Online Analytical Processing and its Characteristics
The need for more intensive decision support prompted the introduction of a new generation of tools. Those new tools, called online analytical processing (OLAP), create an advanced data analysis environment that supports decision making, business modeling, and operations research.
OLAP systems share four main characteristics:
Business Intelligence and Its Architecture
Operational Data and Decision Support Data
The Data Warehouse and Data Mart
Relational OLAP
• They use multidimensional data analysis techniques.
• They provide advanced database support.
• They provide easy-to-use end-user interfaces.
• They support the client/server architecture.
I. Multidimensional Data Analysis Techniques:
The most distinctive characteristic of modern OLAP tools is their capacity for multidimensional analysis. In multidimensional analysis, data are processed and viewed as part of a multidimensional structure. This type of data analysis is particularly attractive to business decision makers because they tend to view business data as data that are related to other business data.
To better understand this view, let’s examine how a business data analyst might investigate sales figures.
In the above tabular (operational) view of sales data is not well suited to decision support, because the relationship between INVOICE and LINE does not provide a business perspective of the sales data. But consider the following figure.
The end user’s view of sales data from a business perspective is more closely represented by the multidimensional view of sales than by the tabular view of separate tables. Note also that the multidimensional view allows end users to consolidate or aggregate data at different levels: total sales figures by customers and by date. Finally, the multidimensional view of data allows a business data analyst to easily switch business perspectives (dimensions) from sales by customer to sales by division, by region, and so on.
Multidimensional data analysis techniques are augmented by the following functions:
• Advanced data presentation functions:
3-D graphics, pivot tables, crosstabs, data rotation, and three dimensional cubes. Such facilities are compatible with desktop spreadsheets, statistical packages, and query and report packages.
• Advanced data aggregation, consolidation, and classification functions:
These allow the data analyst to create multiple data aggregation levels, slice and dice data and drill down and roll up data across different dimensions and aggregation levels. For example, aggregating data across the time dimension (by week, month, quarter, and year) allows the data analyst to drill down and roll up across time dimensions.
• Advanced computational functions:
These include business-oriented variables (market share, period comparisons, sales margins, product margins, and percentage changes), financial and accounting ratios (profitability, overhead, cost allocations, and returns), and statistical and forecasting functions. These functions are provided automatically, and the end user does not need to redefine their components each time they are accessed.
• Advanced data-modeling functions:
These provide support for what-if scenarios, variable assessment, variable contributions to outcome, linear programming, and other modeling tools.
II. Advanced Database Support:
To deliver efficient decision support, OLAP tools must have advanced data access features. Such features include:
• Access to many different kinds of DBMSs, flat files, and internal and external data sources.
• Access to aggregated data warehouse data as well as to the detail data found in operational databases.
• Advanced data navigation features such as drill-down and roll-up.
• Rapid and consistent query response times.
• The ability to map end-user requests, expressed in either business or model terms, to the appropriate data source and then to the proper data access language (usually SQL). The query code must be optimized to match the data source, regardless of whether the source is operational or data warehouse data.
• Support for very large databases. As already explained the data warehouse can easily and quickly grow to multiple gigabytes and even terabytes.
III. Easy-to-Use End-User Interface:
Advanced OLAP features become more useful when access to them is kept simple. OLAP tools have equipped their sophisticated data extraction and analysis tools with easy-to-use graphical interfaces. Many of the interface features are “borrowed” from previous generations of data analysis tools that are already familiar to end users. This familiarity makes OLAP easily accepted and readily used.
IV. Client/Server Architecture:
Client/server architecture provides a framework within which new systems can be designed, developed, and implemented. The client/server environment enables an OLAP system to be divided into several components that define its architecture. Those components can then be placed on the same computer, or they can be distributed among several computers. Thus, OLAP is designed to meet ease-of-use requirements while keeping the system flexible.
Star Schema and Its Components
Issues In Data Warehouse Implimentation
