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Data validation is intended to provide certain well-defined guarantees for fitness and consistency of data in an application or automated system. Data validation rules can be defined and designed using various methodologies, and be deployed in various contexts. [1]
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]
An Open Schema implementation can use an XML column in a table to capture the variable/sparse information. [26] Similar ideas can be applied to databases that support JSON-valued columns: sparse, hierarchical data can be represented as JSON. If the database has JSON support, such as PostgreSQL and (partially) SQL Server 2016 and later, then ...
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.
A logical data model is sometimes incorrectly called a physical data model, which is not what the ANSI people had in mind. The physical design of a database involves deep use of particular database management technology. For example, a table/column design could be implemented on a collection of computers, located in different parts of the world.
A table (called the referencing table) can refer to a column (or a group of columns) in another table (the referenced table) by using a foreign key. The referenced column(s) in the referenced table must be under a unique constraint, such as a primary key. Also, self-references are possible (not fully implemented in MS SQL Server though [5]).
In a database, a table is a collection of related data organized in table format; consisting of columns and rows. In relational databases, and flat file databases, a table is a set of data elements (values) using a model of vertical columns (identifiable by name) and horizontal rows, the cell being the unit where a row and column intersect. [1]
Splitting a column into multiple columns (e.g., converting a comma-separated list, specified as a string in one column, into individual values in different columns) Disaggregating repeating columns Looking up and validating the relevant data from tables or referential files