1. Machine Learning for Online Query Relaxation
Ion Muslea
SRI International
333 Ravenswood
Menlo Park, CA 94025
Published in SIGKDD

2. Ion Muslea Director of Research & Product Development,
Scalability and Big Data at SDL

3. What is paper is about? given a query that returns an empty
Failing query problem
given a query that returns an empty
answer, how can one relax the query’s
constraints so that it returns a
non-empty set of tuples?

4. Motivation

5. Motivation

6. Why Query fails? Too many constraints
Database does not have enough tuples
Users often want everything to be satisfied

7. Intuition Relax failing queries Relax the constraints
Discover implicit relationships among various domain attributes
Use this knowledge to relax the constraints

8. Intuition explained And It Fails!!!
- laptops that have large screens (i.e., Display ≥ 1700) weigh more than three pounds;
- fast laptops with large hard disks (CPU ≥ 2.5GHz And HDD ≥60GB) cost more than $2,000.

9. Intuition Formalized Step 1: Extracting domain knowledge
In the form of rules
Step 2: Finding the “most useful” rule
Step 3: Relaxing the failing query

10. Intuition explained Step 1: Extracting domain knowledge
Randomly-chosen subset, D’
For each constraint in Q0 (e.g., CPU ≥ 2.5 GHz), use D’ to find patterns that predict whether this constraint is satisfied

11. Intuition explained
Step 1: Extracting domain knowledge

12. Intuition explained
Step 1: Extracting domain knowledge

13. Intuition explained Step 2: Finding the “most useful” rule
Converts rules to existential statements

14. Intuition explained Step 2: Finding the “most useful” rule
Find the Q1 which is the most similar to Q0
How? (nearest-neighbor techniques)

15. Intuition explained
Step 3: Relaxing the failing query

16. Intuition explained Step 3: Relaxing the failing query
How to get Qr from Q1 and Q0?
dropping the original constraint on the hard disk
keeping the constraint on CPU unchanged
setting the values in the constraints on Price, Display, and W eight to the least constraining ones

17. Intuition explained Step 3: Relaxing the failing query
Price and HDD are dropped out

18. LOQR
Learning for Online Query Relaxation

19. LOQR

20. Step 1: Extracting domain knowledge

21. Step 2: Finding the “refiner statement”

22. Step 3: Refining the failing conjunction

23. EXPERIMENTS Five algorithms evaluated Loqr
loqr-50 (one variant of loqr)
loqr-90 (another variant of loqr)
S-nn (Baseline 1)
r-nn (Baseline 2)

24. S-nn Find the example Ex ∈ D that is the most similar to Ck
Use Ex to create a conjunction Ck’
Use Ck’ as the relaxed query
Doesn’t learn rules at all

25. r-nn Apply s-nn uses Ck’ to relax Ck and apply the relaxed rule
Doesn’t learn rules at all

26. loqr-50 Vs loqr-90
Generate over-relaxed queries that are highly unlikely to fail, but return a (relatively) large number of tuples [loqr-90]
Allow 90% of the possible tuples VS
Create under-relaxed queries that return fewer tuples, but are more likely to fail [loqr-50]
Allow 50% of the possible tuples

27. loqr-50 Vs loqr-90

28. Datasets Six different datasets Laptops
1257 laptop configurations extracted from yahoo.com
five numeric attributes: price, CPU speed, RAM, HDD space and weight
Other Five (UC Irvine repository)
breast cancer Wisconsin (bcw)
low resolution spectrometer (lrs)
Pima Indians diabetes (pima)
water treatment plant (water)
waveform data generator (wa ve)

29. The Setup
Given a failing query Q and a dataset D, each algorithm uses D to generate a relaxed query QR
QR is then evaluated on a test set that consists of all examples in the target database except the ones in D
Dataset D variations:
50, 100, 150, , 350 examples
100 arbitrary instances of D for each number

30. The Setup 7 failing queries
For each query, each query relaxation algorithms is run 100 times
The results reported here are the average of these 700 runs

31. Performance measures Robustness Coverage
what percentage of the failing queries are successfully relaxed (i.e., they don’t fail anymore)?
Coverage
what percentage of the examples in the test set satisfy the relaxed query?

32. Results (Robustness)

33. Results (Robustness) Loqr obtains by far the best results
s-nn and r-nn, display extremely poor robustness
Loqr-50 and loqr-90 are better than baselines

34. Results (Coverage)

35. Results (Coverage) Low-coverage results preferred? !
A low-coverage, non-robust algorithm is of little practical importance
loqr - not so spectacular!!
loqr-90 is excellent (authors claim )
robustness levels between 69% and 98%
coverage under 5%

36. Time complexity loqr is extremely fast Depends on the
size of the dataset D
the number of attributes in the query
creates a new dataset for each attribute in the query. the more attributes in the query , the longer it takes to process the query .

37. Online vs offline learning
OFF-k, an offline variant
performs the learning step only once, independently of the constraints
for discrete attributes:
learns to predict each discrete value from the values of the other attributes
for continuous attributes
it discretizes the attribute’s range of values in D

38. Online vs offline learning
Two offline versions (i.e., k = 2 and k = 3) for both loqr and loqr-90

39. Online vs offline learning
both loqr and loqr-90 clearly outperform their offline variants

40. Query-driven learning
four main scenarios
no constraints
Offline
class-attribute constraints
LOQR
set of hard constraints
subset of constraints that must be satisfied
all constraints
simultaneously replacing the original values of all the attributes

41. Some Issues Sampling Create separate datasets from each attribute?
Entirely Random?
Create separate datasets from each attribute?
C4.5 is a greedy algorithm
Is that a problem?
Use only the closet rule to relax the query?
Why not use more?

42. Conclusion Novel, data-driven approach to query relaxation
loqr is a fast algorithm
Successfully relaxes the vast majority of the failing queries