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The purpose of the ROLAP Engine is to respond to queries from cube end users. The OLAP queries are performed in the server side using Java interface. To increase performance, Axional ROLAP Engine may runs on specially configured server computers.
With its specific architecture, Axional ROLAP engine includes the following features:
The Axional OLAP module uses a multidimensional view of aggregate data to provide quick access to strategic information for further analysis. Users can gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information. This allows everyone in the organization to see the corporate data warehouse from their particular point of view.
This module also provides users with the information they need to make effective decisions about an organization’s strategic directions. Its features range from basic navigation and browsing, to complex calculations, to more serious analyses–such as time series. As decision-makers gain experience with OLAP capabilities, they move from data access to information to knowledge. The tool’s goal is to convert your business data into business intelligence. To achieve this objective, it uses a pre-configured datamart structure that at the same time offers a flexible configuration.
The Axional OLAP module insulates users from complex query syntax, modeling designs and elaborate joins. Its multidimensional view of data provides the foundation for analytical processing through flexible information access. Users are able to analyze data across any dimension, at different levels of aggregation, with equal functionality and ease.
The OLAP module works on facts and facts are numbers. A fact could be a count of sales, the sum of the sales amounts, or an average of sales amounts. Facts are also known as Measures and are organized into dimensions which are ways that the facts can be broken down. For instance, total sales might be able to be broken up by geography. Similarly, total sales might also be broken down by time. Dimensions have also hierarchies of levels.
The set of dimensions and measures is called a Cube. The cubes facilitate multifaceted data analysis in response to complex business queries. Because they can be made up of more than three dimensions (hypercube), in-depth analysis is enabled, allowing users to gain comprehensive and valuable business insights.
Axional Analytics allows for virtually unlimited numbers of dimensions to be added to the data structure (OLAP cube), allowing for detailed data analysis. Analysts can view data sets from different angels or pivots.
It uses a relational database which directly stores the information contained in the various cubes (ROLAP model). This approach translates native OLAP queries into the appropriate SQL statements. Thanks to the use of DB high performance tools, such as Informix IWA, this approach performs as well as a MOLAP database.
This approach also enables an easy implementation of In-memory analytics allowing for faster analysis, rapid insights and minimal IT involvement. The In-memory analytics approach eliminates the need to store pre-calculated data in the form of OLAP cubes or aggregate tables. It offers business-users faster analysis, and access to analysis of large data sets, with minimal data management requirements.
With the ETL process, several cubes can be created, each one with a specific set of dimensions and measures more fitted to the requirements of a particular group of users. With cubes, managers gain insight into data through fast, accurate, and flexible methods to various views of business metrics that have been transformed from raw data into meaningful information.