are designed to efficiently store and manipulate large volumes of data. They organize data into , which represent the various categories or characteristics of the data. Each dimension consists of a set of attributes that define it, enabling users to slice and dice the data based on different dimensions.
, the core notion in multi-dimensional databases, are an array of data points organized along dimensions. They are used to represent the multi-dimensional data and provide a concise and efficient representation. , on the other hand, quantify the data stored within the cubes, such as sales figures or quantities. By associating measures with dimensions, analysts can analyze the data from different perspectives.
play a crucial role in multi-dimensional databases by defining the various levels of granularity within a dimension. They enable users to drill down or the data by navigating through different levels of detail. For instance, in a sales dimension, the hierarchy might include levels such as region, country, city, and store, providing different levels of aggregation for analysis.
and allow users to focus on specific portions of the data by selecting or filtering certain dimensions or attributes. Slicing refers to reducing the data set based on specific criteria, while dicing refers to dividing the data into smaller subsets. These operations are particularly useful when examining subsets of data that meet specific conditions or exploring relationships between different dimensions.
Both roll-up and operations are essential for navigating multi-dimensional databases at different levels of aggregation. Roll-up involves summarizing the data from a detailed level to a higher-level perspective, while drill-down enables users to explore the data in greater detail by moving from a higher-level view to a more detailed view, going down the hierarchy.
Online Analytical Processing () is a technology closely associated with multi-dimensional databases. It facilitates the analysis of data for decision-making purposes and provides advanced capabilities such as aggregations, calculations, and comparisons. OLAP allows users to interactively explore and analyze large datasets through operations like slicing, dicing, roll-up, and drill-down within multi-dimensional databases.
