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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.
Additionally, the validation of migrated data for completeness and the decommissioning of legacy data storage are considered part of the entire data migration process. [ 1 ] [ 2 ] Data migration is a key consideration for any system implementation, upgrade, or consolidation, and it is typically performed in such a way as to be as automated as ...
Data type validation is customarily carried out on one or more simple data fields. The simplest kind of data type validation verifies that the individual characters provided through user input are consistent with the expected characters of one or more known primitive data types as defined in a programming language or data storage and retrieval ...
Data verification is a process in which different types of data are checked for accuracy and inconsistencies after data migration is done. [1] In some domains it is referred to Source Data Verification (SDV), such as in clinical trials .
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]
A schema migration is performed on a database whenever it is necessary to update or revert that database's schema to some newer or older version. Migrations are performed programmatically by using a schema migration tool. When invoked with a specified desired schema version, the tool automates the successive application or reversal of an ...
Verification and validation of computer simulation models is conducted during the development of a simulation model with the ultimate goal of producing an accurate and credible model. [ 1 ] [ 2 ] "Simulation models are increasingly being used to solve problems and to aid in decision-making.
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