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online analytical processing presentation
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Online Analytical Processing (OLAP)


Business Intelligence (BI) Technologies
OLAP definitions
Data cube & hypercube
OLAP operations
Types of OLAP tools

Business Intelligence (BI) Technologies

With the growth in data warehousing, users demand for more powerful access tools that provide advanced analytical capabilities
Two main types of these access tools are
¢ Online Analytical Processing (OLAP)
¢ Data mining
OLAP and Data Mining differ in what they offer the user
¢ complementary technologies
Data warehouse (or data marts) together with tools such as OLAP and /or data mining are referred to as Business Intelligence (BI) technologies

What is OLAP

Online Analytical Processing (OLAP) is a system that further transforms the data into a more structured (summarized) form than tables
OLAP is a form of Executive Information System (EIS) and Decision Support System (DSS)
OLAP looks at data in multi-dimensional form (data cube)
OLAP can be used by multiple users to access data in a data warehouse, e.g. via Internet
OLAP provides managers with a quick and flexible access to large volume of data

OLAP Definitions

Codd (1993) “ OLAP is the dynamic synthesis, analysis, and consolidation of large volumes of multi-dimensional data.
OLAP technology uses a multi-dimensional view of aggregate data to provide quick access to strategic information


Users need powerful tools for the analysis of large-volume of data,
¢ i.e. data in data warehouse
Two main types of analysis tools for data warehouse are:
¢ Online Analytical Processing (OLAP)
top-down analysis
¢ Data Mining
bottom-up analysis
OLAP vs. general-purpose query tools
¢ OLAP has ability to answer ˜what if™ and ˜why™ questions (not only ˜what™, ˜when™, ˜where™ and ˜how much™ questions)
¢ OLAP has more advanced and interactive functionalities
Complex analyses

OLAP Applications

OLAP applications usually have the following common features:
¢ Multi-dimensional views of data
Data can be viewed from various perspectives, e.g. product, location, time, etc.
¢ Support for complex calculations
e.g. sales forecasting, moving averages, percentage growth, etc.
¢ Time intelligence
e.g. comparisons of sales performance between different time periods

Data Cube

Multi-dimensional structures are best visualized as cubes of data
Cube represents data as cells in an array
Each side of a cube is a dimension
A cube supports matrix arithmetic
Hypercube is a form of data cube that has more than 3 dimensions
¢ Hypercube can be represented as cube that contains cubes for other dimensions (cubes within cubes)
¢ As number of dimensions increases, number of the cube™s cells increases exponentially

OLAP Operations

¢ Select data on a single dimension of a data cube
¢ Extracts a sub-cube from the original cube
Roll-up (aggregation)
¢ Combing of cells for one dimension
¢ Generalization, e.g. Jan, Feb, Mar = Quarter 1
¢ May be used with concept hierarchy
¢ Reverse of Roll-up operation
¢ Examine data at level of greater detail, e.g. Northern Region = Chiang Mai, Chiang Rai, ¦
Rotation (pivot)
¢ Allow user to view data from a new perspective
¢ Axis rotation

Multi-dimensional OLAP (MOLAP)

Use Multi-dimensional Database Management System (MDDBMS) to organize and analyze data
Use some efficient storage techniques to minimize disk space requirement
Provides good performance when data is used as designed
Provide a tight coupling between data structure and presentation layer
¢ Access to data structure may be provided via application programming interfaces (APIs)

MOLAP Issues

MOLAP products require different skills and tools to build and maintain the database, thus increasing the cost and complexity of support
¢ MDDBMS is a new and immature technology (compared to RDBMS)

Relational OLAP (ROLAP)

Fastest-growing type of OLAP technology
MOLAP databases has some limitations
¢ Not all data can be efficiently stored in MOLAP databases
Uses supports from RDBMS
¢ avoids need to create multi-dimensional database
¢ creates multi-dimensional views from relational database
May use SQL to support multi-dimensional data analysis

ROLAP Issues

Need to create a middleware to work with multi-dimensional applications
¢ The middleware must convert relational data structure to multi-dimensional data structure
Performance problems for complex queries that require complex transformations from relational data


Provide query support for both RDBMS and MDDBMS
¢ Query data directly from the RDBMS using SQL or via a MOLAP server in the form of a data cube
May cause data redundancy and inefficient network usage

Desktop OLAP (DOLAP)

Store and process the OLAP data on client side
Data are held on client machines
¢ Database may be distributed in advance, or created on demand (e.g. through the Web)
¢ The maintenance of database is usually done by a central server
DOLAP uses the power of desktop PC to perform multi-dimensional calculations

DOLAP Issues

Security (access control) can be difficult
¢ Can not utilize access control feature of DBMS
Current trends are towards thin client machines
¢ Complex calculations are increasingly moved to server machine rather than client machine

OLAP Benchmark

APB-1 (OLAP Council, 1998) is a standard for OLAP benchmark
¢ Measurement of OLAP server performance
APB-1 evaluates OLAP server performance for the following operations:
¢ Loading of data
¢ Aggregation of data
¢ Complex Calculations
¢ Time series analysis
¢ Complex Queries
¢ Drill-down through hierarchies
¢ Multiple online sessions
¢ etc.
A benchmark metric used by APB-1 is AQM (Analytical Queries per Minute)
AQM measures the number of analytical queries that an OLAP server can process per minute
¢ The time is measured from when the data is loaded until the results are returned to user

OLAP Extensions to SQL

SQL has limited capability to support complex management queries
ANSI adopted a set of OLAP functions as an extension to SQL
¢ IBM and Oracle jointly proposed these extensions in 1999 as part of the current SQL standard
The extensions are referred to as the ËœOLAP packageâ„¢:
¢ Feature T431, ˜Extended Grouping capabilities™
¢ Feature T611, ˜Extended OLAP operators™
Post: #2
online analytical processing presentation


Aggregation, summarization and exploration

Of historical data

To help management make informed decisions

Query Language Extensions

In the real world, data is stored in RDBs.
How to express N-dimensional problems using 2D tables?

Query Language Extensions

In the real world, data is stored in RDBs.

How to express N-dimensional problems using 2D tables?

Can we combine OLAP and SQL queries?

Jim Gray et al: Data Cube: A Relational Aggregation Operator 1997

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