Data Cube - Multidimensional Data Modeling PDF

Data Cube MULTIDIMENSIONAL DATA MODELLING  Concept of data cube A multidimensional data model views data in the form of a cube. A data cube is characterized with two things Dimension: the perspective or entities with respect to which an organization wants to keep record. Fact:...

Data Cube MULTIDIMENSIONAL DATA MODELLING  Concept of data cube A multidimensional data model views data in the form of a cube. A data cube is characterized with two things Dimension: the perspective or entities with respect to which an organization wants to keep record. Fact: The actual values in the record Example: Rainfall data of Metrological Department Time (Year, Season, Month, Week, Day, etc.) Location (Country, Region, State, etc.) 2-D view of rainfall data In this 2-D representation, the rainfall for “North-East” region are shown with respect to different months for a period of years 3-D view of rainfall data Suppose, we want to represent data according to times (Year, Month) as well as regions of a country say East, West, North, North-East, etc. A 2-D view of 3-D rainfall data 3-D view of the rainfall data Elements of a data cube  A data cube is a multi-dimensional data structure.  A data cube is characterized by its dimensions (e.g., Year, Month, region).  Each dimension is associated with corresponding attributes (e.g., the attributes of region are East, West, North east etc. ).  All dimensions connect in order to create a certain fact.  A fact has a corresponding measure in the data cube (e.g., the rainfall is measured in cm. ) Operations on data cube  Rollup – decreases dimensionality by aggregating data along a certain dimension.  using sum, average, standard deviation.  Drill-down – increases dimensionality by splitting the data further (month can be further splits to weeks and days).  Slicing – decreases dimensionality by choosing a single value from a particular dimension (slicing a rainfall for a particular region)  Dicing – picks a subset of values from each dimension (taking rainfall information for month of May-July in year 2007.  Pivoting – rotates the data cube.