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Quick Objects is an object–relational mapping tool for the Microsoft .NET Framework, a built in framework for business logic and validation. The architecture for Quick Objects differs from other ORM tools. The focus of Quick Objects is to provide the advantages of code reuse, code generation and object relational mapping in a single tool set.
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]
The Biba Model or Biba Integrity Model developed by Kenneth J. Biba in 1975, [1] is a formal state transition system of computer security policy describing a set of access control rules designed to ensure data integrity. Data and subjects are grouped into ordered levels of integrity.
Verification is intended to check that a product, service, or system meets a set of design specifications. [6] [7] In the development phase, verification procedures involve performing special tests to model or simulate a portion, or the entirety, of a product, service, or system, then performing a review or analysis of the modeling results.
The model is viewed as an input-output transformation for these tests. The validation test consists of comparing outputs from the system under consideration to model outputs for the same set of input conditions. Data recorded while observing the system must be available in order to perform this test. [3]
The second set of commands send data to be printed on the current line plus a paper movement instruction to the printer. Note that in contrast to the ASA control characters, the IBM machine print control characters ask the printer to firstly print the data on the current line, and then secondly advance the paper.
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]
User input validation: User input (gathered by any peripheral such as a keyboard, bio-metric sensor, etc.) is validated by checking if the input provided by the software operators or users meets the domain rules and constraints (such as data type, range, and format).