Multi-Dimensional Database is a database which has been constructed with the multiple dimensions pre-filled in hyper dimensional “cubes” of data rather than the traditional two dimensional tables of Relational Databases.
It is also a database concept designed for decision support systems in which related data is stored in multidimensional “hypercubes”. This data organization allows for sophisticated and complex queries and can provide superior performance in certain cases over traditional relational structures.
Multi-dimensional databases are commonly used in data warehousing projects. There are actually two types of databases for a data warehouse and the other kind is the relational database. The only element which is the determinant for which database to use in a data warehouse is the data itself. And saying this, it would follow that the more data that the data warehouse is expected to handle and the more complex existing among these, the better off it would be to use a multidimensional database system.
A multidimensional database is actually based on the combination of data aggregators which takes together data from various sources, databases that offer networks, arrays, hierarchies and other data formatting methods which are difficult to model using SQL. In other words, a multidimensional database can offer better flexibility in the definition of dimensions, units and unit relationships whatever the format of the data is.
In the business enterprise environment, the particular field of sales and marketing greatly benefits from the use of multidimensional data in applications which involve time series. Because by nature this aspect of business deals with large volumes of sale and inventory data, these data may be stored so they can ultimately be used for planning related to logistics and executive decision making.
For instance, the high volume of data may be read segregated according to regional sales, product of time period. While a lot of the major databases developers have been implementing at least a partial solution for this scenario, many databases have been relying on the star schema design. But the star schema does not account for sparse data which means there is wasted space in the storage medium.
A multidimensional database uses the idea of a data cube in representing the data dimensions which are available to the users. For instance, sales may be seen in the dimension of product model, time, geography or any other applicable dimensions. For this case, sales may be referred to as the measure attribute of the data cube while the rest of the dimensions may be viewed as featured attributes. Hierarchies and levels within each dimension may be added by the database administrator.
The data cube may be implemented in a lot of different ways such as top-down, bottom-up, and arrays. A multidimensional database to be used for time series and other data vector analysis is better choice compared to using relational databases because of the volume of data and the complexity of relationships involved.
But a multidimensional database is not without some problems. When working with a multidimensional database having more than four dimensions, problem with dimensionality springs up and some of the results include having sparse or empty data. When one tries to take away these empty or sparse data, the database could be at risk because the context and vector coordinates of the data may be badly affected.
A multidimensional database is optimized for use with data warehouses and online analytical processing (OLAP) applications and is often created using input data from existing relational databases. There are current process and database developments from various database vendors that are meant to further improve multidimensional database while overcoming some of its problems.