Encoded Bitmap Indexing for data warehousing

1
Encoded Bitmap Indexing for data warehousing

• Presented by Russell Myers
• Paper by Ming-Chuan Wu and Alejandro P. Buchmann

2
Problem

• Optimizations and tuning designed for On-Line
  Transaction Processing rather than Data
  Warehouses
• Data Warehouses have specific needs
• Complex queries
• Huge volume of data returned as well as updated
• Database systems generally not built for image
  data

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Data Warehousing

• How do we retrieve data efficiently based on
  various metrics?
• Represented as a portion of the tuple
• Represented in metrics tables
• Represented as a defined data range (e.g.
  bitmaps)

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Tuple-based

• Including the metrics directly in the tuple
• Hard to index based on and search through
• Not easily updated and costs a lot of storage
  space (e.g. have to insert new metrics for every
  tuple)
• Traverse in linear time

5
Metrics Tables

• Data could be represented in associative metrics
  tables
• Less data to store just associations and the
  metric data
• Quicker search follow the metric to all the
  primary keys of the individual pieces of data
• Still linear

6
Bitmapping

• Each tuple contains a small portion of encoded
  bitmap data
• Can organize the bitmap into decision trees and
  use to search less time
• Insertion and amount of data is lessened
• Slice the data into various portions to represent
  multiple metrics and search through those

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Encoding Techniques

• Hierarchical encoding
• Ex Region to district to store
• Range
• Eliminate searching from things outside of a
  range (e.g. searching between 10 and 13 and
  excluding all others)
• Ordering

8
The Paper

• The paper seeks to prove that bitmapping
  techniques are the way to go
• Performance analysis of various bitmapping
  techniques
• Proofs of operations times

9
Critique

• Excellent start
• Explained the topic clearly
• Made the basis of the paper easy to understand
• Confounding expansion
• Confused by direction
• Differences between various bitmap techniques?

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Critique

• Bit slicing
• Mapping techniques
• Representations of bitmaps
• Metric graphical representation
• Advantages to different encoding techniques

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Contributions

• Bitmap indexing and tree structures bring faster
  data results
• Much cleaner than alternative means
• Easily represented
• Mapping situations are smaller than representing
  each piece of data