ISDA'2003 Data Mining Techniques in Materialised Projection View

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ISDA'2003 Data Mining Techniques in
Materialised Projection View

• Ying Wah Teh and Abu Bakar Zaitun
• tehyw_at_.um.edu.my, zab_at_um.edu.my
• University of Malaya
• Faculty of Computer Science and Information
  Technology

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Contents

• Introduction
• Motivation
• Research Objectives
• Data Mining Techniques
• Evaluation of Data Mining Prototypes
• Conclusion

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Introduction

• View
• A view is just a relation, but a view stores a
  definition, rather than a set of tuples.
• It creates to control the users access to
  information.
• It enforces security access rules based on
  virtual tables.
• Materialised view
• Physically storing the results of the view
• Decrease in cost of data storage
• Costly to create materialised views for every
  query.
• An intelligent way of handling the materialised
  view is one of the main issues of this research.

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Motivation

• The response time of a particular query is
  affected by
• Irrelevant attributes,
• Irrelevant tuples,
• As well as irrelevant attributes and tuples.

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Research Objectives

• the factors of irrelevant attributes.
• High priority users
• What are the important criteria or quality
  attributes that must be materialised in the
  relational/table for high priority users?
• Has the adoption of the high priority user
  accesss log file contributed to good response
  time?
• Are the high priority users access log files
  useful and practical?

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Conclusion

• encourage wider use of data mining techniques in
  the physical design of a data warehouse.
• Conceptual and practical
• Pre-processed data mining and data mining
  technique can help to generate the redundant data
  structures.
• Pre-processed data mining in high priority users
  access log file is one of most significant
  challenges in the research.

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Thank You

• Questions?
• tehyw_at_um.edu.my