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The V-model is a graphical representation of a systems development lifecycle.It is used to produce rigorous development lifecycle models and project management models. The V-model falls into three broad categories, the German V-Modell, a general testing model, and the US government standard.
In software development, the V-model [2] represents a development process that may be considered an extension of the waterfall model and is an example of the more general V-model. Instead of moving down linearly, the process steps are bent upwards after the coding phase, to form the typical V shape.
Data models represent information areas of interest. While there are many ways to create data models, according to Len Silverston (1997) [7] only two modeling methodologies stand out, top-down and bottom-up: Bottom-up models or View Integration models are often the result of a reengineering effort. They usually start with existing data ...
Around the 1970s/1980s the term information engineering methodology (IEM) was created to describe database design and the use of software for data analysis and processing. [3] [4] These techniques were intended to be used by database administrators (DBAs) and by systems analysts based upon an understanding of the operational processing needs of organizations for the 1980s.
For example, the Document Object Model (DOM) is a collection of objects that represent a page in a web browser, used by script programs to examine and dynamically change the page. There is a Microsoft Excel object model [1] for controlling Microsoft Excel from another program, and the ASCOM Telescope Driver is an object model for controlling an ...
Attribute free, the predicates of an ORM Model lend themselves to the analysis and design of graph database models in as much as ORM was originally conceived to benefit relational database design. The term "object–role model" was coined in the 1970s and ORM based tools have been used for more than 30 years – principally for data modeling.
For instance, in model-driven development, technical artifacts such as source code, documentation, tests, and more are generated algorithmically from a domain model. [ 2 ] A modeling paradigm for MDE is considered effective if its models make sense from the point of view of a user that is familiar with the domain, and if they can serve as a ...
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]