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The authors briefly introduce the various types of metadata used in data warehousing.
For the integration and import of data we use the extract, transform and load (ETL) approach commonly used in data warehousing [ 20].
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The most common data model used in data warehouses is dimensional modeling.
Embedded dependencies capture a knowledge specially relevant in all fields where materialized data sets are managed (e.g. materialized views widely used in data warehouses).
Hierarchies are used in data warehouses (DWs) and on-line analytical processing (OLAP) systems to see data at different levels of detail.
It is derived from core software engineering standards and adapts them for use in data warehousing.
Such a neural network might be used in data mining, for example, to discover clusters of customers in a marketing data warehouse.
The Connection algorithm [29] has a behaviour which is analogous to the MRFP-growth, is also based on the FP-growth and was initially idealised for use in data warehouses.
We use a data model for representing scqd metadata similar to those used in a data warehouse environment.
However, none of these novel systems has been demonstrated to efficiently perform multi-dimensional range queries incorporating many boolean operators, a task which is commonly used in scientific data exploration, data warehousing and business analytics.
For example, Ananthakrishna et al. [23] introduced a method that eliminates duplicates in data warehouses using a dimensional hierarchy (e.g., city-state-country) over the link relations.
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