OLAP


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OLAP

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OLAP

(OnLine Analytical Processing) Decision support software that allows the user to quickly analyze information that has been summarized into multidimensional views and hierarchies. OLAP tools are used to perform trend analysis on sales and financial information. They enable users to drill down into masses of sales statistics in order to isolate products that are the most volatile.

OLAP and MOLAP
Traditional OLAP products are also called "multidimensional OLAP" (MOLAP) because they summarize transactions into multidimensional views ahead of time. Data are organized into a cube structure that can be rotated by the user, which is particularly suited for financial summaries. Queries are fast because the consolidation has already been done.

Relational OLAP (ROLAP)
Relational OLAP tools extract data from relational databases. Using complex SQL statements against relational tables, ROLAP is able to create multidimensional views on the fly. ROLAP tends to be used on data that has a large number of attributes, where it cannot be easily placed into a cube structure. For example, customer data with numerous descriptive fields are typically ROLAP candidates, rather than financial data.

Database OLAP and Web OLAP (DOLAP and WOLAP)
A database OLAP refers to a relational DBMS that is designed to host OLAP structures and perform OLAP calculations. Web OLAP refers to OLAP data that is accessible from a Web browser. See MDX.
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References in periodicals archive ?
While the IT industry and the academic world are inventing new technologies for Big Data, traditional techniques of BA, such as OLAP, are still the major tools for business organizations to dealing with structured data (Howson, 2012; SoftwareAdvice, 2018).
However, it is widely accepted that the collaborative filtering approach suffers from sparsity and cold start problems, the recent work can not well solve this problem in the OLAP session recommendation, which will affect the final effectiveness of recommendation.
They are refreshed every five seconds (by default) so that any OLAP query routed to them will get reasonably current data.
Three kinds of data warehouse applications, information processing, supports querying, basic statistical analysis, and reporting using cross tabs, tables, charts and graphs and analytical processing, multidimensional analysis of data warehouse data, supports basic OLAP operations, slice-dice, drilling, pivoting data mining, knowledge discovery from hidden patterns and supports associations constructing analytical models, performing classification and prediction, and presenting the mining results using visualization tools of differences among the three tasks.
The function of data drilling in OLAP technology is to make further refined analysis of data for the suspicious data found in observation and analysis of audit data, thus to obtain more precise information to determine and reach the purpose of judging whether there is any problem.
Analytic OLAP operations including rollup, drill-down, slicing and dicing and pivoting have different nature than OLTP operations.
We know that a cube-based OLAP tool does enable end-users to slice and dice their data, perform multi-dimensional analysis, present information in graphs and charts, and more.
The pre-processing phase is followed by the creation of OLAP (OnLine Analytical processing) cube and dimensions.
On-Line Analytical Processing (OLAP) is a multidimensional technology usually used to analyze aggregated data from different points of view.
Founded in 1993, Panorama is a leading innovator in Online Analytical Processing (OLAP) and Multidimensional Expressions (MDX).
Appropriate for both CEOs and DBAs, this guide outlines the similarities and differences between Oracle Essbase 11 and Oracle OLAP 11g and explains how both products implement online analytical processing (OLAP) technology.
And instead of using conventional methods for analysing statistics, the researchers borrowed an approach from the computer science field of artificial intelligence, OLAP, which was developed in the early 1990s and was exploited primarily in industrial and commercial applications, for financial and marketing analysis.