.cyberark.com/privilege-cloud-standard/latest/en/content/privilege%20cloud/privcloud-introduction.htmhttps://docs.cyberark.com/privilege-cloud-standard/latest/en/content/privilege%20cloud/privcloud-introduction.htm
An area, section, or banner detailing changes that have happened, within a user’s domain, since the last time they logged in. For example, a top 5 list of newest/hottest/top canvas’s, charts, custom apps. Akin to “what you have missed since you last logged in”. This provides information to the user on critical work they may have been involved in (a collaborative canvas they have been involved in has been updated) or a notification about new work posted (a canvas + chart detailing insights an overnight anomaly). This would not cover subjects that would be noted under Cognite’s “Release notes”.
Need (NEED) to be able to take pictures OFFLINE. This is critical for operators to use since their routes are in areas where signal can be lost.
When using the search function in industrial tools, the system currently checks whether the search term appears in any metadata fields such as Name, Description, and many others, then returns the results accordingly. This is a great feature; however, there are cases where users want to search only within specific metadata fields, such as Name or Description.Is it possible for users to manually specify which metadata fields to include in the search? In a previous tool (Data Explorer), we had a similar capability, so we are hoping for an enhanced version of that functionality.
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