Define Condition-Based Capsules

There is a clear need for a native way to organize and classify time‑series data into reusable, condition‑based time segments (“capsules”) representing distinct operating states or events.
Capsules enable users to define such periods based on signals, logic, or events (for example when a valve is open, a pump is running, a compressor is loaded, a system is in start‑up, or production is above a given threshold), and then apply analysis consistently within those time windows.

This functionality is missing in Cognite Data Fusion, making it difficult to perform structured, state‑aware analysis without repeatedly rebuilding filters or custom logic. The problem occurs frequently during process optimization, production and injection accounting, energy and mass balance calculations, equipment performance analysis, and troubleshooting, where engineers need to calculate KPIs such as integrated flow, average efficiency, chemical consumption, or loss rates only during relevant operating conditions.

Without a capsule‑like abstraction, analyses become hard to scale, difficult to maintain, and less transparent, increasing the risk of inconsistent results across users and studies and reducing overall analytical efficiency.

What impact would a change have?

• Time savings
• Better data quality
• Fewer errors / less manual work
• Better decision-making foundation

Capsule‑based time segmentation would have a major impact on engineer productivity by significantly reducing the time and effort spent on repetitive data preparation. Engineers are typically both time‑constrained and pragmatic: if an analysis requires excessive manual filtering, context rebuilding, or custom logic, it will either not be done at all or be done in other tools that already support this efficiently. Capsules allow engineers to define operating contexts once and reuse them across analyses, enabling faster root cause investigations by isolating comparable operating states and eliminating irrelevant data early. In day‑to‑day work, this directly translates into time savings for common tasks such as integrated volumes, efficiency tracking, loss analysis, and conditional KPIs, making it feasible to perform deeper analysis within Cognite Data Fusion rather than exporting data elsewhere. Without this capability, CDF risks being used primarily for data access and visualization, while detailed engineering analysis is performed in platforms that better support state‑based analytics.

Introduce a native capability in Cognite Data Fusion to organize time‑series data into reusable, condition‑based time intervals (“capsules”) that represent operating states, events, or periods of interest. These capsules should allow engineers to easily segment data based on process conditions or logic and reuse the same time windows consistently across analyses, metrics, and investigations. The goal is to enable fast, context‑aware analysis where calculations and KPIs can be evaluated only during relevant operating conditions, reducing repetitive manual filtering and lowering the effort required to perform meaningful engineering analysis. This improvement would make Cognite Data Fusion a more efficient and attractive environment for daily engineering work and root cause analysis, rather than primarily a data access layer feeding other analytical tools.

If this capability is not implemented, engineers will continue to rely on manual filtering, ad‑hoc logic, or external tools to perform state‑based and event‑based analysis, leading to unnecessary extra work and reduced analytical quality. Practical consequences include longer time spent preparing data, higher risk of inconsistent or incorrect results, and fewer root cause analyses being performed due to the effort required. In reality, time‑constrained engineers will avoid doing deeper trending and investigations in Cognite Data Fusion altogether if similar analyses can be performed more efficiently in other platforms. This results in lower adoption for advanced engineering use cases, increased frustration among users, and Cognite Data Fusion being perceived primarily as a data access or visualization layer rather than a tool for serious process analysis and decision support.

Requested by: ​@kasperdybvad