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On-Line Analytical Processing (OLAP)

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Title: On-Line Analytical Processing (OLAP)


1
On-Line Analytical Processing(OLAP)
Theodoros Chrysafis Department of Computer
Science CITY Liberal Studies an Affiliated
Institution of the University of
Sheffield Academix 2003
2
Contents
  • Introduction
  • Decision Support Systems
  • On-Line Analytical Processing (OLAP)
  • OLAP Taxonomy
  • MOLAP
  • ROLAP
  • HOLAP
  • DOLAP
  • Conclusion

3
Introduction
  • What is
  • On-Line A process controlled by a computer.
  • Analytical Processing needs Analytical Data.
  • Analytical Data Data that involve analysis.
  • Analytical Data consist of Business Data.
  • Business Data Time, Customers, Sales, Stores,
    Products, etc.

4
Decision Support Systems
  • Decision Support Systems (DSS) can defined in two
    ways
  • By describing the software tools or the
    technologies we use to perform business
    decisions.
  • OR
  • By describing the function or the concept of
    Decision Support Systems in a tool neutral way.

5
Decision Support Systems
  • Definition of DSS by Erik Thomsen
  • The activity of using logic-based
    fact-processing rules in combination with
    goal-oriented management rules with or without
    human intervention to translate larger sets of
    lower-level facts and fact relationships into
    smaller sets of higher-level facts and fact
    relationships (and vise versa).

Client
Analytical Processing
Analytical Data
Business Data
6
Decision Support Systems
  • DSS treats facts and rules differently. This
    treatment groups DSS in the following categories
  • Data Mining Tools - Discover rules that explain
    relationships between facts.
  • OLAP Tools - organise facts according to multiple
    dimensions and they use powerful rules for
    combining those facts to form aggregate facts.
  • Business Modeling Tools - organise the management
    and the expert tools.
  • Data Visualisation Tools - graphically illustrate
    relationships between facts.

7
Data Dimensionality
Customers
Sale
  • Possible Views of Sale
  • How many Products sold at Time to specific
    Customer(s)?
  • How many Customers bought at specific Time the
    Product(s)?
  • At which Time(s) the Customer(s) bought the
    specific Product(s)?
  • Products

Time
8
On-Line Analytical Processing
  • On-Line Analytical Processing (OLAP) definition
    by OLAP Council
  • A category of software technology that enables
    analysts, managers and executives to gain insight
    into data through fast, consistent, interactive
    access to a wide variety of possible views of
    information that has been transformed from raw
    data to reflect the real dimensionality of the
    enterprise us understood by the user.

Security
Interactivity
Customisation
Visualisation
OLAP
9
On-Line Analytical Processing
  • OLAP functionality provides dynamic
    multi-dimensional analysis, supporting analytical
    and navigational activities.
  • OLAP functionality is provided by the OLAP
    Server.
  • OLAP Council defines OLAP Server as A high
    capacity, multi-user data manipulation engine
    specifically designed to support and operate on
    multi-dimensional data structures.

10
On-Line Analytical Processing
  • OLAP Taxonomy
  • Multi-dimensional OLAP (MOLAP)
  • A k-dimensional matrix based on a non relational
    storage structure. Agrawal et al.
  • Relational OLAP (ROLAP)
  • A relational back-end wherein operations of the
    data are translated to relational queries.
    Agrawal et al.
  • Hybrid OLAP (HOLAP)
  • Integration of MOLAP and ROLAP.
  • Desktop OLAP (DOLAP)
  • Provides a specific cube for analysis. Simplified
    version of MOLAP or ROLAP.

11
On-Line Analytical Processing
  • MOLAP
  • Multi-dimensional data management in
    Multi-Dimensional Database Management Systems
    (MDDBMS).
  • Advantages Fast data access, many dimensions,
    performance.
  • Further Research on storage techniques and
    realisation of transactional concepts.

12
On-Line Analytical Processing
  • ROLAP
  • A multi-dimensional user view on relational data
    storage using Star or Snowflake Database Schemata.

Region Dimension
Customer Dimension
Product Dimension
Time Dimension
Snowflake Schema
13
On-Line Analytical Processing
  • ROLAP
  • Advantages Easy to understand, easy to model,
    easy to implement.
  • Further Research on dynamic optimisation, on
    meta-models, on functional extensions for the
    ROLAP engines, on user-defined functions for the
    OLAP.

14
On-Line Analytical Processing
  • HOLAP
  • A system, which supports (and integrates)
    multi-dimensional and relational storage for data
    in an equivalent manner in order to benefit from
    the corresponding characteristics and
    optimisation techniques. Dinter et al.
  • Advantages use of best techniques introduced on
    MOLAP and ROLAP, transparency between MOLAP and
    ROLAP systems.
  • Further Research on storage systems, on global
    multi-dimensional schema, on common interface and
    mutual integration of MOLAP and ROLAP.

15
On-Line Analytical Processing
  • Conclusion
  • OLAP Tools are Market Driven. That is, no
    standards either academic or from an organisation
    exist.
  • A common model approach is to use Star or
    Snowflake Database Schemata (common in Data
    Warehouse Modeling).
  • Reviews contain many technical issues resulting
    difficulties to understand while a candidate end
    user needs information about a candidate product.
  • Hence, we advice candidate end users to look for
    the following, independent tool architecture or
    vendor, characteristics.

16
On-Line Analytical Processing
  • Conclusion
  • Interactivity How easy the end user interacts
    with the tool?
  • Customisation How easy the end user make
    changes on the data representation provided by
    the tool?
  • Security How easy the end user can access
    unauthorised data?
  • Visualisation How easy the tool provide
    multi-dimensional graphical representations?

17
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