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Feed Compositions - Context Items or Individual Tags?

  • February 17, 2026
  • 2 replies
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yracette
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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 the possible use of context items with these tools.

Best answer by Wouter Daniels

Hi Yves,

I think your idea is correct here. Context items can offer nice features, but for root cause analysis as you describe, I think you definitely need that data as tags. Indeed the time shifts and cross correlations would not be possible with context items. We are working on removing this gap that exist between context item data domain and the tag time series data domain, but today, connecting as tag data would be the clear choice for your use case.

Since your data seems nicely structured, you could consider adding an asset framework on top of this new time series data, either automatically with a connection to the same database, or built manually within TrendMiner. This could help adding more structure in TrendMiner.

Also, if there is a good reason to do so, there would be nothing stopping you from adding the same data twice: once as tags and once as context items.

Best regards,

Wouter

Yves Racette

2 replies

Wouter Daniels
Employee
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  • Wouter Daniels
  • Employee
  • Answer
  • February 18, 2026

Hi Yves,

I think your idea is correct here. Context items can offer nice features, but for root cause analysis as you describe, I think you definitely need that data as tags. Indeed the time shifts and cross correlations would not be possible with context items. We are working on removing this gap that exist between context item data domain and the tag time series data domain, but today, connecting as tag data would be the clear choice for your use case.

Since your data seems nicely structured, you could consider adding an asset framework on top of this new time series data, either automatically with a connection to the same database, or built manually within TrendMiner. This could help adding more structure in TrendMiner.

Also, if there is a good reason to do so, there would be nothing stopping you from adding the same data twice: once as tags and once as context items.

Best regards,

Wouter

    yracette
    Pioneer
    Forum|alt.badge.img+2
    • yracette
    • Author
    • February 18, 2026

    Wouter, 

    Thank you very much for taking the time to analyze and help me on the matter.

    This comforts me in my decision to primarily use tags, but also consider collecting context items as well.

    I also like the idea of creating an asset structure to organize this data.

    I much appreciate your support.

    Yves

    Yves Racette

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