ONLINE ANALYTICAL PROCESSING AND CLASSIFICATION

Introduction to OLAP

OLAP (online analytical processing) is computer processing that enables a user to easily and selectively extract and view data from different points of view.

Online Analytical Processing Server (OLAP) is based on multidimensional data model. It allows the managers, analysts to get insight the information through fast, consistent, interactive access to information.

Types of OLAP Servers

We have four types of OLAP servers that are listed below.

·         Relational OLAP(ROLAP)

·         Multidimensional OLAP (MOLAP)

·         Hybrid OLAP (HOLAP)

·         Specialized SQL Servers

Relational OLAP(ROLAP)

The Relational OLAP servers are placed between relational back-end server and client front-end tools. To store and manage warehouse data the Relational OLAP use relational or extended-relational DBMS.

ROLAP includes the following.

·         Implementation of aggregation navigation logic.

·         Optimization for each DBMS back end.

·         Additional tools and services.

Multidimensional OLAP (MOLAP)

Multidimensional OLAP (MOLAP) uses the array-based multidimensional storage engines for multidimensional views of data. With multidimensional data stores, the storage utilization may be low if the data set is sparse. Therefore many MOLAP Servers use the two level of data storage representation to handle dense and sparse data sets.

Hybrid OLAP (HOLAP)

The hybrid OLAP technique is combination of ROLAP and MOLAP. It has both the higher scalability of ROLAP and faster computation of MOLAP. HOLAP server allows to store large data volumes of detail data. The aggregations are stored separated in MOLAP store.

Specialized SQL Servers

Specialized SQL servers provides advanced query language and query processing support for SQL queries over star and snowflake schemas in a read-only environment.

OLAP Operations

As we know that the OLAP server is based on the multidimensional view of data hence we will discuss the OLAP operations in multidimensional data.

Here is the list of OLAP operations.

·         Roll-up

·         Drill-down

·         Slice and dice

·         Pivot (rotate)

Multidimensional OLAP (MOLAP) uses the array-based multidimensional storage engines for multidimensional views of data. With multidimensional data stores, the storage utilization may be low if the data set is sparse. Therefore many MOLAP Servers uses the two level of data storage representation to handle dense and sparse data sets.

Points to remember:

·         MOLAP tools need to process information with consistent response time regardless of level of summarizing or calculations selected.

·         The MOLAP tools need to avoid many of the complexities of creating a relational database to store data for analysis.

·         The MOLAP tools need fastest possible performance.

·         MOLAP Server adopts two level of storage representation to handle dense and sparse data sets.

·         Denser sub-cubes are identified and stored as array structure.

·         Sparse sub-cubes employ compression technology.

·         MOLAP Architecture

·         MOLAP includes the following components.

·         Database server

·         MOLAP server

·         Front end tool

Advantages

·         MOLAP allows fastest indexing to the pre-computed summarized data.

·         Helps the users who are connected to a network and need to analyze larger, less defined data.

·         Easier to use therefore MOLAP is best suitable for inexperienced user.

Disadvantages

·         MOLAP are not capable of containing detailed data.

·         The storage utilization may be low if the data set is sparse.