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Creating Competitive Products

Creating Competitive Products. Qian Wan [1] , Raymond Chi-Wing Wong [1] , Ihab F. Ilyas [2] , M. Tamer Ozsu [2] , Yu Peng [1] [1] Hong Kong University of Science and Technology [2] University of Waterloo Presented by Qian Wan Prepared by Qian Wan. Outline. Background

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Creating Competitive Products

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  1. Creating Competitive Products Qian Wan[1], Raymond Chi-Wing Wong[1], Ihab F. Ilyas[2], M. Tamer Ozsu[2], Yu Peng[1] [1] Hong Kong University of Science and Technology [2] University of Waterloo Presented by Qian Wan Prepared by Qian Wan

  2. Outline • Background • Skyline, Related Work • Motivation • Examples,Problem Definition • Algorithm • Framework, Grouping, Pruning • Experiments • Synthetic, Real data • 6 factors • Conclusions

  3. Skyline • Definition • Skyline contains the points which are not dominated by others • Hotel searching problem • Distance to beach VS Price • Dominance • Skyline H1 H2 Dist Dist H3 H4 H6 H5 H7 H8 H2 H9 H1 Price Price

  4. Related Work • Skyline Queries in DBMS [S.Borzsonyi, 2001] • Single Table Skyline Queries • Bitmaps[K.L. Tan,2001], Nearest Neighbor[D.Kossomann, 2002], Branch and Bound Skylines[D.Papadias, 2005] • Multi-Table Skyline Queries • Natural Join [W.Jin, 2007][D.Sun, 2008] • Our Work • Join different source tables via a “Cartesian product” like procedure.

  5. Outline • Background • Skyline, Related Work • Motivation • Examples,Problem Definition • Algorithm • Framework, Grouping, Pruning • Experiments • Synthetic, Real data • 6 factors • Conclusions

  6. A Travel Agency’s Database Existing Vacation Packages Newly Created Vacation Packages Skyline tuples Direct attributes Indirect attributes One indirect attribute characteristic e.g. Travel Agency (Price), PC Manufacture(Price) and Logistic Transportation Service (Price) Source Tables

  7. Finding Competitive Products • Given a set of source tables • Market packages • New packages • Then, a tuple q in TQ is said to be competitive product if q is in Skyline with respect to

  8. Naïve Solution Intra-dominance checking Inter-dominance checking Existing Vacation Packages Newly Created Vacation Packages Competitive Products Source Tables

  9. Outline • Background • Skyline, Related Work • Motivation • Examples,Problem Definition • Algorithm • Framework, Grouping, Pruning • Experiments • Synthetic, Real data • 6 factors • Conclusions

  10. Algorithm Overview • Intra-dominance checking (Framework) • To Find Skyline in Source Tables • Inter-dominance checking • Skyline in Existing Market Packages • R* Tree Indies in Existing Market Packages • Full Pruning • Partial Pruning • Post-processing

  11. Intra-dominance Checking NO intra-dominance checking(one indirect attribute) NO competitive products are missing Competitive Products Newly Created Vacation Packages Skyline Tuples of Source Tables

  12. Algorithm Overview • Intra-dominance checking (Framework) • To Find Skyline in Source Tables • Inter-dominance checking • Skyline in Existing Market Packages • R* Tree Indies in Existing Market Packages • Full Pruning • Partial Pruning • Post-processing

  13. Inter-dominance Checking Inter-dominance Checking  Range query Existing Vacation Packages Skyline in Existing Vacation Packages No Missing Competitive Products R* Tree will speedup the inter-dominance checking

  14. Algorithm Overview • Intra-dominance checking (Framework) • To Find Skyline in Source Tables • Inter-dominance checking • Skyline in Existing Market Packages • R* Tree Indies in Existing Market Packages • Full Pruning • Partial Pruning • Post-processing

  15. Grouping Full Pruning Existing Vacation Packages A1 C1={A1, B1} A2 C4={A2, B2} B1 B2 Newly Created Vacation Packages Skyline Tuples of Source Tables Competitive Products

  16. Full Pruning Best Representative Quality of Best Representative: tightness of each group (Clustering, e.g. KMeans)

  17. Algorithm Overview • Intra-dominance checking (Framework) • To Find Skyline in Source Tables • Inter-dominance checking • Skyline in Existing Market Packages • R* Tree Indies in Existing Market Packages • Full Pruning • Partial Pruning • Post-processing

  18. Partial Pruning • Partial Pruning • Full pruning prunes all members in the group • Partial pruning prunes some members in the group • Partial pruning is used when full pruning cannot be applied • Idea • Direct attribute does not change • Estimate the best possible value for indirect attributes • Eliminate a combination , if • It is dominated on all direct attributes • It is dominated on all indirect attributes according to their best estimation

  19. Algorithm Overview • Framework • Intra-dominance checking • To Find Skyline in Source Tables • Inter-dominance checking • Skyline in Existing Market Packages • R* Tree Indies in Existing Market Packages • Full Pruning • Partial Pruning • Post-processing

  20. Post-processing • More than one indirect attributes • Calculation • Previous algorithm  Intra-dominance checking • Any existing Skyline algorithm • Post-processing cost depends on the size of Competitive Products

  21. Outline • Background • Skyline, Related Work • Motivation • Examples,Problem Definition • Algorithm • Framework, Grouping, Pruning • Experiments • Synthetic, Real data • 6 factors • Conclusions

  22. Experiments • Pentium IV 2.4GHz PC with 4GB memory, Linux platform, C++ • Synthetic anti-correlated datasets • Real datasets, Travel Agency A and Travel Agency B • A, 296 packages, 1014 hotels and 4394 flights • B, 149 packages, 995 hotels and 866 flights • Implementation • Algorithm for Creating Competitive Products (ACCP) • Baseline algorithm • Naïve algorithm

  23. Synthetic Datasets • Schema is the same as example • Anti-correlated • 6 factors • Measurement • Execution time • Pruning Power • Ratio of Competitive Products out of all combinations • Memory Usage

  24. Experiments

  25. Experiments TQ, TQ’, and TR From 100k to 500k Full pruning & partial pruning Pruning Power slightly increases

  26. Outline • Background • Skyline • Motivation • Examples &Problem Definition • Algorithm • Framework, Partition, Pruning • Experiments • On both synthetic and real data • Over 6 factors • Conclusions

  27. Conclusions • Creating Competitive Products • Example • Problem Definition • Algorithms • Framework • Intra-dominance checking • Inter-dominance checking • Post-processing • Experiments • Synthetic anti-correlated datasets • Real datasets

  28. Q&A Thank You !

  29. Appendix

  30. Partial Pruning Full Pruning Existing Vacation Packages C1={A1, B1} A1 B1 Newly Created Vacation Packages Skyline Tuples of Source Tables Competitive Products

  31. Meta Transformation A1 B1 Meta-Flight Meta-Hotel • No inter-dominance checking for {F2} X{H2}

  32. Experiments From 2.5M to 10M More competitive Slightly decreases

  33. Experiments DOM SKY • A, 296 packages, 1014 hotels and 4394 flights . B, 149 packages, 995 hotels and 866 flights • Source tables from B, and Package from A • Vary discount from 0 to 0.50 • Efficiency • ACCP(44.74s) and Baseline (84.47s) • |SKY|/|TQ| • |DOM|/|TE| Travel Agency A Package Generation Set

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