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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 expansion of abbreviations ("st, rd, etc." to "street, road, etcetera").
In addition to basic equality and inequality conditions, SQL allows for more complex conditional logic through constructs such as CASE, COALESCE, and NULLIF.The CASE expression, for example, enables SQL to perform conditional branching within queries, providing a mechanism to return different values based on evaluated conditions.
In data mining and association rule learning, lift is a measure of the performance of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model.
Most schema migration tools aim to minimize the impact of schema changes on any existing data in the database. Despite this, preservation of data in general is not guaranteed because schema changes such as the deletion of a database column can destroy data (i.e. all values stored under that column for all rows in that table are deleted ...
Data-driven languages frequently have a default action: if no condition matches, line-oriented languages may print the line (as in sed), or deliver a message (as in sieve). In some applications, such as filtering, matching is may be done exclusively (so only first matching statement), while in other cases all matching statements are applied.
The best example of the plug-in principle, the bootstrapping method. Bootstrapping is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample, most often with the purpose of deriving robust estimates of standard errors and confidence intervals of a population parameter like a mean, median, proportion, odds ratio ...
The third normal form (3NF) is a normal form used in database normalization. 3NF was originally defined by E. F. Codd in 1971. [2] Codd's definition states that a table is in 3NF if and only if both of the following conditions hold: The relation R (table) is in second normal form (2NF).
Data conditioning principles can be applied to any demanding computing environment to create significant cost, performance and system utilization efficiencies, and are typically deployed by data center managers, system integrators, and storage and server OEMs seeking to optimize hardware and software utilization, simplified, non-intrusive ...