What about further development of data cube and OLAP technology? Of course, it definitely will take place. But now, let's one more time make sure in the form of user's questions thread. What is OLAP cube? OLAP cube presents an array of multidimensional data. They are used in online analytical processing technology for data storage and future utilization. Cube has a form of star or snowflake schema in the middle of which is a fact table consisting of data aggregations and reconciling various dimensions. OLAB cubes are suitable for business reports, analysis, forecasting, management and budgeting. The technology allows users searching for specific product, project, etc., and receive result quickly. What is a measure in an OLAP cube? Measure is the smallest component in OLAP cube data model. This is the real data in OLAP cubes. Measures are stored in their own dimensions, which are called measure dimensions. What is dimension in OLAP cube? An OLAP dimension takes the highest level in the OLAP datacube structure.
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DOLAP is relatively cheaper to deploy as it offers very few functionalities compares to other OLAP systems. Web OLAP (WOLAP) Web OLAP which is OLAP system accessible via the web browser. WOLAP is a three-tiered architecture. It consists of three components: client, middleware, and a database server. Mobile OLAP: Mobile OLAP helps users to access and analyze OLAP data using their mobile devices Spatial OLAP: SOLAP is created to facilitate management of both spatial and non-spatial data in a Geographic Information system (GIS) ROLAP ROLAP works with data that exist in a relational database. Facts and dimension tables are stored as relational tables. It also allows multidimensional analysis of data and is the fastest growing OLAP. Advantages of ROLAP model: High data efficiency. It offers high data efficiency because query performance and access language are optimized particularly for the multidimensional data analysis. Scalability. This type of OLAP system offers scalability for managing large volumes of data, and even when the data is steadily increasing.
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Let's look at a scenario. There's a star schema with time, product, customer and channel dimension tables and a sales fact table. The hierarchies are as follows: Time: Day > Month > Quarter > Year Product: Item > Subtype > Type > Category > Department Customer: Customer > City > State > Country > Region Channel: Channel > Class The fact table contains data at the Day, Item, Customer and Channel levels for measures Sales and Quantity. The objective is to create a cube and cube-organized materialized view that manages all summary data beginning at Month, Item, City and Channel levels. Note that in this example the detail of the cube will be a summary of the fact table. Step 1 is to create SQL dimension objects for each of the dimension tables. (If you want to try this script yourself, download and install the OLAPTRAIN schema from the OLAP page on OTN.. ) -- -- Create SQL dimension objects for hierarchies and attributes. The cube will -- use these as the source definitions for cube. -- -- Time dimension.