Chapter 9 2 3

8/6/2019 Chapter 9 2 3

1/29

Asst.Prof.RachaneeAsst.Prof.Rachanee Kalayavinai,SIT,KMUTTKalayavinai,SIT,KMUTT77//3131//20112011 11

Functional requirements ofFunctional requirements of

OLAP SystemOLAP System

8/6/2019 Chapter 9 2 3

2/29


OLAPOLAP

The dynamic synthesis, analysis, andconsolidation of large volumes ofmultidimensional view of aggregate data

Enables users to gain a deeper

understanding and knowledge about variousaspects of their corporate data through fast,consistent, interactive access to a widevariety of possible views of the data

Types of analysis ranges from basicnavigation and browsing (slicing and dicing)to calculations, to more complex analysissuch as time series and complex modeling

8/6/2019 Chapter 9 2 3

3/29


OLAP BenefitsOLAP Benefits

Increased productivity of end users

Improved potential revenue and

profitability Reduced load of OLTP systems

8/6/2019 Chapter 9 2 3

4/29


OLAP ApplicationsOLAP Applications

A wide range of applications; domain oriented;

usually have the following features

multidimensional views of data

support complex calculations

time intelligence

E.g.,

financial performance analysis, budgeting, sales

analysis, sales forecasting, market research analysis,

production planning

8/6/2019 Chapter 9 2 3

5/29


ROLAP

ROLAP stands for Relational Online Analytical Process that

provides multidimensional analysis of data, stored in a Relational

database(RDBMS). MOLAP

MOLAP(Multidimensional OLAP), provides the analysis of data

stored in a multi-dimensional data cube.

HOLAP

HOLAP(Hybrid OLAP) a combination of both ROLAP and MOLAPcan provide multidimensional analysis simultaneously of data stored

in a multidimensional database and in a relational

database(RDBMS).

DOLAP

DOLAP(Desktop OLAP or Database OLAP)provide

multidimensional analysis locally in the client machine on the data

collected from relational or multidimensional database servers.

8/6/2019 Chapter 9 2 3

6/29


Multidimensional View of DataMultidimensional View of Data

Users can view data from various aspects(dimensions) with ease

All dimensions are equally treated

8/6/2019 Chapter 9 2 3

7/29


Support Complex CalculationsSupport Complex Calculations

Provide a range of powerful computational

methods for complex calculations such as

sales forecasting (moving average,percentage growth)

Mechanisms for implementing

computational methods should be clear

and non-procedural

8/6/2019 Chapter 9 2 3

8/29


Time IntelligenceTime Intelligence

Support operations on temporal dimension

Temporal operators are different fromothers (e.g., moving average)

8/6/2019 Chapter 9 2 3

9/29


Representation of MultiRepresentation of Multi--dimensional Datadimensional Data

Relational OLAP (ROLAP) Data are in a table with multiple columns

Each keyed column of represents a dimension of the

data Each non-keyed column represents a corresponding

fact

Multidimensional OLAP (MOLAP) Data are in a hypercube with multiple dimensions

Each dimension of the hypercube represents eachkeyed attribute of the data

Each cell of the cube represents a corresponding fact

8/6/2019 Chapter 9 2 3

10/29


ROLAPROLAP

8/6/2019 Chapter 9 2 3

11/29


MOLAPMOLAP

8/6/2019 Chapter 9 2 3

12/29


ROLAP vs. MOLAPROLAP vs. MOLAP

ROLAP

Straightforward from Relational Database

MOLAP

Better capture relationship between data

8/6/2019 Chapter 9 2 3

13/29


ROLAPROLAP vsvs MOLAPMOLAPData Storage Underlying Technologies Functions and Features

Data stored as relational Use of complex SQL to Known environment and

tables in the warehouse. fetch data from warehouse. availability of many tools.

Detailed and light Limitations on complexsummary data available. ROLAP engine in analysis functions. analytical server creates Very large data volumes. data cubes on the fly. Drill-through to lowest

level easier. Drill-across All data access from the Multidimensional views not always easy.

warehouse storage. by presentation layer.Data stored as relational Creation of pre-fabricated Faster access,tables in the warehouse. data cubes by MOLAP Large library of functions engine, Proprietary for complex calculations.Various summary data kept technology to storein proprietary databases multidimensional views in Easy analysis irrespective

(MDDBs) arrays, not tables. High of the number of dimensions.Moderate data volumes. speed matrix data retrieval.

Summary data access from Sparse matrix technology Extensive drill-down and MDDB. detailed data to manage data sparsity in slice-and -dicelities.

access from warehouse. summaries.

8/6/2019 Chapter 9 2 3

14/29


ROLAPROLAP

8/6/2019 Chapter 9 2 3

15/29


MOLAPMOLAP

8/6/2019 Chapter 9 2 3

16/29


Example of a twoExample of a two--DimensionalDimensionalreportreport

PRODUCT

C

US

T

O

MER

8/6/2019 Chapter 9 2 3

17/29


xamp e o a t reexamp e o a t ree-- imensionaimensionacubecube

ProductC

U

S

T

O

MER

Sales Report

8/6/2019 Chapter 9 2 3

18/29


OLAP TerminologyOLAP Terminology

Roll-up Summarize data

Drill-down

View detailed data Slicing

Select a plane

Dicing

Select a range of planes

Pivoting (slicing and dicing)

8/6/2019 Chapter 9 2 3

19/29


SQL Extensions to OLAPSQL Extensions to OLAP

GROUP BY ROLLUP

GROUP BY CUBE

GROUP BY GROUPING SET

GROUPING function

8/6/2019 Chapter 9 2 3

20/29


8/6/2019 Chapter 9 2 3

21/29


8/6/2019 Chapter 9 2 3

22/29

8/6/2019 Chapter 9 2 3

23/29


8/6/2019 Chapter 9 2 3

24/29


8/6/2019 Chapter 9 2 3

25/29


8/6/2019 Chapter 9 2 3

26/29


8/6/2019 Chapter 9 2 3

27/29


8/6/2019 Chapter 9 2 3

28/29


Star schemaStar schema vsvs OLAPOLAP

8/6/2019 Chapter 9 2 3

29/29


SixSix--dimensionaldimensional

Documents

Chapter 9 2 3