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).
Based on this concept, we propose the imageDW, an extended data warehouse with dimension tables specifically designed to support one or more perceptual layers.
In this data warehouse, the time dimension stores the hour, minute, and second for each observation, and the date dimension stores the day, month, year, and week day of the observations.
In our data warehouse, the relationship between a dimension table and an auxiliary table is 1 N.
We present scientific challenges to and tools for building data warehouses and describe the three dimensions of N-Catch: space, time and an original hierarchical description of cropping systems.
Its brick exterior, downtown location, asymmetrical dimensions and old B&O warehouse as a unique backdrop and distant outfield boundary would feel new if they were not so familiar.
An important dimension of the data warehouse is Marketing, representing the classification of products: it is organized as a tree and it represents a hierarchy built on the product typologies, designed by marketing experts of the company.
The standard normal forms and conformed dimensions that undergird most data warehouses built until before agile data engineering became available unfortunately result in applications that are very expensive to modify once data is loaded into their integration layers.
Star queries and star query transformations are used to transparently change the way queries are executed, typically between a single very large fact table and multiple much smaller dimension tables in a data warehouse.
The dimension, measure, attributes of the data warehouse are represented as concepts in the ontology.
