Presentation is loading. Please wait.

Presentation is loading. Please wait.

Optimal Top-k Generation of Attribute Combinations based on Ranked Lists Jiaheng Lu, Renmin University of China Joint work with Pierre Senellart, Chunbin.

Similar presentations


Presentation on theme: "Optimal Top-k Generation of Attribute Combinations based on Ranked Lists Jiaheng Lu, Renmin University of China Joint work with Pierre Senellart, Chunbin."— Presentation transcript:

1 Optimal Top-k Generation of Attribute Combinations based on Ranked Lists Jiaheng Lu, Renmin University of China Joint work with Pierre Senellart, Chunbin Lin, Xiaoyong Du, Shan Wang, and Xinxing Chen

2 Motivation & Problem Statement Goal: Select a combination of three players including forward, center, and guard positions. Methods select players with the highest score in each group calculate the average scores of players across all games … Limitation: overlook team spirit Game ID Score

3 Motivation & Problem Statement consider their combined scores in the same game Select the top-k combinations according to top-m aggregate scores Top-k,m Problem Tuple aggregation function Instance aggregation function

4 Motivation & Problem Statement Top-1,2: select the top-1 combination of players according to top-2 aggregate scores for games where they played together. F2C1G1 is the best combination, since ( ) is the highest overall score.

5 Difference between top-k queries and top-k,m queries Top-k Top-k,m Return the top-k tuples Return the top-k combinations of attributes Can be transformed into a SQL Cannot be transformed into a SQL

6 Application XML keyword refinement Example Q = {DB;UC Irvine; 2002} Groups: G1 = {"DB"; "database"}, G2={"UCI";"UC Irvine"} G3 = {"2002"}. Answer: Q={DB, UCI, 2002} Consider a top-1,2 query

7 Application (Cont.) Evidence combination mining in medical databases Package recommendation systems …

8 Motivation & Problem Statement Top-k,m Query Processing Experimental Results Conclusion Outline

9 Top-k,m Query Processing Access Model: Sorted Accesses (a, 9.0) (b, 8.7) (c, 8.7) (d, 7.4) (i, 5.3) …… (i, 8.8) (f, 6.9) (a, 7.5) (d, 4.7) (c, 7.9) ……

10 Top-k,m Query Processing Access Model: Random Accesses (a, 9.0) (b, 8.7) (c, 8.7) (d, 7.4) (i, 5.3) …… (i, 8.8) (f, 6.9) (a, 7.5) (d, 4.7) (c, 7.9) ……

11 Top-k,m Query Processing Baseline Method: ETA Compute top-m tuples for each combination Threshold Algorithm (TA) Calculate aggregate score for each combination Return the top-k combinations

12 Top-k,m Query Processing Upper and Lower bounds Algorithm: ULA Consider top-m seen match instances Lower Bound Consider threshold value and top-m match instances Upper Bound Compute the upper and lower bounds for each combination Termination condition: k combinations meet the hit-condition

13 (G1, 9.3) (G2, 8.3) (G5,7.8) (G11,7.3) (G2, 7.9) (G1,7.0) (G4,8.0) (G8,7.3) (G8, 3.0) (G4, 2.6) (G4, 1.8) (G2, 1.5) (G11, 4.2) (G5, 3.3) (G2, 4.4) (G1, 2.3) Upper and Lower bounds Algorithm: ULA A1B1

14 (G1, 9.3) (G2, 8.3) (G5,7.8) (G11,7.3) (G2, 7.9) (G1,7.0) (G4,8.0) (G8,7.3) (G8, 3.0) (G4, 2.6) (G4, 1.8) (G2, 1.5) (G11, 4.2) (G5, 3.3) (G2, 4.4) (G1, 2.3) Upper and Lower bounds Algorithm: ULA A1B1 U: 34.4 L: 32.5 A1B2 U: 34.6 L: 22.2 A2B1 U: 31.4 L: 20.5 A2B2 U: 31.6 L: 9.8 L: 32.5 A1B1 G1, , G2, U: 31.4 A2B1 Threshold value= =15.7, 15.7*2=31.4

15 (G1, 9.3) (G2, 8.3) (G5,7.8) (G11,7.3) (G2, 7.9) (G1,7.0) (G4,8.0) (G8,7.3) (G8, 3.0) (G4, 2.6) (G4, 1.8) (G2, 1.5) (G11, 4.2) (G5, 3.3) (G2, 4.4) (G1, 2.3) Upper and Lower bounds Algorithm: ULA A1B1 U: 32.5 L: 32.5 A1B2 U: 31.2 L: 22.2 A1B1

16 Can we run fast?

17 Optimization heuristics (1) Pruning combinations without computing the bounds (A3,B2) is dominated by (A2,B1) 6.3<7.1 and 8.0<8.2

18 Optimization heuristics (2) Reducing the number of accesses Avoiding both sorted and random accesses for specific lists (A1,B1)and(A1,B2) cannot be part of answers, all sorted accesses and random accesses on list A1 are unnecessary.

19 Optimization heuristics (3) Reducing the number of accesses Reducing random accesses across two lists (A1,B1,C1)and(A1,B1,C2) cannot be part of answers, random accesses between A1 and B1 are unnecessary.

20 Optimization heuristics (4) Reducing the number of accesses Eliminating random accesses for specific tuples Random access from L e to L t for tuple x is useless

21 Prune dominated combinations Compute upper and lower bounds for unterminated combinations Terminate combinations by reducing number of accesses Until k combinations meet hit- condition Top-k,m Query Processing ULA+

22 Interesting theoretical results Optimality properties Instance Optimality If wild guesses are not allowed, and the size of each group is treated as a constant, then ULA and ULA+ are instance-optimal. The upper bound of the optimality ratio is tight for every instance there exist two constants a and b such that cost(A) <= a*cost(A) + b

23 Interesting theoretical results (Cont.) Optimality properties No Instance Optimal Algorithms If wild guesses are allowed, Then there is no deterministic algorithm that is instance-optimal.

24 Motivation & Problem Statement Top-k,m Query Processing Experimental Results Conclusion Outline

25 Experimental Results Experimental Setup Language: Java; OS: Windows XP; CPU: 2.0GHz; Disk:320GB Data sets

26 Experimental Results Experimental results on NBA and YQL datasets ULA+ outperforms ETA by 1-2 orders of magnitude both in running time and access number.

27 Experimental Results Performance of optimization to reduce combinations More than 60% combinations are pruned without computing their bounds

28 Experimental Results Performance of different optimizations Combination of all optimizations has the most powerful pruning capability.

29 Experimental Results Experimental results on XML DBLP dataset XULA and XULA+ perform better than XETA and scale well in both running time and number of accesses.

30 U. Güntzer etc, VLDB2000 S. Nepal etc, ICDE1999 Fagin etc, PODS 2001 Top-k with both random and sorted accesses R. Fagin etc, JCSS2003 N. Mamoulis etc, TDS2007 Fagin etc, PODS 2001 Top-k with only sorted accesses Related Works

31 I. F. Ilya etc, VLDB2002 Top-k with no need for exact aggregate score C. Li etc, SIGMOD2006 M. L. Yiu etc, DKE2008 Ad-hoc top-k queries Related Works N. Bruno etc, ICDE2002 K. C. C. Chang etc, SIGMOD2002 Top-k with sorted access on restricted lists

32 Related Works T. Deng, W, Fan and F. Geerts, On the Complexity of Package Recommendation Problems PODS 2012 Top-k Package recommendation

33 Motivation & Problem Statement Top-k,m Query Processing Experimental Results Conclusion Outline

34 Conclusion Propose a new problem called top-k,m query evaluation Developed a family of efficient algorithms, including ULA and ULA+ Study the optimality properties of our algorithms Apply top-k,m query to the context of XML keyword query refinement

35 Optimal Top-k Generation of Attribute Combinations based on Ranked Lists


Download ppt "Optimal Top-k Generation of Attribute Combinations based on Ranked Lists Jiaheng Lu, Renmin University of China Joint work with Pierre Senellart, Chunbin."

Similar presentations


Ads by Google