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In computer programming, duplicate code is a sequence of source code that occurs more than once, either within a program or across different programs owned or maintained by the same entity. Duplicate code is generally considered undesirable for a number of reasons. [ 1 ]
Target deduplication is the process of removing duplicates when the data was not generated at that location. Example of this would be a server connected to a SAN/NAS, The SAN/NAS would be a target for the server (target deduplication). The server is not aware of any deduplication, the server is also the point of data generation.
SEMMA is an acronym that stands for Sample, Explore, Modify, Model, and Assess. It is a list of sequential steps developed by SAS Institute, one of the largest producers of statistics and business intelligence software. It guides the implementation of data mining applications. [1]
duplicate code detection (e.g.) [12] code. Polyspace: 2022-09-15 No; proprietary Ada C, C++ — — — — — Uses abstract interpretation to detect and prove the absence of certain run time errors and dead code in source code as well as used to check all MISRA (2004, 2012) rules (directives, non directives). Pretty Diff: 2019-04-21 (101.0.0 ...
SAS macros are pieces of code or variables that are coded once and referenced to perform repetitive tasks. [8] SAS data can be published in HTML, PDF, Excel, RTF and other formats using the Output Delivery System, which was first introduced in 2007. [9] SAS Enterprise Guide is SAS's point-and-click interface.
For example, appending addresses with any phone numbers related to that address. Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [ 2 ] and transforming it into one cohesive data set; a simple example is the ...
"Don't repeat yourself" (DRY), also known as "duplication is evil", is a principle of software development aimed at reducing repetition of information which is likely to change, replacing it with abstractions that are less likely to change, or using data normalization which avoids redundancy in the first place.
One should check the success of the randomization procedure, for instance by checking whether background and substantive variables are equally distributed within and across groups. [ 121 ] If the study did not need or use a randomization procedure, one should check the success of the non-random sampling, for instance by checking whether all ...