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In a data warehouse, a measure is a property on which calculations (e.g., sum, count, average, minimum, maximum) can be made. A measure can either be categorical, algebraic or holistic. A measure can either be categorical, algebraic or holistic.
In statistical quality control, an EWMA chart (or exponentially weighted moving average chart) is a type of control chart used to monitor either variables or attributes-type data using the monitored business or industrial process's entire history of output. [1]
Simple example of a process control chart, tracking the etch (removal) rate of Silicon in an ICP Plasma Etcher at a microelectronics waferfab. [1] Time-series data shows the mean value and ±5% bars. A more sophisticated SPC chart may include "control limit" & "spec limit" % lines to indicate whether/what action should be taken.
Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.
This complexity should be transparent to the users of the data warehouse, thus when a request is made, the data warehouse should return data from the table with the correct grain. So when requests to the data warehouse are made, aggregate navigator functionality should be implemented, to help determine the correct table with the correct grain.
Data Warehouse and Data mart overview, with Data Marts shown in the top right. In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is a core component of business intelligence. [1] Data warehouses are central repositories of data integrated from ...
Image source: The Motley Fool. First Solar (NASDAQ: FSLR) Q4 2024 Earnings Call Feb 25, 2025, 4:30 p.m. ET. Contents: Prepared Remarks. Questions and Answers. Call ...
The greater the risk associated with data use, the lower its value. The process of realizing value from data can be subdivided into a number of key stages: data assessment, where the current states and uses of data are mapped; data valuation, where data value is measured; data investment, where capital is spent to improve processes, governance ...