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They were time consuming to deploy, and users needed to create data warehouse models before reports could be created.
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This paper proposes a new distributed data warehouse model.
An easily understood data warehouse model enables users to better identify and retrieve its data.
A widely accepted approach for developing data warehouses is the hybrid approach, where requirements and data sources must be accommodated to a reconciliated data warehouse model.
To compute the semantic distance between references of each query we use an edge counting method by applying the Rada distance [36] using an application ontology representing the different concepts of the data warehouse model (dimensions, measures and attributes).
The purpose of this paper is to propose, as an extension to the data warehouse model, a knowledge warehouse (KW) architecture that will not only facilitate the capturing and coding of knowledge but also enhance the retrieval and sharing of knowledge across the organization.
Data warehouse modeling is a complex task, which involves knowledge of business processes of the domain of discourse, understanding the structural and behavioral system's conceptual model, and familiarity with data warehouse technologies.
By using a data warehouse model, retrieving data from the database is very fast, resulting in exceptional web performance.
Thus, the most common approaches to clinical data warehouse modelling are variations on the entity-attribute-value (EAV) model, 22 28 where data are stored in a single table with three columns: entity identification, attribute and attribute value.
Data integration is accomplished by using a data warehouse model that combines and stores a wide range of data types from multiple sources in denormalized precomputed tables in a PostgreSQL database.
As a data warehouse framework, InterMine allows the integration of a wide range of biological data sources by framing its underlying data models on an ontology-based foundation.
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