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  2. Data modeling - Wikipedia

    en.wikipedia.org/wiki/Data_modeling

    Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, the process of data modeling involves professional data modelers working closely with business stakeholders, as well as potential users of the ...

  3. 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.

  4. Programming paradigm - Wikipedia

    en.wikipedia.org/wiki/Programming_paradigm

    A programming paradigm is a relatively high-level way to conceptualize and structure the implementation of a computer program. A programming language can be classified as supporting one or more paradigms. [1] Paradigms are separated along and described by different dimensions of programming. Some paradigms are about implications of the ...

  5. EXPRESS (data modeling language) - Wikipedia

    en.wikipedia.org/wiki/EXPRESS_(data_modeling...

    EXPRESS (data modeling language) Fig 1. Requirements of a database for an audio compact disc (CD) collection, presented in EXPRESS-G notation. EXPRESS is a standard for generic data modeling language for product data. EXPRESS is formalized in the ISO Standard for the Exchange of Product model STEP (ISO 10303), and standardized as ISO 10303-11.

  6. Error correction model - Wikipedia

    en.wikipedia.org/wiki/Error_correction_model

    Forecasts from such a model will still reflect cycles and seasonality that are present in the data. However, any information about long-run adjustments that the data in levels may contain is omitted and longer term forecasts will be unreliable. This led Sargan (1964) to develop the ECM methodology, which retains the level information. [4] [5]

  7. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).

  8. Data analysis - Wikipedia

    en.wikipedia.org/wiki/Data_analysis

    Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. [ 1 ] Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business ...

  9. Dimensional modeling - Wikipedia

    en.wikipedia.org/wiki/Dimensional_modeling

    The process of dimensional modeling builds on a 4-step design method that helps to ensure the usability of the dimensional model and the use of the data warehouse. The basics in the design build on the actual business process which the data warehouse should cover. Therefore, the first step in the model is to describe the business process which ...

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