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Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container. The data can be collected from one or more sources and it can also be output to one or more destinations.
Decision tables are a concise visual representation for specifying which actions to perform depending on given conditions. Decision table is the term used for a Control table or State-transition table in the field of Business process modeling; they are usually formatted as the transpose of the way they are formatted in Software engineering.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Microsoft Excel is a spreadsheet editor developed by Microsoft for Windows, macOS, Android, iOS and iPadOS.It features calculation or computation capabilities, graphing tools, pivot tables, and a macro programming language called Visual Basic for Applications (VBA).
An MS-DOS command line, illustrating parsing into command and arguments. A command-line argument or parameter is an item of information provided to a program when it is started. [23] A program can have many command-line arguments that identify sources or destinations of information, or that alter the operation of the program.
In Python, the keyword def denotes the start of a function definition. The statements of the function body follow as indented on subsequent lines and end at the line that is indented the same as the first line or end of file. [30]
EnergyPlus [1] is a whole-building energy simulation program that engineers, architects, and researchers use to model both energy consumption — for heating, cooling, ventilation, lighting, and process and plug loads — and water use in buildings.
pull() { highest = list.get_first_element() foreach node in list { if highest.priority < node.priority { highest = node } } list.remove(highest) return highest } In another case, one can keep all the elements in a priority sorted list ( O (n) insertion sort time), whenever the highest-priority element is requested, the first one in the list can ...