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  2. Data model - Wikipedia

    en.wikipedia.org/wiki/Data_model

    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.

  3. Data modeling - Wikipedia

    en.wikipedia.org/wiki/Data_modeling

    Data modeling in software engineering is the process of creating a data model for an information system by applying certain formal techniques.

  4. Modeling and simulation - Wikipedia

    en.wikipedia.org/wiki/Modeling_and_simulation

    Modeling and simulation (M&S) is the use of models (e.g., physical, mathematical, behavioral, or logical representation of a system, entity, phenomenon, or process) as a basis for simulations to develop data utilized for managerial or technical decision making. [1] [2]

  5. Data vault modeling - Wikipedia

    en.wikipedia.org/wiki/Data_Vault_Modeling

    Dan Linstedt, the creator of the method, describes the resulting database as follows: "The Data Vault Model is a detail oriented, historical tracking and uniquely linked set of normalized tables that support one or more functional areas of business. It is a hybrid approach encompassing the best of breed between 3rd normal form (3NF) and star schema

  6. Data model (GIS) - Wikipedia

    en.wikipedia.org/wiki/Data_model_(GIS)

    A hybrid topological data model has the option of storing topological relationship information as a separate layer built on top of a spaghetti data set. An example is the network dataset within the Esri geodatabase. [23] Vector data are commonly used to represent conceptual objects (e.g., trees, buildings, counties), but they can also represent ...

  7. Data-driven model - Wikipedia

    en.wikipedia.org/wiki/Data-driven_model

    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]

  8. IDEF0 - Wikipedia

    en.wikipedia.org/wiki/IDEF0

    IDEF0 Diagram Example. IDEF0, a compound acronym ("Icam DEFinition for Function Modeling", where ICAM is an acronym for "Integrated Computer Aided Manufacturing"), is a function modeling methodology for describing manufacturing functions, which offers a functional modeling language for the analysis, development, reengineering and integration of information systems, business processes or ...

  9. Data processing - Wikipedia

    en.wikipedia.org/wiki/Data_processing

    Data processing is the collection and manipulation of digital data to produce meaningful information. [1] Data processing is a form of information processing , which is the modification (processing) of information in any manner detectable by an observer.