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Overview of a data-modeling context: Data model is based on Data, Data relationship, Data semantic and Data constraint. A data model provides the details of information to be stored, and is of primary use when the final product is the generation of computer software code for an application or the preparation of a functional specification to aid a computer software make-or-buy decision.
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 ...
Hibernate ORM (or simply Hibernate) is an object–relational mapping [2]: §1.2.2, [12] tool for the Java programming language. It provides a framework for mapping an object-oriented domain model to a relational database.
Data models are progressive; there is no such thing as the final data model for a business or application. Instead a data model should be considered a living document that will change in response to a changing business. The data models should ideally be stored in a repository so that they can be retrieved, expanded, and edited over time.
From a model specification described in XML Metadata Interchange (XMI), EMF provides tools and runtime support to produce a set of Java classes for the model, a set of adapter classes that enable viewing and command-based editing of the model, and a basic editor.
DESMO-J provides a comprehensive set of readily usable Java classes [2] for stochastic distributions, static model components (like queues or resource synchronization), time representation and scheduling, experiment conduction and reporting. Supported by this simulation infrastructure, the user is free to concentrate on specifying the model's ...
For example, a simple linearized object would consist of a length field, a code point identifying the class, and a data value. A more complex example would be a command consisting of the length and code point of the command and values consisting of linearized objects representing the command's parameters.
Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, [3] neural networks for approximating functions, [4] global optimization and evolutionary computing, [5] statistical learning theory, [6] and Bayesian methods. [7]