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The Data Warehouse
Published in Richard J. Roiger, Data Mining, 2017
Each attribute of an OLAP cube may have one or more associated concept hierarchies. A concept hierarchy defines a mapping that allows the attribute to be viewed from varying levels of detail. Figure 14.7 displays a concept hierarchy for the attribute location. As you can see, region holds the highest level of generality within the hierarchy. The second level of the hierarchy tells us that one or more states make up a region. The third and fourth levels show us that one or more cities are contained in a state and one or more addresses are found within a city. By definition, the hierarchy shows that each state is contained entirely within one and only one region and each city is part of exactly one state. Let’s create a scenario where our OLAP cube, together with the concept hierarchy of Figure 14.7, will be of assistance in a decision-making process.
Online analytical processing
Published in Catherine Dawson, A–Z of Digital Research Methods, 2019
Do you have a good understanding of OLAP systems, terms and techniques? This can include: an OLAP cube: a multidimensional database that is used to store data for processing, analysing and reporting purposes;dimension: a dataset of individual, non-overlapping data elements for filtering, grouping and labelling;measure: a numeric value by which the dimension is detailed or aggregated (or an additive numerical value that represents a business metric);fact table: a table that joins dimension tables with measures;star schema: a dimensional model that resembles a star (facts are surrounded with associated dimensions);snowflake schema: a logical arrangement of tables in a multidimensional database that resembles a snowflake;drill down: summarises data to lower levels of a dimension hierarchy;pivot (rotation): rotates the data axis, providing a different perspective to view data;roll-up (consolidation): summarises data along a dimension;sorting: adds, moves or alters the order of dimensions and measures in rows and columns;slice: selects one specific dimension from a given cube and provides a new sub-cube;dice: selects two or more dimensions from a given cube and provides a new sub-cube;data blending: mixes data from different structured and unstructured sources.
OCP: an OLAP-based bus crowdedness smart-perceiving mechanism for urban transportation
Published in Connection Science, 2022
Shiwen Gong, Dandan Liu, Md Zakirul Alam Bhuiyan, Xiaoguang Niu
Online Analytical Processing (OLAP) is a technology that enables analysts to quickly, consistently, and interactively observe information from all aspects to gain a deep understanding of the data. To facilitate this analysis, data is collected from multiple data sources and stored in a data warehouse, which is then cleaned and organised into data cubes. Each OLAP cube contains dimensionally ordered data extracted from dimension tables in the data warehouse. The dimensions are then populated by members of the hierarchical organisation. OLAP cubes are often pre-aggregated across dimensions to dramatically reduce query times for relational databases.