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In computing, the star schema or star model is the simplest style of data mart schema and is the approach most widely used to develop data warehouses and dimensional data marts. [1] The star schema consists of one or more fact tables referencing any number of dimension tables .
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.
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 fact constellation schema, also referred to as a galaxy schema, is a model using multiple fact tables and multiple dimension tables. [1] These schemas are implemented for complex data warehouses. [1] The fact constellation is a measure of online analytical processing and can be seen as an extension of the star schema.
Optional arcs are used to model scenarios for which an association represented in a fact schema is not defined for a subset of events. Optional arcs are marked with a dash. For instance, attribute diet in Figure 2 takes a value (such as cholesterol-free, gluten-free, or sugar-free) only for food products; for the other products, it is undefined.
The snowflake schema is in the same family as the star schema logical model. In fact, the star schema is considered a special case of the snowflake schema. The snowflake schema provides some advantages over the star schema in certain situations, including: Some OLAP multidimensional database modeling tools are optimized for snowflake schemas. [3]
Waikato Environment for Knowledge Analysis (Weka) is a collection of machine learning and data analysis free software licensed under the GNU General Public License.It was developed at the University of Waikato, New Zealand and is the companion software to the book "Data Mining: Practical Machine Learning Tools and Techniques".
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]