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Encoded Bitmap Indexing for data warehousing

Presented by Russell Myers Paper by Ming-Chuan Wu and Alejandro P. Buchmann. Encoded Bitmap Indexing for data warehousing. Problem. Optimizations and tuning designed for On-Line Transaction Processing rather than Data Warehouses Data Warehouses have specific needs Complex queries

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Encoded Bitmap Indexing for data warehousing

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  1. Presented by Russell Myers Paper by Ming-Chuan Wu and Alejandro P. Buchmann Encoded Bitmap Indexing for data warehousing

  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

  3. 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)

  4. 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

  7. 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?

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

  11. 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

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