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.
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.
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.