1. Lineage Processing over Correlated Probabilistic Databases Bhargav Kanagal Amol Deshpande University of Maryland

2. Motivation: Information Extraction/Integration [Gupta&Sarawagi’2006, Jayram et al. 2006] Structured entities extracted from text in the internet Reputed SENTIMENT ANALYSIS Location...located at 52 A Goregaon West Mumbai... ADDRESS SEGMENTATION CarAds INFORMATION EXTRACTION CORRELATIONS

3. Reputed SELECT SellerId FROM Location, CarAds, Reputed WHERE reputation = ‘good’ AND city = `Mumbai’ Location.SellerId = CarAds.SellerId AND CarAds.SellerId = Reputed.SellerId Why Lineage Processing ? [Das Sarma et al. 2006] Location CarAds List all “reputed” car sellers in “Mumbai” who offer Honda cars We need to compute the probability of the above boolean formula

4. Motivation: RFID based Event Monitoring [RFID Ecosystem UW, Diao et al. 2009, Letchner et al. 2009, KD 2008] A building instrumented with RFID readers to track assets / personnel found(PC, X, 2pm), prob = 0.9 RFID readings are noisy – Miss readings – Add spurious readings Subjected to probabilistic modeling Probabilities associated with events Spatial and Temporal correlations found(x,PC) ∧ found(z,PC) ∧ [found(y 1,PC) ∨ found(y 2,PC)] Was the PC correctly transferred from room A to the conference room ?

5. A Relational DBMS Data tables Uncertainty Parameters INDSEP Indexes Query Processor PARSER INDSEP Manager User insert into reputation values (‘z1’,219, uncertain(‘Good 0.5; Bad 0.5’); insert factor ‘0 0 1; 1 1 1’ in address on ‘y1.e’,‘y2.e’; PrDB System Overview [Kanagal & Deshpande SIGMOD 2009, SDG08, www.cs.umd.edu/~amol/PrDB/] Insert data + correlations Issue – SPJ queries – Inference queries – Aggregation queries

6. Outline Motivation & Problem definition [done] Background – Probabilistic Databases as Junction trees – Query processing over Junction trees – INDSEP Lineage Processing over Junction trees Lineage Processing using INDSEP Results

7. idYExists ? 134? 233? 325?.. 511? idYExists ? 134a 233b 325c.. 511q Background: ProbDBs as Junction trees Tuple Uncertainty Attribute Uncertainty Converted to Tuple Uncertainty Attribute Uncertainty Converted to Tuple Uncertainty Correlations Consise encoding of the joint probability distribution Query evaluation is performed directly over Junction Trees Forest of junction trees Random Variable 1 tuple exists 0 otherwise

8. Background: Junction trees Each clique and separator stores joint pdf (POTENTIAL) Tree structure reflects Markov property Given b, c: a independent of d p(a,b,c) p(b,c) p(b,c,d) Clique Separator Marginal: p(a,d) Joint distribution

9. Marginal Computation {b, c, n} Keep query variables Keep correlations Remove others Keep query variables Keep correlations Remove others PIVOT Steiner tree + Send messages toward a given pivot node For ProbDBs ≈ 1 million tuples, not scalable (1)Span of the query can be very large – almost the complete database accessed even for a 3 variable query (2)Searching for cliques is expensive: Linear scan over all the nodes is inefficient

10. 50 ops Shortcut Potentials How can we make marginal computation scalable ? 100 ops Shortcut Potential Junction tree on set variables {c, f, g, j, k, l, m} Shortcut Potential Junction tree on set variables {c, f, g, j, k, l, m} (1)Boundary separators (2)Distribution required to completely shortcut the partition (3)Which to build ? (1)Boundary separators (2)Distribution required to completely shortcut the partition (3)Which to build ?

11. Root I1I1 I1I1 I2I2 I2I2 I3I3 I3I3 P1P1 P1P1 P2P2 P2P2 P6P6 P6P6 P5P5 P5P5 P4P4 P4P4 P3P3 P3P3 INDSEP - Overview 1.Variables: {a,b,..} {c,f,..} {j,n..q} 2.Child Separators: p(c), p(j) 3.Tree induced on the children 4.Shortcut potentials of children: {p(c), p(c,j), p(j)} 1.Variables: {a,b,..} {c,f,..} {j,n..q} 2.Child Separators: p(c), p(j) 3.Tree induced on the children 4.Shortcut potentials of children: {p(c), p(c,j), p(j)} Obtained by hierarchical partitioning of the junction tree Actual Construction: [Kanagal & Deshpande SIGMOD 2009]

12. Computing Marginals using INDSEP Root I1I1 I1I1 I2I2 I2I2 I3I3 I3I3 P1P1 P1P1 P2P2 P2P2 P6P6 P6P6 P5P5 P5P5 P4P4 P4P4 P3P3 P3P3 {b, c, n} {b, c} {n} {b, c} {c, j} {j, n} {b, c, n} Intermediate Junction tree Intermediate Junction tree [Kanagal & Deshpande SIGMOD 2009] Recursion on INDSEP

13. Outline Motivation & Problem definition [done] Background [done] – Junction trees & Query processing over junction trees – INDSEP Lineage Processing over Junction trees Lineage Processing using INDSEP Results

14. Lineage Processing Typically classified into 2 types Read-Once (a ∧ b) ∨ (c ∧ d) Non-Read-Once (a ∧ b) ∨ (b ∧ c) ∨ (c ∧ d) The problem of lineage processing is #P- complete in general for correlated probabilistic databases, even for read-once lineages Reduction from #DNF

15. Lineage Processing on Junction trees Naïve: (a ∧ b) ∨ (c ∧ d) p(a, b, c, d) p(a, b, a ∧ b, c, d) p(a ∧ b, c, d) p(a ∧ b, c, d, c ∧ d) p(a ∧ b, c ∧ d) p((a ∧ b) ∨ (c ∧ d)) Multiply with p(a ∧ b|a,b) Eliminate a,b Multiply / Eliminate Evaluate marginal query over variables in formula COMPLEXITY (1)Simplifcation (name of the above process) (2)Dependent on the size of the intermediate pdf (3)Here, it is at least (n+1) (#terms in the formula) (4)Not scalable to large formulae Multipl y Eliminate

16. Lineage Processing [Optimization opportunities] 1. EAGER Exploit conditional independence & simplify early p(a, c, d) p(a, c ∧ d) PIVOT Query: (a ∧ b) ∨ (c ∧ d) [Kanagal & Deshpande SIGMOD 2010] p(a, d)

17. Lineage Processing [Optimization opportunities] [Kanagal & Deshpande SIGMOD 2010] p(c, f, g, h, m ∧ n) p(c, h, m ∧ n) p((c ∧ h) ∨ (m ∧ n)) p(f, h) p(g,m ∧ n) p(c, f, g)p(c, f, g, h) p(g,m ∧ n) p(g, c ∧ h) p(c ∧ h, m ∧ n) Max pdf: 5 Distribute simplification into the product 2. EAGER+ORDER (c ∧ h) ∨ (m ∧ n) Max pdf: 4 How to compute good ordering ?

18. Lineage Processing [Pivot Selection] Also influences the intermediate pdf size Optimal Pivot: Only n possible choices, estimate pdf size for each pivot location Pivot = (ab) Pivot = (cfg) (b ∧ c) ∨ g Max pdf: 3 Max pdf: 4

19. Outline Motivation & Problem definition [done] Background [done] – Junction trees & Query processing over junction trees – INDSEP Lineage Processing over Junction trees [done] Lineage Processing using INDSEP Results

20. Lineage Processing using INDSEP Root I1I1 I1I1 I2I2 I2I2 I3I3 I3I3 P1P1 P1P1 P2P2 P2P2 P6P6 P6P6 P5P5 P5P5 P4P4 P4P4 P3P3 P3P3 {b, c, d, e} {n, o} {j, n ∨ o} {b ∧ c, d ∨ e, c} {c, j} Recursion bottomed out using EAGER+ORDER (b ∧ c) ∨ (( d ∨ e) ∧ ( n ∨ o) ) But what is the running time ?

21. Lineage Planning Phase (b ∧ c) ∨ (( d ∨ e) ∧ ( n ∨ o) ) QUERY PLAN Estimate maximum intermediate pdf size at each node 4 5644 7 4 If a node exceeds a threshold, do approximations to estimate probability In addition, modify query plan for: – Multiple lineages that share variables – Exploiting disconnections

22. Results Query Processing times for different heuristics Query Processing times for different heuristics Datasets (1)D1: Fully independent (2)D2: Correlated (3)D3: Highly Correlated (long chains) NOTE: LOG scale Comparison Systems (1)NAIVE (2)EAGER (3)EAGER + ORDER EAGER+ORDER is much more efficient than others

23. Results Query Processing time vs Lineage size Query Processing time vs Lineage size NOTE: LOG scale Ratio vs Sharing factor Ratio vs Sharing factor Multiquery processing exploits sharing Highly dependent on size of lineage

24. Conclusions Proposed a scalable system for evaluating boolean formula queries over correlated probabilistic databases Future – Plan to further the approximation approaches – Envelopes of boolean formulas for upper and lower bounds Thank you

25. Lineage Processing (contd.) Amount of simplification possible when nodes are multiplied Construct complete graph on factors to be multiplied p(g, c ∧ h) p(f, h)p(c, f, g) 4 - 2 1.Pick the biggest edge 2.Merge / Simplify nodes together 3.Recompute new edge weights

26. Lineage Processing via INDSEP [Improvement 1] Multiple Lineage Processing: Exploit possibility of sharing Root I1I1 I1I1 I2I2 I2I2 I3I3 I3I3 P1P1 P1P1 P2P2 P2P2 P6P6 P6P6 P5P5 P5P5 P4P4 P4P4 P3P3 P3P3 {c, g, j} {j, m} {c, g, j} {j, n} (m ∧ c) ∨ g (n ∧ c) ∨ g Sharing across multiple levels Need not even share variables, just paths

27. Lineage Processing via INDSEP [Improvement 2] Extend to forest of junction trees: Real world data sets may have independences Root I1I1 I1I1 I2I2 I2I2 I3I3 I3I3 P1P1 P1P1 P2P2 P2P2 P6P6 P6P6 P5P5 P5P5 P4P4 P4P4 P3P3 P3P3 Index constructed to minimize disk wastage, combining forests together (a ∧ o) {a, c} {j, o} {a, c} {c, j} {j} {o} j and o are disconnected !! a and o are disconnected !! Preprocess formula, keep variables in connected components together

28. Lineage Processing via INDSEP [Improvement 3] What about complexity ? Complexity not evident from the algorithm Root I1I1 I1I1 I2I2 I2I2 I3I3 I3I3 P1P1 P1P1 P2P2 P2P2 P6P6 P6P6 P5P5 P5P5 P4P4 P4P4 P3P3 P3P3 {b, c, d, e} {n, o} {j, n ∨ o} {b ∧ c, d ∨ e, c} {c, j} {d ∨ e} {b ∧ c, c} {j, n} {o} Compute lwidth here Intermediate junction tree “Predict” how large the intermediate cliques will be Approximate for all portions whose estimate is more than a threshold, e.g., 10