1. Efficient Computation of Combinatorial Skyline Queries Author: Yu-Chi Chung, I-Fang Su, and Chiang Lee Source: Information Systems, 38(2013), pp.369-387 Reporter: Yueh-Lin Lin 1

2. Outline  Introduction  Related Work  Combinatorial Skyline Query Processing  The Brute-Force Method  The Decomposition Algorithm (DA)  The Improved Decomposition Algorithm (IDA)  Performance Evaluation  Conclusions 2

3. Introduction  The skyline operator has received considerable attention from database community  Importance in numerous disciplines  Data mining, multi-criteria decision making, and market analysis 3

4. Skyline Example  Mercedes-Benz plans to increase automobile sales  Considering advertising  TV is the most effective mass-market advertising format  Advertising cost and audience number  Tries to find a best advertising slot  Costs lower and higher number of customers  The slots that meet Benz need form a skyline 4

5. Skyline Example 5

6. Motivation 6

7. Combinations of Two Advertising Slots 7

8. Combinatorial Skyline Query (CSQ) 8

9. Observation 9

10. Challenge 10

11. Related Work  After the skyline operator  Many algorithms are proposed for skyline query processing  BBS, bitmap, etc.  Variations of the skyline  Subspace skyline, k-dominate skyline, dynamic skyline, etc.  The concept of combination is not mentioned in previous work  Top-k combinatorial skyline queries (DASFAA 2010) 11

12. Problem 12

13. Combinatorial Skyline Query Processing The Brute-Force Method 13

14. The Brute-Force Method Example 14

15. The Brute-Force Method Example 15

16. The Decomposition Algorithm (DA)  The brute-force method incurs high computation overhead since it enumerates all combinations.  The Decomposition Algorithm  To find the combinatorial skyline tuples without enumerating all combinations 16

17. DA Example 17

18. The Improved Decomposition Algorithm (IDA) 18

19. Enhanced Pruning Example 19

20. The Improved Decomposition Algorithm Example 20

21. Performance Evaluation 21

22. Scalability with respect to Data Size Query Processing Time 22

23. Scalability with respect to Data Size Query Processing Time 23

24. Comparison on Real Dataset 24

25. The Real Dataset Processing Time Dimensionality 25

26. The Real Dataset Processing Time Cardinality 26

27. Conclusions  Proposed a new type of query  The combinatorial skyline query  Proposed two algorithms  DA  IDA  The experimental results show IDA better than DA in all performance metrics 27

28. On Skyline Groups Author: Nan Zhang, Chengkai Li, Naeemul Hassan, Sundaresan Rajasekaran, and Gautam Das Source: IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No. 4, April 2014, pp. 942-956 Reporter: Yueh-Lin Lin 28

29. Outline  Introduction  Skyline Group Problem  Finding Skyline Groups  Techniques  Algorithms  Experiments  Conclusions  Comments 29

30. Motivation 30

31. Challenge 31

32. Techniques 32

33. Skyline Group Problem 33

34. Aggregate Functions 34

35. Finding Skyline Groups 35

36. Finding Skyline Groups 36

37. Techniques 37

38. Output Compression  Number of skyline groups may be large, many of them share the same aggregate vector  Main idea  To store  Not all skyline groups  The distinct skyline aggregate vectors  One skyline group for each skyline vector 38

39. Input Pruning 39

40. Search Space Pruning: Anti-Monotonicity  To find and leverage two anti-monotonic properties for skyline search, analogy to the Apriori algorithm  Order-Specific Anti-Monotonic Property (OSM)  SUM, MIN and MAX  Weak Candidate-Generation Property (WCM)  MIN and MAX  The challenge is to find anti-monotonic properties that hold for skyline search  The main contribution is not about proving, but rather about finding the right ones that can effectively prune the search space. 40

41. Algorithm  Dynamic Programming Algorithm Based on Order- Specific Property  Iterative Algorithm Based on Weak Candidate- Generation Property 41

42. Dynamic Programming Algorithm Based on Order-Specific Property 42

43. Dynamic Programming Algorithm Based on Order-Specific Property 43

44. Experiments  The algorithms implemented in C+  Environment  Dell PowerEdge 2900 III server  Linux kernel 2.6.27-7  Dual Quad-Core Xeon 2.0 GHz  8GB RAM  250 GB HDD in RAID5 44

45. Datasets  NBA players (2009 season)  512 tuples (players)  5 attributes  Stocks (2009/12/31)  35000 tuples (stocks)  4 attributes  Synthetic data  1-10 million tuples  5 attributes 45

46. Aggregate Functions & Methods Compared  Aggregate functions  SUM, MIN, and MAX  Two algorithms compared with baseline method  Order-Specific Property (OSM)  Weak Candidate-Generation Property (WCM) 46

47. Comparison of Various Methods: SUM 47

48. Effect of Input Pruning 48

49. Conclusions  The novel problem of computing skyline groups  The novel algorithmic techniques  Output compression  Input pruning  Search space pruning  The experiments run the real and synthetic data sets to evaluate the proposed algorithms 49

50. Comments  Group skyline with constraint  NBA teams have salary limits  Parallel computing  MapReduce 50

51. Q&A 51