What is OLAP?

Data MiningDatabaseData Structure

OLAP stands for On-Line Analytical Processing. OLAP is an element of software technology that authorizes analysts, managers, and executives to gain insight into data through fast, consistent, interactive access in a wide variety of possible views of information that has been changed from raw information to reflect the actual dimensionality of the enterprise as learned by the client.

OLAP allows the users to generate online descriptive or comparative summaries of data and other analytics queries. It designates an element of software and technologies that allows the collection, storage manipulation, and reproduction of multidimensional records with the aim of analysis.

It allows the decision-makers to profit insight into data through quick consistent and interactive access to a broad variety of possible views of data that has been changed from raw data to the real dimensionality of the attributes.

OLAP servers present business users with multidimensional data from the data warehouse or data marts, without concerns regarding how or where the data are stored. The physical structure and execution of OLAP servers should consider data storage issues.

OLAP services are characterized by dynamic multidimensional analysis of consolidated enterprise data. OLAP is executed in a multiuser client/server mode and provides consistently fast response to queries, regardless of database size and complexity. It helps the users to synthesize enterprise information through comparative, personalized viewing and analysis of historical and projected data in various data model scenarios.

Some OLAP systems provide more drilling operations. For instance, drill-across implements queries containing (i.e., across) more than one fact table. The drillthrough services need relational SQL functions to drill through the bottom level of a data cube down to its back-end relational tables.

Several OLAP operations can involve ranking the top N or bottom N items in lists, and calculating moving averages, growth values, and interests, internal values of return, depreciation, currency conversions, and statistical services.

Efficient Processing of OLAP Queries

The goal of materializing cuboids and constructing OLAP index structures is to speed up query processing in data cubes.

Determine which operations should be performed on the available cuboids − This contains transforming some selection, projection, roll-up (group-by), and drill-down operations represented in the query into the corresponding SQL and/or OLAP operations.

Determine to which materialized cuboid(s) the relevant operations should be used − This includes identifying some materialized cuboids that can probably be used to answer the query, pruning the following collection using knowledge of "dominance" relationships among the cuboids, calculating the values of using the remaining materialized cuboids and selecting the cuboid with the minimum cost.

Updated on 15-Feb-2022 11:15:54