OLAP tools are categorized based on how they store and process multi-dimensional data, with the main categories being MOLAP, ROLAP, HOLAP, and DOLAP. MOLAP uses specialized data structures and MDDBMS to organize and analyze aggregated data for optimal query performance. ROLAP uses relational databases with a metadata layer to facilitate multiple views of data. HOLAP combines aspects of MOLAP and ROLAP. DOLAP provides limited analysis directly from databases or via servers to desktops in the form of datacubes for local storage, analysis and maintenance.

1. OLAP Categories

2.  OLAP tools are categorized according to the
architecture used to store and process multi-
dimensional data.
 There are four main categories:
 Multi-dimensional OLAP (MOLAP)
 Relational OLAP (ROLAP)
 Hybrid OLAP (HOLAP)
 Desktop OLAP (DOLAP)
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3.  Use specialized data structures and multi-
dimensional Database Management Systems
(MDDBMSs) to organize, navigate, and
analyze data.
 Data is typically aggregated and stored
according to predicted usage to enhance
query performance.
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4.  Use array technology and efficient storage
techniques that minimize the disk space
requirements through sparse data
management.
 Provides excellent performance when data is
used as designed, and the focus is on data for
a specific decision-support application.
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5.  Traditionally, require a tight coupling with the
application layer and presentation layer.
 Recent trends segregate the OLAP from the
data structures through the use of published
application programming interfaces (APIs).
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6. 6

7.  MOLAP products require a different set of
skills and tools to build and maintain the
database, thus increasing the cost and
complexity of support.
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8. Observe la
normalización
de los miembros
Observe el
almacenamiento del
array en disco ó RAM
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9.  Fastest-growing style of OLAP technology
due to requirements to analyze ever-
increasing amounts of data and the
realization that users cannot store all the
data they require in MOLAP databases.
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10.  Supports RDBMS products using a metadata
layer - avoids need to create a static multi-
dimensional data structure - facilitates the
creation of multiple multi-dimensional views
of the two-dimensional relation.
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11.  To improve performance, some products use
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.
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12. 12

13.  Performance problems associated with the
processing of complex queries that require
multiple passes through the relational data.
 Middleware to facilitate the development of
multi-dimensional applications. (Software
that converts the two-dimensional relation
into a multi-dimensional structure).
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14.  Provide limited analysis capability, either
directly against RDBMS products, or by using
an intermediate MOLAP server.
 Deliver selected data directly from the DBMS
or via a MOLAP server to the desktop (or
local server) in the form of a datacube, where
it is stored, analyzed, and maintained locally.
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15.  Promoted as being relatively simple to install
and administer with reduced cost and
maintenance.
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16. 16

17.  Architecture results in significant data
redundancy and may cause problems for
networks that support many users.
 Ability of each user to build a custom
datacube may cause a lack of data
consistency among users.
 Only a limited amount of data can be
efficiently maintained.
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18.  Store the OLAP data in client-based files and
support multi-dimensional processing using a
client multi-dimensional engine.
 Requires that relatively small extracts of data
are held on client machines. They may be
distributed in advance, or created on demand
(possibly through the Web).
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19.  As with multi-dimensional databases on the
server, OLAP data may be held on disk or in
RAM, however, some DOLAP products allow
only read access.
 Most vendors of DOLAP exploit the power of
desktop PC to perform some, if not most,
multi-dimensional calculations.
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20.  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.
 Once the basic processing is done, each user
can then access their portion of the data.
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22.  Provision of appropriate security controls to
support all parts of the DOLAP environment.
Since the data is physically extracted from
the system, security is generally
implemented by limiting the information
compiled into each cube. Once each cube is
uploaded to the user's desktop, all additional
meta data becomes the property of the local
user.
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23.  Reduction in the effort involved in deploying
and maintaining the DOLAP tools. Some
DOLAP vendors now provide a range of
alternative ways of deploying OLAP data
such as through e-mail, the Web or using
traditional client/server architecture.
 Current trends are towards thin client
machines.
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24.  Efraim Turban. Business Intelligence. Prentice
Hall.2008.