Most times used interchangeably, the terms Online Analytical Processing (OLAP) and data warehousing apply to decision support and business intelligence systems. OLAP systems help data warehouses to analyze the data effectively. The dimensional modeling in data warehousing primarily supports OLAP, which encompasses a greater category of business intelligence like relational database, data mining and report writing.
Many of the OLAP applications include sales reporting, marketing, business process management (BPM), forecasting, budgeting , creating finance reports and others. Each OLAP cube is presented through measures and dimensions. Measures refers to the numeric value categorized by dimensions. In below diagrams, dimensions are time, item type and courtiers/cities and the values inside them (605, 825, 14, 400) are measures.
The OLAP approach is used to analyze multidimensional data from multiple sources and perspectives. The three basic operations in OLAP are:
- Roll-up (Consolidation).
- Slicing and dicing.
Roll-up or consolidation refers to data aggregation and computation in one or more dimensions. It is actually performed on an OLAP cube. For instance, the cube with cities is rolled up to countries to depict the data with respect to time (in quarters) and item (type).
On the contrary, Drill-down operation helps users navigate through the data details. In the above example, drilling down enables users to analyze data in the three months of the first quarter separately. The data is divided with respect to cities, months (time) and item (type)
Slicing is an OLAP feature that allows taking out a portion of the OLAP cube to view specific data. For instance, in the above diagram, the cube is sliced to a two dimensional view showing Item(types) with respect to Quadrant (time). The location dimension is skipped here. In dicing, users can analyze data from different viewpoints. In the above diagram, the users create a sub cube and chose to view data for two Item types and two locations in two quadrants.
OLAP systems are mainly classified into three :
- MOLAP (Multi-dimensional OLAP)
- ROLAP (Relational OLAP) : works with relational databases
- HOLAP (Hybrid OLAP): database divides data between relational and specialized storage
MDX ParallelPeriod Function06-November-2019
PARALLELPERIOD( Level_Expression, Member_Position, Member_Expression)
Member_Expression: Any Multidimensional Expression that returns valid Member.
Level_Expression: Please specify the level you want to navigate
Member_Position: Please specify the position of a member you want to Navigate.
- If we use Zero as the Member_Position then ParallelPeriod Function will write the same Member_Expression that we mentioned before ParallelPeriod function.
- If we use Negative Value as the Member_Position then ParallelPeriod Function will move forward to specified value and returns the Member_Expression at that position.
- And, If we use Positive Value as the Member_Position then ParallelPeriod Function will move Backwards to specified value and returns the Member_Expression at that position.
In this article we will show you, How to write ParallelPeriod function to navigate both forward and backward with examples. For this, we are going to use below shown data.
MDX ParallelPeriod Function with Zero
In this example we will show you, What happen when we use Zero value for the ParallelPeriod Function. The following query will return the Internet Sales amount of December in the Calender Year 2013 itself.
SELECT [Measures].[Internet Sales Amount] ON COLUMNS, PARALLELPERIOD ( [Date].[Calendar].[Month], 0, [Date].[Calendar].[Month].[December 2013] ) ON ROWS FROM [Adventure Works]
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