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1195 Ideas

Feature Request: First and Last Value Aggregates for Granularity-Based QueriesNew

Hello Cognite Support Team, I'm working with the Cognite Data Points API and would like to request a feature enhancement regarding aggregation functions. Current Situation: When querying time series data with a specific granularity (e.g., "1d" for daily), the available aggregates (Sum, Average, Count, Interpolation, StepInterpolation, etc.) don't directly provide the first or last actual data point within each granularity period. Feature Request: Could you add first and last aggregate functions that would:first: Return the earliest data point (by timestamp) within each granularity period last: Return the latest data point (by timestamp) within each granularity period  Use Case Example: For a time series with granularity: "1d" and aggregates: ["first"], the API would return the first recorded value for each day (e.g., the value at 00:00 or the earliest available timestamp that day). Similarly, aggregates: ["last"] would return the last recorded value for each day (e.g., the value at 23:00 or the latest available timestamp). Current Workaround: Currently, we're fetching hourly data (granularity: "1h") and then manually filtering/grouping to extract the first or last value per day, which is less efficient for large datasets. Question: Are there any plans to add native first and last aggregate functions? If this feature is already available through a different approach, I'd appreciate guidance on the best practice.

Dinesh Makked
Practitioner
Dinesh MakkedPractitioner

Process-Aware Knowledge Graphs for Industrial AIGathering Interest

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.