Multi Dimensional Database - Challenges & Benefits

A multidimensional database designed to make the best use of storing and utilizing data. This form of database is usually structured in order to optimize OLAP and data warehouse applications. The multidimensional database can receive data from a variety of relational databases and structure the information into categories and sections that can be accessed in a number of different ways. Even persons who have relatively little experience working with a database often find that a multidimensional database, or MDB, requires only a short time to master.

Multidimensional database structure

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.

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.

Uses of Multi-dimensional databases

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.

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.

Problems with multidimensional databases

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. There are some processes & developments from various database vendors that are meant to further improve multidimensional database while overcoming some of its problems.