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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.
The dimensional model is a specialized adaptation of the relational model used to represent data in data warehouses in a way that data can be easily summarized using online analytical processing, or OLAP queries. In the dimensional model, a database schema consists of a single large table of facts that are described using dimensions and measures.
A single record in this table is referred to as an analytical record or analytic record (AR), and represents the subject of the prediction (e.g. a customer) and stores all data (variables) describing this subject. [2] If for example the subject is a customer then the record may be referred to as a customer analytic record or "CAR". [3] [4] [5]
While the functional model retains the key features of the spreadsheet, it also overcomes its main limitations. With the functional model, data is arranged in a grid of cells, but cells are identified by business concept instead of just row or column. Rather than worksheets, the objects of the functional model are dimensions and cubes.
Snowflake schema used by example query. The example schema shown to the right is a snowflaked version of the star schema example provided in the star schema article. The following example query is the snowflake schema equivalent of the star schema example code which returns the total number of television units sold by brand and by country for 1997.
Example of positive reinforcing loop shown in the illustration: The amount of the Bank Balance will affect the amount of the Earned Interest, as represented by the top blue arrow, pointing from 'Bank Balance to Earned Interest. Since an increase in Bank balance results in an increase in Earned Interest, this link is positive, which is denoted ...
Data model: A data model defines the objects of a domain, their inter-relationships, and their properties, normally for the purpose of a database design. There are three data model levels, from highest to lowest: conceptual, logical, and physical. Conceptual data models are the highest level. They model the user concepts in terms familiar to users.
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]