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NOV-DEC 2017

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INTECH NOVEMBER/DECEMBER 2017 17 PROCESS AUTOMATION ing popularity of ISA100 wireless instrumentation, provide another source of data. If a plant has 100 ISA100 transmitters, each broadcasting once ev- ery six seconds, the historian could receive tens of thousands of data points every hour. These data points include the process variable, diagnostics, alarms, and events. Modern historians no longer rely on a simple proprietary database stored on a local computer to deal with this big data. New SQL-based deploy- ment architectures now support the cloud and centralized and decentralized architectures—and can consolidate data from local to corporate-level historians. Various data-handling techniques are employed to deal with this big data, including filtering, time stamping, and combi- nations of flat-file and relational databases. For example, many of the data points coming from wireless instru- ments rarely change; therefore, the historian stores only those vari- ables changing outside a predefined range, a form of reporting by excep- tion. Devices themselves are becoming more intel- ligent, and with edge computing only the data that is of importance is given to the historian. Data to actionable information In the past, data from historians was primarily used to analyze processes and control functions. Engineers would write software to generate trends and graphs, and then try to visually ana - lyze this process historian data to spot anomalies and areas for improvement. Spreadsheet soft - ware was the tool of choice, but it took a lot of work and expertise to make this general-purpose tool perform this specialized task. In many cases, data scientists had to be engaged to assist. Figure 1. Data can flow into and out of a modern historian from a variety of sources.

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