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  2. Dimension (data warehouse) - Wikipedia

    en.wikipedia.org/wiki/Dimension_(data_warehouse)

    A common data warehouse example involves sales as the measure, with customer and product as dimensions. In each sale a customer buys a product. The data can be sliced by removing all customers except for a group under study, and then diced by grouping by product. A dimensional data element is similar to a categorical variable in statistics.

  3. Data hierarchy - Wikipedia

    en.wikipedia.org/wiki/Data_hierarchy

    Data field value = Jeffrey Tan The above description is a view of data as understood by a user e.g. a person working in Human Resource Department. The above structure can be seen in the hierarchical model, which is one way to organize data in a database. [2] In terms of data storage, data fields are made of bytes and these in turn are made up ...

  4. Multilevel model - Wikipedia

    en.wikipedia.org/wiki/Multilevel_model

    Another way to analyze hierarchical data would be through a random-coefficients model. This model assumes that each group has a different regression model—with its own intercept and slope. [ 5 ] Because groups are sampled, the model assumes that the intercepts and slopes are also randomly sampled from a population of group intercepts and slopes.

  5. Predictive analytics - Wikipedia

    en.wikipedia.org/wiki/Predictive_analytics

    Then, analyze the source data to determine the most appropriate data and model building approach (models are only as useful as the applicable data used to build them). Select and transform the data in order to create models. Create and test models in order to evaluate if they are valid and will be able to meet project goals and metrics.

  6. Star schema - Wikipedia

    en.wikipedia.org/wiki/Star_schema

    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 .

  7. Predictive coding - Wikipedia

    en.wikipedia.org/wiki/Predictive_coding

    This makes predictive coding similar to some other models of hierarchical learning, such as Helmholtz machines and Deep belief networks, which however employ different learning algorithms. Thus, the dual use of prediction errors for both inference and learning is one of the defining features of predictive coding.

  8. Data warehouse - Wikipedia

    en.wikipedia.org/wiki/Data_warehouse

    Data Warehouse and Data mart overview, with Data Marts shown in the top right. In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is a core component of business intelligence. [1] Data warehouses are central repositories of data integrated from ...

  9. Mixture of experts - Wikipedia

    en.wikipedia.org/wiki/Mixture_of_experts

    Other approaches include solving it as a constrained linear programming problem, [27] making each expert choose the top-k queries it wants (instead of each query choosing the top-k experts for it), [28] using reinforcement learning to train the routing algorithm (since picking an expert is a discrete action, like in RL), [29] etc.

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