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dataPARC has been working with time-series data from the outset as an essential component of process engineering. The new data historian was designed to allow customers to harness machine data in new ways:
Often, time-series data is proprietary to a specific equipment or part of an automation vendor’s larger suite of applications, making it difficult to apply that data in third-party applications. Democratizing historian data with an open system gives manufacturing intelligence teams more flexibility and the ability to leverage their data in other systems.
dataPARC customers will see an increase in performance speed, cutting load times in half for custom calculations. Customers can also use dataPARC historian data to support AI and machine learning modeling in the cloud. For example, a predictive use case may have historian data transferred to the cloud for analysis and the modeled data delivered back to manufacturing operations management applications to highlight the source of potential or recurring maintenance or process upsets.
Another use case is to allow stakeholders to quickly access data regardless of where it resides. The goal is for every data call to perform as though the user is local to the data historian, ensuring that remote experts assisting multiple industrial plants experience the same performance as local plant engineers. With an emphasis on openness, the overall architecture allows for easy integration of other vendor historians and provides an SDK for third-party applications, ultimately enabling customers to efficiently leverage their existing and future investments.