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

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

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)

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

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

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

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

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

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?

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

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