1. 1 Towards a Synopsis Warehouse Peter J. Haas IBM Almaden Research Center San Jose, CA

2. 2 Acknowledgements: Kevin Beyer Paul Brown Rainer Gemulla (TU Dresden) Wolfgang Lehner (TU Dresden) Berthold Reinwald Yannis Sismanis

3. 3 Information Discovery for the Enterprise Syndicated Data Provider Crawlable/deep Web Company Data Semi-StructuredUnstructuredStructured Office documents E-Mail, Product Manuals ECM (reports, spreadsheets, Financial docs (XBRL)) ERP (SAP), CRM, WBI BPM, SCM Business-Object Discovery Query: “Explain the product movement, buyer behavior, maximize the ROI on my product campaigns.” Query: “The sales team is visiting company XYZ next week. What do they need to know about XYZ?” Content Metadata Business objects Enterprise Repository Analyze, Integrate Crawl, ETL Search Business Intelligence Order Account Customer Data Analysis &Similarity

4. 4 Motivation Challenge: Scalability –Massive amounts of data at high speed Batches and/or streams –Structured, semi-structured, unstructured data Want quick approximate analyses –Automated data integration and schema discovery –“Business object” identification –Quick approximate answers to queries –Data browsing/auditing Our approach: a warehouse of synopses

5. 5 A Synopsis Warehouse Full-Scale Warehouse Of Data Partitions Synop. S 1,1 S 1,2 S n,m Warehouse of Synopses merge S *,* S 1-2,3-7 etc

6. 6 Outline Synopsis 1: Uniform samples –Background –Creating and combining partitions Hybrid Bernoulli and Hybrid Reservoir algorithms –Updating partitions Stable datasets: random pairing Growing datasets: resizing algorithms Synopsis 2: AKMV samples –Goal: estimating the number of distinct values –Our choice of synopsis and estimator Relation to other work Analysis based on theory of uniform order statistics

7. 7 Synopsis 1: Uniform Samples Advantages –Flexible, used to produce other synopses –Mandatory if future use unknown a priori –Used in many statistical and mining methods –Building block for complex schemes Ex: stratified sampling Design goals –True uniformity (same sample size = same probability) –Bounded memory (no unpleasant surprises) –Memory efficiency (keep sample full) –Support for compressed samples Handle common case of few distinct values 80% of 1000 customer datasets had < 4000 distinct values

8. 8 Classical Uniform Methods Bernoulli sampling –Bern(q) independently includes each element with prob = q –Random, uncontrollable sample size Binomially distributed, Nq on average –Easy to merge: union of two Bern(q) samples is Bern(q) Reservoir sampling –Creates uniform sample of fixed size k –Insert first k elements into sample –Then insert ith element with prob. p i = k / i replace random victim Vitter optimization: Simulate coin tosses –Variants to handle large disk-resident samples –Merging more complicated than Bernoulli

9. 9 Drawback of Basic Methods Neither method is very compact –Especially when few distinct values E.g., DS = (, ) Stored as (A,A,…,A,B,B,…B) - 800 chars Concise sampling (GM 98) –Compact: purge Bern(q) sample S if too large Bern(q’/q) subsample of S  Bern(q’) sample –Not uniform (rare items under-represented)

10. 10 New Sampling Methods (ICDE ’06) Two flavors: –Hybrid reservoir (HR) –Hybrid Bernoulli (HB) Properties –Truly uniform –Bounded footprint at all times Not as memory efficient as Concise Sampling –Will store exact distribution if possible –Samples stored in compressed form –Merging algorithms available

11. 11 Basic Ideas: Sample Creation Phase 1 –Start by storing 100% sample compactly –Termination in Phase 1  exact distribution Abandon Phase 1 if footprint too big –Take subsample and expand –Fall back to reservoir(HR) or Bernoulli(HB) sampling (Phase 2) –HB can revert to reservoir sampling (Phase 3) Compress sample upon termination If Phase 2 termination –HR: uniform sample of size k –HB: uniform, (almost) Bernoulli sample Stay within footprint at all times –Messy details

12. 12 Basic Ideas: Merging Both samples in Phase 2 (usual case) –Bernoulli: equalize q’s and take union Take subsample to equalize q’s –Reservoir: take subsamples and merge Random (hypergeometric) subsample size Corner cases –One sample in Phase 1, etc. –See ICDE ’06 paper for details

13. 13 HB versus HR Advantages: –HB samples are cheaper to merge Disadvantages: –HR sampling controls sample size better –Need to know partition size in advance For subsampling during sample creation –Engineering approximation required

14. 14 Speedup: HB Sampling You derive “speed-up” advantages from parallelism with up to about 100 partitions.

15. 15 Speedup: HR Sampling Similar results to previous slide, but merging HR samples is more complex than HB samples.

16. 16 Linear Scale-Up HB Sampling HR Sampling

17. 17 Updates Within a Partition Arbitrary inserts/deletes (updates trivial) Previous goals still hold –True uniformity –Bounded sample size –Keep sample size close to upper bound Also: minimize/avoid base-data access

18. 18 New Algorithms (VLDB ’06+) Stable datasets: Random pairing –Generalizes reservoir/stream sampling Handles deletions Avoids base-data accesses –Dataset insertions paired randomly with “uncompensated deletions” Only requires counters (c g, c b ) of “good” and “bad” UD’s Insert into sample with probability c b / (c b + c g ) –Extended sample-merging algorithm Growing datasets: Resizing –Theorem: can’t avoid base-data access –Main ideas: Temporarily convert to Bern(q): requires base-data access Drift up to new size (stay within new footprint at all times) Choose q optimally to reduce overall resizing time –Approximate and Monte Carlo methods

19. 19 Multiset Sampling (PODS ’07) Bernoulli samples over multisets (w. deletions) –When boundedness not an issue –Problem: how to handle deletions (pairing?) Idea: maintain “tracking counter” –# inserts into DS since first insertion into sample (GM98) Can exploit tracking counter –To estimate frequencies, sums, avgs Unbiased (except avg) and low variance –To estimate # distinct values Maintaining tracking counter –Subsampling: new algorithm –Merging: negative result –Synopsis-warehouse subsampling and merging still OK

20. 20 Synopsis 2: AKMV Synopses (SIGMOD ’07) Goal: Estimate # distinct values –Dataset similarity (Jaccard distance) –Key detection –Data cleansing Within warehouse framework –Must handle multiset union, intersection, difference

21. 21 KMV Synopsis Used for a base partition Synopsis: k smallest hashed values –vs bitmaps (e.g., logarithmic counting) Need inclusion/exclusion to handle intersection Less accuracy, poor scaling –vs sample counting Random size K (between k/2 and k) –vs Bellman [DJMS02] minHash for k independent hash functions O(k) time per arriving value, vs O(log k) Can view as uniform sample of DV’s

22. 22 The Basic Estimator Estimator: –U (k) = kth smallest hashed value Properties (theory of uniform order statistics) –Normalized hashed values “look like” i.i.d. uniform[0,1] RVs Large-D scenario (simpler formulas) –Theorem: U (k) approx.= sum of k i.i.d exp(D) random variables –Analysis coincides with [Cohen97] –Can use simpler formulas to choose synopsis size

23. 23 Compound Partitions Given a multiset expression E –In terms of base partitions A 1,…,A n –Union, intersection, multiset difference Augmented KMV synopsis –Augment with counters –AKMV synopses are closed under multiset operations Modified unbiased estimator for # DVs in E See SIGMOD ’07 for details

24. 24 Experimental Comparison Unbiased SDLogLog Sample-CountingUnbiased-baseline 0 0.02 0.04 0.06 0.08 0.1 Absolute Relative Error

25. 25 For More Details "Toward automated large scale information integration and discovery." P. Brown, P. J. Haas, J. Myllymaki, H. Pirahesh, B. Reinwald, and Y. Sismanis. In Data Management in a Connected World, T. Härder and W. Lehner, eds. Springer-Verlag, 2005. “Techniques for warehousing of sample data”. P. G. Brown and P. J. Haas. ICDE ‘06. “A dip in the reservoir: maintaining sample synopses of evolving datasets”. R. Gemulla, W. Lehner, and P. J. Haas. VLDB ‘06. “Maintaining Bernoulli samples over evolving multisets”. R. Gemulla, W. Lehner, and P. J. Haas. PODS ‘07. “On Synopses for DistinctValue Estimation Under Multiset Operations” K. Beyer, P. J. Haas, B. Reinwald, Y. Sismanis, and R. Gemulla. SIGMOD 2007.

26. 26 Backup Slides

27. 27 Bernoulli Sampling –Bern(q) independently includes each element with probability q –Random, uncontrollable sample size –Easy to merge Bernoulli samples: union of 2 Bern(q) samp’s = Bern(q) +t 1 2/3 1/3 1 +t 2 2 2/31/3 2 1 1 2/31/3 30% 12 3 232 13112 3 2/31/32/31/32/31/32/31/3 +t 3 15% 7%15%7% 4% q = 1/3

28. 28 Reservoir Sampling (Example) Sample size M = 2 12 +t 1 +t 2 100% 12 1/3 3213 1/3 1/3 12 +t 1 +t 2 +t 3 33%

29. 29 Concise-Sampling Example Dataset –D = { a, a, a, b, b, b } Footprint –F = one pair Three (possible) samples of size = 3 –S 1 = { a, a, a }, S 2 = { b, b, b }, S 3 = { a, a, b }. –S 1 = { }, S 2 = { }, S 3 = {, }. Three samples should have with equal likelihood –But Prob(S 1 ) = Prob(S 2 ) > 0 and Prob(S 3 ) = 0 In general: –Concise sampling under-represents ‘rare’ population elements

30. 30 Hybrid Reservoir (HR) Sampling { }+a {,b}+b {, }+b +c {a, } (subsample) {a,b,b} (expand) {c,b,b} (reservoir sampling) +d{c,b,d} Ex: Sample capacity = two pairs or three values +a{a,a,a} { } (compress) { }+a … … done Phase 1 (Maintain exact frequency distribution) Phase 2 (Reservoir sampling) {, }+b

31. 31 Subsampling in HB Algorithm Goal: find q such that P{|S| > n F } = p Solve numerically: Approximate solution (< 3% error):

32. 32 Merging HB Samples If both samples in Phase 2 (the usual case) –Choose q as before (w.r.t. |D 1 U D 2 |) –Convert both samples to compressed Bern(q) [Use Bern(q’/q) trick as in Concise Sampling] –If union of compressed samples fits in memory then join and exit else use reservoir sampling (unlikely) See paper for remaining cases

33. 33 Merging a Pair of HR Samples If at least one sample in Phase 1 –Treat Phase-1 sample as “data stream” –Run HR sampling algorithm If both samples in Phase 2 –Set k = min(|S 1 |, |S 2 |) –Select L elements from S 1 and k – L from S 2 L has hypergeometric distribution on {0,1,…,k} –Distribution depends on |D 1 |, |D 2 | Take (compressed) reservoir subsamples of S 1, S 2 Join (compressed union) and exit

34. 34 Generating Realizations of L L is a random variable with probability mass function P(l) = P{ L=l } given by: for l = 0, 1, …. k-1 Simplest implementation –Compute P recursively –Use inversion method (probe cumulative distribution at each merge) Optimizations when |D|’s and |S|’s unchanging – Use alias methods to generate L from cached distributions in O(1) time

35. 35 Naïve/Prior Approaches unstableconduct deletions, continue with smaller sample (RS with deletions) Comments Technique Algorithm expensive, low space efficiency in our setting tailored for multiset populations Distinct-value sampling special case of our RP algorithm developed for data streams (sliding windows only) Passive sampling inexpensive but unstable“coin flip” sampling with deletions, purge if too large Bernoulli sampling with purging stable but expensiveimmediately sample from base data to refill the sample CAR(WOR) expensive, unstablelet sample size decrease, but occasionally recompute RS with resampling not uniformuse insertions to immediately refill the sample Naïve Counting samples Not uniform Modification of concise sampling

36. 36 Random Pairing

37. 37 Performance

38. 38 A Negative Result Theorem –Any resizing algorithm MUST access base data Example –data set –samples of size 2 –new data set –samples of size 3 Not uniform!

39. 39 Resizing: Phase 1 Conversion to Bernoulli sample –Given q, randomly determine sample size U = Binomial(|D|,q) –Reuse S to create Bernoulli sample Subsample if U < |S| Else sample additional tuples (base data access) –Choice of q small  less base data accesses large  more base data accesses

40. 40 Resizing: Phase 2 Run Bernoulli sampling –Include new tuples with probability q –Delete from sample as necessary –Eventually reach new sample size –Revert to reservoir sampling –Choice of q small  long drift time large  short drift time

41. 41 Choosing q (Inserts Only) Expected Phase 1 (conversion) time Expected Phase 2 (drifting) time Choose q to minimize E[T 1 ] + E[T 2 ]

42. 42 Resizing Behavior Example (dependence on base-access cost): –resize by 30% if sampling fraction drops below 9% –dependent on costs of accessing base data Low costs immediate resizing Moderate costs combined solution High costs degenerates to Bernoulli sampling

43. 43 Choosing q (w. Deletes) Simple approach (insert prob. = p > 0.5) –Expected change in partition size (Phase 2) (p)(1)+(1-p)(-1) = 2p-1 –So scale Phase 2 cost by 1/(2p-1) More sophisticated approach –Hitting time of Markov chain to boundary –Stochastic approximation algorithm Modified Kiefer-Wolfowitz

44. 44 Estimating the DV Count Exact computation via sorting –Usually infeasible Sampling-based estimation –Very hard problem (need large samples) Probabilistic counting schemes –Single-pass, bounded memory –Several flavors (mostly bit-vector synopses) Linear counting (ASW87) Logarithmic counting (FM85,WVT90,AMS, DF03) Sample counting (ASW87,Gi01, BJKST02)

45. 45 Intuition Look at spacings –Example with k = 4 and D = 7: –E[V]  1 / D so that D  1 / E[V] –Estimate D as 1 / Avg(V 1,…,V k ) –I.e., as k / Sum(V 1,…,V k ) –I.e., as k / u (k) –Upward bias (Jensen’s inequality) so change k to k-1