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We recently upgraded to TrendMiner 2025.R3.0-15 and noticed that users with access to shared content can also delete it.In our previous PI Vision setup, deletion was typically restricted to higher-level accounts. Is it possible in TrendMiner to limit deletion rights to specific roles while still allowing broader create/edit access?Also, are there recommended best practices (permissions, folder structure, etc.) to prevent accidental deletion of shared content?

We have tags available where we monitor fa oilusage on a shift or Daily basis. After the shift the tags goes back to zero. I like to create a graph or table to monitor the oilusage per shift for the last 5 7 days

This tutorial guides you through pulling time series tag data from TrendMiner into Power BI or Excel using Power Query. It follows the same structure and authentication pattern as the TrendMiner ContextHub article, replacing the context item endpoint with the tag index data endpoint.We will cover the following steps:Finding the Time Series UUID of your tag. Setting up parameters to make the queries dynamic. Writing queries for API interaction. Configuring the output table. Step 1: Find Your Tag's Time Series UUIDThe API identifies tags by a UUID (Universally Unique Identifier), not by the tag name. You need to look this up once per tag. The easiest way is directly from the browser while using TrendMiner.How to Find the UUIDOpen TrendMiner in your browser (Chrome recommended). Open the Developer Tools by pressing F12, or right-click anywhere on the page and choose Inspect. Click the Network tab at the top of the Developer Tools panel. In the TrendHub, search for your tag in the “Search

TrendMiner’s PlantIntegrations can be installed as WebHost (IIS) or AppHost (self‑hosted). AppHost is a lightweight, simpler alternative that is easier to install, maintain and upgrade. While WebHost currently still provides more flexibility and security features. There is a difference in available features before version 2026.R1.0 and after 2026.R1.0 which is explained below. Deployment features before version 2026.R1.0: These are the most important differences between the two hosting options:Feature WebHost (IIS) AppHost (Self‑hosted) Installation IIS setup Simple exe, self‑contained Port configuration Flexible Fixed to 8001 Security TLS/HTTPS, Basic Auth Relies on network/proxy security Multiple instances X Single instance (fixed port) Best for Complex environments needing full control Lightweight setups or quick deployments  Summary: AppHost is currently ideal for small or lightweight setups, quick deployment, and simpler maintenance. WebHost is curr

Hi all Is there a way to use a single cursor across multiple views in DashHub, similar to the functionality available in PI Vision? See below snapshotsThank you in advancePeter

Dear all, I would like to display the downtime (in minutes) of one machine in a DashHub view. I created a Gantt chart that helps me quickly identify downtime on the line and the related root causes. The goal for the operator is to keep the number of events as low as possible. Similarly to the Event Count, I would like to have a count of total downtime (in minutes). Since the counter only seems to count ContextItems, I considered creating one ContextItem per minute whenever the machine is stopped. However, I’m not sure how to create a tag that tracks minutes, and I’m also wondering if there is a better approach.

You may notice that CPU and memory usage on a TrendMiner VM often stays low — sometimes below 25%. This raises the question: why do we have minimum recommend X GB RAM and X vCPU?Understanding Resource AllocationTrendMiner is a microservices-based application, where each component runs in its own pod within Kubernetes. Each pod has: Request: Minimum guaranteed CPU and memory. Limit: Maximum resources it can use under load. Kubernetes schedules (create & run) pods based on requests, not current usage. Even if a pod is idle most of the time, the VM must have enough capacity for all pods’ requests simultaneously. Visualizing the TrendMiner VMBelow is a simplified view of a TrendMiner VM hosting multiple microservices pods:  Each pod acts like a mini-VM running a single microservice. The TrendMiner VM acts like the hypervisor, guaranteeing resources for all pods at all times.Why Not Scale Down?Even if your TrendMiner VM doesn’t look busy, reducing CPU or memory can cause problems: Pod

I wish to create dashboards for all our plants (13 of them). I would like each dashboard to have a series of associated components for each of the major process units in the plant (anywhere between 10 to 30 major process units per plant): For each process unit, create:TrendHub view with the charge rate in comparison to limits and or planned rate (3 or 4 known tags from different connected data sources). Specify aliases for each plot line - save in a shared site\unit folder Value-based search for unit downtime. Search entire available history and exclude downtime periods + set a context item on the unit charge rate tag - save in a shared site\unit folder Dynamic filter based on the above value-based search - save in a shared site\unit folder Apply this dynamic filter to the TrendHub view Context view for the context items defined with the value-based search - save in a shared site\unit folder Monitor based on the downtime value-based search and add context Once this is created for all m

We are currently using the trendminer‑interface SDK  to create and populate custom tags (e.g. predictions / calculated KPIs). During implementation and testing, we have identified a major limitation that becomes critical at scale.When using: client.io.tag.save(tag_data_dict, index=False)to write data to an existing tag:Saving a new dataset overwrites existing historical data for that tag. It is not possible to append new time‑series data to an existing tag. Writing data for a new time range clears previously uploaded history.Although the documentation refers to this operation as an “update”, the actual behavior is a full overwrite of the time series, not an incremental append.Why this is a problem?Because appending is not supported, every  update would require:Re‑uploading the entire historical dataset for each tag Maintaining full history externallyThis leads to:Significant performance and bandwidth overhead Long execution times High operational risk (partial uploads or failures can w

If you’ve attended one of our VILT TrendMiner trainings, you’ve probably seen a popular workaround: creating a daily aggregation tag that looks discrete (a clean stepwise plot) and updates once per day at midnight. You may have also seen Natasha’s post where she discusses how to build the stepped daily tag.This is a simple but powerful technique when you want a tag that reports something like: Daily average Daily total Daily max/min Daily standard deviation Any other 24-hour aggregation you want to visualize as a “daily value” In training, we typically build this so the tag updates at midnight. But recently a user asked a great follow-up: can we build a similar tag that steps at a certain hour of the day instead of midnight?Good news! You can do this with a small modification to the same workaround. In this post, I’ll walk through this modified approach that lets you step your daily aggregation at the hour to your liking. Step the daily aggregation at 7:00 AM (or any hour)In th

I am looking for an advice.We have a SQL Server database that records the various compositions of the daily raw feeds to upstream units for each of our plants. The data is therefore by definition time-series and indexed by plant, unit name and raw feed type with a value of volume/day.Because these data items are related, there would be a natural fit with context items and fields.. They could also be ingested as individual tags with a unique naming convention by plant, unit, raw feed type.Our primary objective is to troubleshoot and perform root cause analysis for issues observed on downstream units based on the various daily raw feed blends. This definitely involves time shifts and also value-based searches + event analytics.Even though the data is naturally organized as event frames or context items, I am inclined to prefer ingesting this data as individual tags to enable diagnose/cross-correlations, event analyses and possibly fingerprint, unless there is something I am missing with