What are the features of OLAP Servers?

Data MiningDatabaseData Structure

There are various features of OLAP Servers which are as follows −

Multidimensional conceptual view − A user view of the enterprise data is multidimensional. The conceptual view of OLAP models should be multidimensional. The multidimensional models can be manipulated more easily and intuitively than in the case of single-dimensional models.

Transparency − A user should be able to get full value from an OLAP engine without regarding the source of the data. The OLAP system’s technology, underlying database, and computing architecture, and the heterogeneity of input data sources should be transparent to users to preserve their productivity and proficiency with familiar front-end environments and tools.

It should also be transparent to the user as to whether or not the enterprise data input to the OLAP tool comes from homogeneous or heterogeneous database environments.

Accessibility − The OLAP user must be able to perform analysis based upon a common conceptual schema composed of enterprise data in relational DBMS. The OLAP tool should map its logical schema to heterogeneous physical data stores, create the data and implement some conversions necessary to present a single, coherent and consistent user view.

Consistent performance − As the number of dimensions or the size of the database increases, there should not be any significant degradation in reporting performance. Consistent reporting performance is essential to supporting the ease to use and lack of complexity needed in bringing OLAP to the end-user.

Client-server architecture − Most of the data which require online analytical processing are stored on mainframe systems and accessed via personal computers. The OLAP servers must be capable of operating in a client-server environment.

The server component of OLAP tools should be sufficiently intelligent such that various clients can be connected with minimum effort and integration programming. This server may be capable of performing the mapping and consolidation between disparate logical and physical enterprise database schema necessary to affect transparency and to build common conceptual, logical, and physical schemas.

Generic dimensionality − Each data dimension should be similar in both its architecture and operational capabilities. Additional operational capabilities can be granted to selected dimensions but since dimensions are symmetric a given additional function can be granted to any dimension.

Multi-user support − There can be a need to work concurrently with either the same analytical model or to create different models from the same enterprise data.

Intuitive data manipulation − Consolidation by drilling down across columns or rows, zooming out, and other manipulation inherent in the consolidation outlines should be accomplished via direct action upon the cells of the analytical model, and should neither require the use of a menu nor multiple tips across the user interface.

Updated on 15-Feb-2022 11:24:51