1. Anthony Okorodudu CSE 6392 2006-2-7
ICICLES: Self-tuning Samples for Approximate Query Answering By Venkatesh Ganti, Mong Li Lee, and Raghu Ramakrishnan
Anthony Okorodudu
CSE 6392

2. ICICLES: Self-tuning Samples for Approximate Query Answering
Outline
Introduction
Uniform random sampling
Icicles
Icicle maintenance
Maintaining frequency relation
Estimators for aggregate queries
Quality Guarantee
Performance evaluation
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

3. ICICLES: Self-tuning Samples for Approximate Query Answering
Introduction
Analysis of data in data warehouses useful in decision support
Users of decision support systems want interactive systems
Most decision support systems can tolerate approximate results
Approximate query answering systems (AQUA)
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

4. ICICLES: Self-tuning Samples for Approximate Query Answering
Approximate Querying
Various approaches to answering approximate queries
Sampling-based
Histogram-based
Clustering
Probabilistic
Wavelet-based
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

5. Uniform Random Sampling
Sales
S_sales
Branch
State
Sales 1 CA
80K 2 TX
42K 3
40K 4 5
75K 6
48K 7
55K 8
38K 9 10
41K
50%
Sample
Branch
State
Sales 2 TX
42K 4 CA 6
48K 8
38K 10
41K
scale factor
SELECT SUM(sales) x 2 AS cnt
FROM s_sales
WHERE state = ‘TX’
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

6. ICICLES: Self-tuning Samples for Approximate Query Answering
Biased Sampling
Sales
S_sales
Branch
State
Sales 1 CA
80K 2 TX
42K 3
40K 4 5
75K 6
48K 7
55K 8
38K 9 10
41K
Branch
State
Sales 2 TX
42K 4 CA 5
75K 7
55K 8
38K
Sample relation for aggregation query workload regarding Texas branches
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

7. ICICLES: Self-tuning Samples for Approximate Query Answering
Class of samples to capture data locality of aggregate queries of foreign key joins
Join synopsis is the join of a uniform random sample of the fact table with a set of dimension tables
Sample relation space better utilized if more samples from actual result set are present
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

8. Icicle-Based Estimators
Icicle is a non-uniform sample of the original relation
Traditional scaling up not appropriate for icicles
Frequency must be maintained for each tuple
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

9. ICICLES: Self-tuning Samples for Approximate Query Answering
Reasoning
Accuracy of approximate answer proportional to number of tuples used for computation
If a lot of queries in workload use the frequent set of tuples, then average quality of answer improves
Drastic changes to workload or queries that don’t conform to workload have less accuracy than static sample
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

10. ICICLES: Self-tuning Samples for Approximate Query Answering
Icicle Maintenance
Probability of tuples presence is proportional to its importance in answering queries in workload
Tuple is selected for icicle base on its frequency
A workload where all tuples are retrieved equally frequently is a uniform workload
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

11. ICICLES: Self-tuning Samples for Approximate Query Answering
Icicle Maintenance
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

12. Icicle Maintenance Example
SELECT average(*)
FROM widget-tuners
WHERE date.month = ‘April’
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

13. Estimators for Aggregate Queries
Traditional estimators can’t be used due to selection bias and duplicates in icicle
Average is the average of distinct tuples in sample satisfying query
Doesn’t require frequency attribute
Count is the sum of expected contributions of all tuples in icicle that satisfy the query
Sum is the product of the average and count
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

14. ICICLES: Self-tuning Samples for Approximate Query Answering
Quality Guarantees
If queries in workload exhibit data locality, then the icicles contain tuples from frequently accessed subset of relation
A new query to workload is more accurate than uniform random sample if query accesses frequent tuples in icicle
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

15. Performance Evaluation
SELECT COUNT(*), AVG(LI_Extendedprice), SUM(LI_Extendedprice)
FROM LI, C, O, S, N, R
WHERE C_Custkey=O_Custkey AND O_Orderkey=LI_Orderkey AND LI_Suppkey=S_Suppkey AND
C_Nationkey = N_Nationkey AND N_Regionkey = R_Regionkey AND
R Name = [region] AND O Orderdate >= Date[startdate] AND O Orderdate <=
Qworkload: Template for generating workloads
SELECT COUNT(*), AVG(LI_Extendedprice), SUM(LI_Extendedprice)
FROM LICOS-icicle, N, R
WHERE C_Nationkey = N_Nationkey AND N_Regionkey = R_Regionkey AND
R Name = [region] AND O Orderdate >= Date[startdate] AND O Orderdate <=
Template for obtaining approximate answers
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

16. Performance Evaluation
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

17. Performance Evaluation: Mixed Workload
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

18. ICICLES: Self-tuning Samples for Approximate Query Answering
Conclusion
Icicles are a new class of samples that are sensitive to workload characteristics
Icicles adapt quickly to changing workload
Experiments show that icicle are good when workload focuses on relatively small subsets in relation
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

19. ICICLES: Self-tuning Samples for Approximate Query Answering
References
V. Ganti, M. Lee, and R. Ramakrishnan. ICICLES: Self-tuning Samples for Approximate Query Answering. VLDB Conference 2000.
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering

20. ICICLES: Self-tuning Samples for Approximate Query Answering
Thanks
2006/2/7
ICICLES: Self-tuning Samples for Approximate Query Answering