1. Presented by: Mariam John CSE 6392 02/14/2006
Dynamic Sample Selection for Approximate Query Processing Brian Babcock, Surajit Chaudari & Gautam Das
Presented by:
Mariam John CSE 6392
02/14/2006

2. Contents Introduction Dynamic Sample Selection
Policies for Sample Selection
Small Group Sampling
Pre-Processing Phase
Summary

3. Why do we do Approximate Query Processing?
Multi-gigabyte data repositories
Data Analysis Application
Data mining
Decision Support Analysis
Fast query response time
Acceptability of inexact query response

4. Problem
Constructing an optimal sample that well represents the underlying data.
Uniform sampling
Non-uniform sampling

5. Non-uniform sampling
Purpose is to produce more accurate results across a particular set of queries.
Produces more approximate results than uniform sampling.
Optimal bias differs from query to query.

6. Dynamic Sample Selection
DATA
SAMPLE
Dynamic Sample Selection
Standard Sampling
DATA
SAMPLE ? ?

7. Dynamic Sample Selection
Pre-Processing Phase
Query
Workload
Sample
Data
Select
Strata
Build
Sample
Data
Meta-
Data

8. Dynamic Sample Selection
Runtime Phase
Query
Sample
Data
Choose
Samples
Rewrite
Query
Meta-
Data

9. Dynamic Sample Selection
How to identify the set of biased samples to be created?
Occurs during pre-processing phase
How to determine which of the various samples to use to answer a query?
Occurs during runtime phase
Simplest and most efficient strategy is when choice of samples is guided by the syntax of incoming query.

10. Small Group Sampling
Specific dynamic sample selection technique which targets aggregate queries with “group-by’s”.
Small group sampling approach:
Overall sample – perform uniform sampling on large groups.
Small group tables-one or more sample tables for smaller groups.

11. Small group Sampling Set of small groups depends on: grouping columns
selection predicates

12. Small Group Sampling Idea behind Small Group Sampling:
Determine for which values in each column to create small group tables.
Create small group tables for each column of a table along with the overall sample.
During runtime, choose a subset of sample tables to answer a query most accurately.
Query is rewritten to run against the sample tables instead of the base tables.

13. Pre-processing Phase
For every column, identify the rare values within it and create small group tables.
Pre-processing phase produces three
outputs:
Overall sample table
Small group tables
Metadata table

14. Pre-processing phase Rows can appear in multiple sample tables.
Bitmask field is used to identify the set of sample tables to which a row was added.
Avoids double counting of rows assigned to multiple sample tables.

15. Summary Dynamic Sample Selection Small Group Sampling
Takes advantage of available disk space
Creates multiple biased sample tables during the pre-processing phase
Picks best samples during runtime for query processing.
Small Group Sampling
Notion is to treat large and small groups differently
Creates an overall sample table for large groups and a number of small group tables for each rare values in each column.