Modul Data Warehouse dan Data Mining
Download Modul Data Warehouse dan Data Mining Bab 09 - OLAP Arsitektur
Bab 09 - OLAP Arsitektur
Abstract
Menjelaskan konsep OLAP Arsitektur
Kompetensi
Mahasiswa mampu memahami OLAP arsitektur
Content
What and Why OLAP
OLAP Applications
OLAP Benefits
OLAP Key Features
Representation of Multi-dimensional Data
OLAP Tools – Features
OLAP Tools – Categories
Multi-dimensional OLAP (MOLAP)
Relational OLAP (ROLAP)
Hybrid OLAP (HOLAP)
Desktop OLAP (DOLAP)
What is OLAP
OLAP is the dynamic synthesis, analysis, and consolidation of large volumes of multidimensional
data.
OLAP is the term that describes a technology that uses multi-dimensional view of
aggregate data to provide quick access to strategic information for the purposes of
advanced analysis.
OLAP enables users to gain a deeper understanding and knowledge about various
aspects of their corporate data through fast, consistent, interactive access to a variety of
possible views of data.
While OLAP systems can easily answer ‘who?’ and ‘what?’ questions, it is easier ability
to answer ‘what if?’ and ‘why?’ type questions that distinguishes them from generalpurpose
query tools.
The types of analysis available from OLAP range from basic navigation and browsing
(referred to as ‘slicing’ and dicing’) , to calculations, to more complex analysis such as
time series and complex modeling.
OLAP APPLICATION
Finance: Budgeting, activity-based costing, financial performance analysis, and financial
modeling.
Sales: Sales analysis and sales forecasting.
Marketing: Market research analysis, sales forecasting, promotions analysis, customer
analysis, and market/customer segmentation.
Manufacturing: Production planning and defect analysis.
OLAP KEY FEATURE
Multi-dimensional views of data.
Support for complex calculations.
Time Intelligence.
OLAP BENEFIT
Increased productivity of business end-users, IT developers, and consequently the entire
organization.
Reduced backlog of applications development for IT staff by making end-users selfsufficient
enough to make their own schema changes and build their own models.
Retention of organizational control over the integrity of corporate data as OLAP
applications are dependent on data warehouses and OLTP systems to refresh their
source data level.
Reduced query drag and network traffic on OLTP systems or on the data warehouse.
Improved potential revenue and profitability by enabling the organization to respond
more quickly to market demands.
Representation of Multi-Dimensional Data
OLAP database servers use multi-dimensional structures to store data and relationships
between data.
Multi-dimensional structures are best-visualized as cubes of data, and cubes within
cubes of data. Each side of a cube is a dimension.
Representation of Multi-Dimensional Data
Multi-dimensional databases are a compact and easy-to-understand way of visualizing
and manipulating data elements that have many inter-relationships.
The cube can be expanded to include another dimension, for example, the number of
sales staff in each city.
The response time of a multi-dimensional query depends on how many cells have to be
added on-the-fly.
As the number of dimensions increases, the number of cube’s cells increases
exponentially.
Representation of Multi-Dimensional Data
Multi-dimensional OLAP supports common analytical operations, such as:
Consolidation: involves the aggregation of data such as ‘roll-ups’ or complex
expressions involving interrelated data. Foe example, branch offices can be
rolled up to cities and rolled up to countries.
Drill-Down: is the reverse of consolidation and involves displaying the detailed
data that comprises the consolidated data.
Slicing and dicing: refers to the ability to look at the data from different
viewpoints. Slicing and dicing is often performed along a time axis in order to
analyze trends and find patterns.
OLAP Tools – Features
In 1993, E.F. Codd formulated twelve rules as the basis for selecting OLAP tools:
Multi-dimensional conceptual view
Transparency
Accessibility
Consistent reporting performance
Client-server architecture
Generic dimensionality
Dynamic sparse matrix handling
Multi-user support
Unrestricted cross-dimensional operations
Intuitive data manipulation
Flexible reporting
Unlimited dimensions and aggregation levels
OLAP Tools – Categories
OLAP tools are categorized according to the architecture used to store and process
multi-dimensional data.
There are four main categories of OLAP tools as defined by Berson and Smith (1997)
and Pends and Greeth (2001) including:
Multi-dimensional OLAP (MOLAP)
Relational OLAP (ROLAP)
Hybrid OLAP (HOLAP)
Desktop OLAP (DOLAP)
Multi-dimensional OLAP (MOLAP)
MOLAP tools use specialized data structures and multi-dimensional database
management systems (MDDBMS) to organize, navigate, and analyze data.
To enhance query performance the data is typically aggregated and stored according to
predicted usage.
MOLAP data structures use array technology and efficient storage techniques that
minimize the disk space requirements through sparse data management.
The development issues associated with MOLAP:
Only a limited amount of data can be efficiently stored and analyzed.
Navigation and analysis of data are limited because the data is designed
according to previously determined requirements.
MOLAP products require a different set of skills and tools to build and maintain
the database.
Relational OLAP (ROLAP)
ROLAP is the fastest-growing type of OLAP tools.
ROLAP supports RDBMS products through the use of a metadata layer, thus avoiding
the requirement to create a static multi-dimensional data structure.
This facilitates the creation of multiple multi-dimensional views of the two-dimensional
relation.
To improve performance, some ROLAP products have enhanced SQL engines to
support the complexity of multi-dimensional analysis, while others recommend, or
require, the use of highly denormalized database designs such as the star schema.
The development issues associated with ROLAP technology:
Performance problems associated with the processing of complex queries that
require multiple passes through the relational data.
Development of middleware to facilitate the development of multi-dimensional
applications.
Development of an option to create persistent multi-dimensional structures,
together with facilities o assist in the administration of these structures.
Hybrid OLAP (HOLAP)
HOLAP tools provide limited analysis capability, either directly against RDBMS products,
or by using an intermediate MOLAP server.
HOLAP tools deliver selected data directly from DBMS or via MOLAP server to the
desktop (or local server) in the form of data cube, where it is stored, analyzed, and
maintained locally is the fastest-growing type of OLAP tools.
The issues associated with HOLAP tools:
The architecture results in significant data redundancy and may cause problems
for networks that support many users.
Ability of each user to build a custom data cube may cause a lack of data
consistency among users.
Only a limited amount of data can be efficiently maintained.
Desktop OLAP (DOLAP)
DOLAP tools store the OLAP data in client-based files and support multi-dimensional
processing using a client multi-dimensional engine. DOLAP requires that relatively small
extracts of data are held on client machines. This data may be distributed in advance or
on demand (possibly through the Web).
The administration of a DOLAP database is typically performed by a central server or
processing routine that prepares data cubes or sets of data for each user.
The development issues associated with DOLAP are as follows:
Provision of appropriate security controls to support all parts of the DOLAP
environment.
Reduction in the effort involved in deploying and maintaining the DOLAP tools.
Current trends are towards thin client machines.
Sumber :
Modul Perkuliahan - Data Warehouse dan Data Mining - Program Studi Sistem Informasi - Fakultas Ilmu Komputer - Universitas Mercu Buana
