From Data Historian to Insight: Scalable Time-Series Workflows in JMP (2026-US-30MP-2952)

Industrial time-series data is growing rapidly in both volume and complexity, driven by widespread use of systems such as the PI System and AspenTech IP.21. JMP’s Data Historian integration enables analysts to connect directly to these systems, bringing high-frequency, multistream data into JMP for interactive exploration and analysis.

This presentation demonstrates practical workflows for selecting, importing, and structuring time-series data in JMP. Attendees learn how to work with both PI Asset Framework hierarchies and AspenTech tags, including techniques for organizing related data streams using stacking and other structuring tools.

We also preview upcoming capabilities that improve scalability when working with large numbers of data streams, including more efficient handling of high-volume imports and new approaches to stream selection. These enhancements include improved PI tag-based workflows and server-assisted filtering.

Many of these enhancements were driven by feedback from Early Adopter customers. This session provides both practical guidance for current workflows and a forward-looking view of how JMP continues to evolve to support large-scale industrial data environments.