Community Discussions

Hi All, I’m Stig Harald Gustavsen. For the past 15 years, I have been spending a third of my time on the Valhall oil platform. I work there as a metering technician/engineer. My daily tasks are production allocation, operating, calibrating, and maintaining the fiscal and analytical measurement systems.I’m incredibly passionate about Open Industrial Data (OID) and the concept of data sharing in general. I feel there is too little sharing of the data and information within heavy asset industrial sectors, mainly due to risk aversion. Humanity as a collective needs solutions on the industrial scale. When we withhold information, we hinder creativity and miss out on opportunities that we could leverage to create a better future together. A great example is how the internet has democratized information sharing in the commercial IT world. The more heavy asset-oriented industrial world has yet to go through this reformation. Still, I hope and wholeheartedly believe the OID is the start of this

we are facing issues while using cognite functions. Throwing an error Time out. please find the below screenshot. We are facing this from 1hr. Function ID 2060650160785332.This function was running successfully before this issueAttached is the screenshot image.

 we are facing issue while deploying the functionMessage: Function deployment failed.Trace:('Process-13 terminated unexpectedly with exit code 1 while running job.', 1)Traceback (most recent call last):File "/app/.venv/lib/python3.7/site-packages/cognite/processpool/processpool.py", line 81, in resultret, err = pickle.loads(self.recv_q.get(block=False))File "/usr/local/lib/python3.7/multiprocessing/queues.py", line 107, in getraise Empty_queue.Empty During handling of the above exception, another exception occurred: Traceback (most recent call last):File "/app/.venv/lib/python3.7/site-packages/cognite/processpool/processpool.py", line 163, in _job_manager_threadresult = worker.result()File "/app/.venv/lib/python3.7/site-packages/cognite/processpool/processpool.py", line 91, in resultself.process.exitcode,cognite.processpool.processpool.WorkerDiedException: ('Process-13 terminated unexpectedly with exit code 1 while running job.', 1)

In the spirit of summer reading- here’s a pretty interesting blog post covering 5 emerging challenges in commodity trading. What similarities/differences/additional challenges apply to power trading? Anything missing here?https://www.cognite.com/en/blog/commodity-trading-data-challenges1. Rapid increases in the number and availability of new data sources are accelerating the complexities of managing data and analytics in global markets2. Reliance on legacy systems means participants in high-paced commodity markets struggle to make data relevant and actionable3. Participants in commodity markets need to accelerate their data management and digital development just to keep up with technological improvements4. Existing data solutions in commodity markets are often designed as one-stop single solutions or platforms, with little room to create a proprietary competitive edge5. Continued growth in market complexities will require that IT platforms and data architectures are designed to remain

Browser is none too happy: https://github.com/cognitedata/auth-wrapper/issues/19

We have an equipment with name PI91111 on the P&ID and on the other hand we have this equipment in the asset Hierarchy.PNID Diagarm This equipment is not getting contextualized for this P&ID.I have used standard model and advanced model with min tokens 1 & 2 as well.The ocr output for this is below: 

We are using the Cognite function , to run the Contextualization Jobs. But we are frequently facing this issue.CogniteAPIError: Bad Gateway | code: 502 | X-Request-ID: 001dfc87-9b02-9b69-8144-3d829c126cbc.  could you also assign the ticket to the below emails ,where we can track the status.morten.nesvik@cognitedata.comben.petree@cognitedata.comphilippe.bettler@cognitedata.com

Cognite Data Fusion is a product built to address the challenges of working with industrial data by: Making data available - Liberate their IT, OT, ET and visual data from siloed source systems with our extractor pipelines. This is done reliably and at scale. Making data meaningful - We use AI-powered contextualisation services to create an Industrial Knowledge Graph that delivers trusted, contextualised data Make data valuable - Cognite Data Fusion enables your teams to access this data with the best-of-breed tools of your choice to turn this data into business value Monolith solutions often end up creating vendor lock-in and can even end up creating more data silos within your organisation With trusted, contextualised data available in an industrial knowledge graph, your teams are equipped to scale solutions both in the volume of new solutions and replicating successful solutions across assets, lines, or sites.The videos are based on the “ice cream factory” use case: a use case

Asset ID not getting removed from file  when you delete any annotations/boundaries on a file.when you manually delete any contextualization result on a file, only the annotations are getting deleted , but AssetId’s linked to that file still exists.  

In CDF, when we click on any Asset , there is a file tab, where it shows Appears In and Linked files for that asset.But the Count is not matching with list of files its showing for Appears In.In the below screenshot, the appears in count showing as 20. But only 16 files were listed. 

Hi, In Cognite AIR, I have put in my phone number and e-mail adress. According to the instructions, this should give me notifications on SMS rather than e-mail. However, I still receive (somewhat delayed) notifications on e-mail.Is there a way to test the SMS functionality to confirm that it is actually working?

Below we have outlined several frequently asked questions and their corresponding answers.Don’t see the answer to your question? Post as a reply in this thread and we’ll be sure to answer and/or add it to the FAQ list below!Protip: Use [cmd+f] or [cntrl+f] to search for keywords related to your question. FAQs How do i use monitoring in CDF and access the documentation?You can access the documentation for the new monitoring solution in CDF here: https://docs.cognite.com/cdf/charts/#monitoring.  

Background: A production facility had numerous valves that were being opened and closed at varying rates and amounts. The subject matter expert at hand wanted to be alerted when the pressure values of these valves exceeded or dropped below a certain value as set by the subject matter expert.Problem: Today, the majority of valve maintenance is being done according to a fixed schedule. Without AIR and CDF the pressure sensors on these valves would have to be manually read and then converted into an excel spreadsheet. Once the data has been extracted into excel the subject matter expert had to analyze this data and figure out if the thresholds were being breached. If they were then the maintenance is done in order to prevent accidents. As it can be inferred this process was very manual and not easily scalable. The subject matter expert  also wastes their time on tedious tasks as compared to actual important tasks.Solution: Using AIR a data scientist is able to easily define and deploy a t

Hello Cognite Community! Izabela Hawrylko here, Cognite’s Partner Development Director working with our Application and Industry partners. In my day-to-day work, I’m joining forces with other digital disruptors to build “Partner+Cognite” technical and go-to-market strategies for end-to-end industry solutions solving most pressing industrial challenges.I can imagine you might be wondering who Open Industrial Data (OID) is for. Anyone who is an industrial data enthusiast. Curious mind interested in better understanding the industrial reality. Are you an Independent Software Vendor building solution and applications for heavy-asset industries? Market Incumbent looking for data to experiment and drive innovation for your products and services? Researcher? Student? We have created this project for you to explore real-life industrial data streams and experiment with analytics, building new algorithms and creating visualizations.Join hundreds of other industrial data enthusiasts and dive into

Cognite Data Fusion is a product built to address the challenges of working with industrial data by: Making data available - Liberate their IT, OT, ET and visual data from siloed source systems with our extractor pipelines. This is done reliably and at scale. Making data meaningful - We use AI-powered contextualisation services to create an Industrial Knowledge Graph that delivers trusted, contextualised data Make data valuable - Cognite Data Fusion enables your teams to access this data with the best-of-breed tools of your choice to turn this data into business value Monolith solutions often end up creating vendor lock-in and can even end up creating more data silos within your organisation With trusted, contextualised data available in an industrial knowledge graph, your teams are equipped to scale solutions both in the volume of new solutions and replicating successful solutions across assets, lines, or sites.The videos are based on the “ice cream factory” use case: a use case

 Hello Charts Early Adopter Community,There have been several new features and functionalities released lately, with some being released just today. I’ve recorded a video (below) to explain several of these core features in detail, namely – calculations running on individual data points, not aggregates.You can also scroll down to read the written details.Please do leave comments below with questions and feedback!DetailsCharts UI/UX Calculations are now running on individual data points, not aggregates In the past, calculations have been running on time series aggregates. This meant that, although calculations were approximately calculations were often not correct his is a very important new feature that greatly increases the accuracy and trustworthiness of calculation results in Charts. Watch the above video or see the slides, below, for more info. If you receive a warning that looks like this (see image, below), then it means that the results of the data has been downsampled to perfo

WhyWhen starting out in Cognite Data Fusion (CDF) project, it's natural to start by creating data governance elements like groups, datasets, and Raw databases from the CDF user interface. But as the solution begins to scale, you'll quickly realize that it is demanding to set up a detailed configuration handling multiple solutions, sources, roles, and other dimensions. For scaling, precise control is needed for access management and data governance and to enforce the guidelines and rules across the solution. The problem is not trivial, and a good way to solve it is by replacing the manual approach with a configuration-driven system, where the configuration language supports higher-level concepts for data lineage and access control. With configuration files as the foundation, you can set up an automated DevOps process and review and approve any changes to the structure before they are deployed. This approach also dramatically simplifies sharing the same configuration across multiple envi

Just stumbled upon an article from McKinsey in 2019 which articulates well what I believe is a crucial role at many of the customers I’ve worked with: the Analytics Translator. The technology in industry is continuing to move forward quickly opening up many new opportunities, but the deep domain expertise in production processes, maintenance, and more is still as important as ever. To me, the Analytics Translator role seems a perfect bridge between these two worlds. I’ve seen a number of manufacturers successfully “upskill” prior machine operators, process engineers, and more to fulfill this role. It’s always very impactful, making for significantly more effective digital initiatives. From my personal experience, I see them doing things like:Running use case workshops and prioritizations Internal training of users Adoption tracking Interim project manager during new technology initiatives Liaison with external technology partners and SIs Curious how others see this role, and whether yo

Cognite Data Fusion is a product built to address the challenges of working with industrial data by: Making data available - Liberate their IT, OT, ET and visual data from siloed source systems with our extractor pipelines. This is done reliably and at scale. Making data meaningful - We use AI-powered contextualisation services to create an Industrial Knowledge Graph that delivers trusted, contextualised data Make data valuable - Congite Data Fusion enables your teams to access this data with the best-of-breed tools of your choice to turn this data into business value Monolith solutions often end up creating vendor lock-in and can even end up creating more data silos within your organisation With trusted, contextualized data available in an industrial knowledge graph, your teams are equipped to scale solutions both in the volume of new solutions and replicating successful solutions across assets, lines, or sites.The videos are based on the “ice cream factory” use case: a use case

Today’s topic is about interacting with Cognite Data Fusion (CDF) from Android devices. Android devices can be phones, but also other kind of devices like hands-free devices (with speech commands). Since Kotlin, the Android preferred language, has interoperability with Java, it facilitates the integration of interactions with CDF in your Android apps. You can indeed use our Java SDK in an Android application, which makes it really easy to retrieve and upload data from/to CDF. The Cognite Java SDK is complete and maintained. This can broaden your thoughts/ideas about what to do with CDF. Below a few examples of what can be done : Retrieve information from CDF to display it on your mobile device Upload data to CDF from a mobile device, which can be useful in an industrial context Using specific features, from mobile devices (like a camera for example) to upload particular data to CDF (raw, or to extract features from them with an ML model, thanks to a Cognite function for example)   

Hi, Just wanted to know if there's any way to use Cognite UI Apps instead of we developing custom UI App. If yes, could you please let us know the process and how to use it? 

We have identified a bug in the time series datapoints fetcher in the Python SDK which may cause duplicated timestamps to be returned. Affected versions are 2.47 through 2.51. Please check if you have anything deployed using the datapoints API in this version range. If you do, please upgrade to >=2.52 ASAP.https://github.com/cognitedata/cognite-sdk-python/blob/master/CHANGELOG.md

This is how it looks like when you push geospatial to the limit and show 4+ billion essential ocean variables (temperature, salinity, pH, oxygen etc) captured from 220,000 research cruises from 1890-YTD. Such data is very important to understand the effects of climate change, for instance ocean warming, deacidification, dead-zones (lack of oxygen), biodiversity migration and more. In the Ocean Data Platform, we have implemented something called the “Ocean Data Connector” which is a cloud-based JupyterHub infrastructure where you can analyze this data in a very efficient way.Ocean Data Platform and the “Explorer” interface 

On 8th March our amazing data scientists at HUB Ocean and Cognite gave a workshop at the Woman in Data Science (WiDS) event in Oslo. Have a look at the video and blog post. The data challenge was related to coral reef bleaching events applying open data on the Ocean Data Platform.Closing the gender gap in Ocean Science? — HUB Ocean | Dedicated to Unlocking Ocean Datahttps://www.linkedin.com/posts/hubocean_coralreefs-biodiversity-collaboration-activity-6928641478181154816-IImk?utm_source=linkedin_share&utm_medium=member_desktop_web

As we announced in our last release post, we recently updated our calculations backend to run on individual data points if the total count of data points is less than the predefined maximum limit.(You can watch the video walkthrough for more information.)When this improved functionality was released a few weeks ago, this limit was set at the intentionally low value of 10,000 individual data points. We did this to test our infrastructure, gather feedback, and ensure our backend will not crash with these more expensive requests — Thanks to those of you who have provided us with input!As of today, we have released an update which increases this limit from 10,000 data points → 100,000 data points.This 10x improvement in performance will help to provide accurate, trustworthy calculation results for larger ranges of time and data. With this new 100k limit, we’ve reached the maximum number of data points we can retrieve from the Cognite Data Fusion Time series API with a single request.While