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Knowledge and Information Systems

, Volume 42, Issue 2, pp 409–440 | Cite as

Dominance relationship analysis with budget constraints

  • Shen GeEmail author
  • Leong Hou U
  • Nikos Mamoulis
  • David W. L. Cheung
Regular Paper

Abstract

Creating a new product that dominates all its competitors is one of the main objectives in marketing. Nevertheless, this might not be feasible since in practice the development process is confined by some constraints, e.g., limited funding or low target selling price. We model these constraints by a constraint function, which determines the feasible characteristics of a new product. Given such a budget, our task is to decide the best possible features of the new product that maximize its profitability. In general, a product is marketable if it dominates a large set of existing products, while it is not dominated by many. Based on this, we define dominance relationship analysis and use it to measure the profitability of the new product. The decision problem is then modeled as a budget constrained optimization query (BOQ). Computing BOQ is challenging due to the exponential increase in the search space with dimensionality. We propose a divide-and-conquer based framework, which outperforms a baseline approach in terms of not only execution time but also space complexity. Based on the proposed framework, we further study an approximation solution, which provides a good trade-off between computation cost and quality of result.

Keywords

Dominance relationship analysis Budget constrained optimization query 

Notes

Acknowledgments

This work was supported by grant HKU 714212E from Hong Kong RGC and grant MYRG109(Y1-L3)-FST12-ULH from University of Macau Research Committee.

References

  1. 1.
    Beckmann N, Kriegel H-P, Schneider R, Seeger B (1990) The R*-tree: an efficient and robust access method for points and rectangles. In: SIGMOD conference, pp 322–331Google Scholar
  2. 2.
    Börzsönyi S, Kossmann D, Stocker K (2001) The skyline operator. In: ICDE, pp 421–430Google Scholar
  3. 3.
    Chomicki J, Godfrey P, Gryz J, Liang D (2003) Skyline with presorting. In: ICDE, pp 717–816Google Scholar
  4. 4.
    Cormen TH, Leiserson CE, Rivest RL, Stein C (2009) Introduction to algorithms, 3rd edn. The MIT Press, CambridgeGoogle Scholar
  5. 5.
    Feng H, Song G, Zheng Y, Xia J (2003) A deadline and budget constrained cost-time optimization algorithm for scheduling dependent tasks in grid computing. In: GCC (2), pp 113–120Google Scholar
  6. 6.
    Household dataset (2008). http://www.ipums.org/
  7. 7.
    Kossmann D, Ramsak F, Rost S (2002) Shooting stars in the sky: an online algorithm for skyline queries. In: VLDB, pp 275–286Google Scholar
  8. 8.
    Kung HT, Luccio F, Preparata FP (1975) On finding the maxima of a set of vectors. J ACM 22(4):469–476CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Li C, Ooi BC, Tung AKH, Wang S (2006) DADA: a data cube for dominant relationship analysis. In: SIGMOD conference, pp 659–670Google Scholar
  10. 10.
    Li C, Tung AKH, Jin W, Ester M (2007) On dominating your neighborhood profitably. In: VLDB, pp 818–829Google Scholar
  11. 11.
    Lin C-Y, Koh J-L, Chen AL (2012) Determining k-most demanding products with maximum expected number of total customers. IEEE Trans Knowl Data Eng (99), 1 (preprints)Google Scholar
  12. 12.
    Lu H, Jensen CS (2012) Upgrading uncompetitive products economically. In: ICDE, pp 977–988Google Scholar
  13. 13.
    Miah M, Das G, Hristidis V, Mannila H (2008) Standing out in a crowd: selecting attributes for maximum visibility. In: ICDE, pp 356–365Google Scholar
  14. 14.
    NBA Basketball Statistics (2009) NBA basketball statistics http://www.databasebasketball.com/
  15. 15.
    Oliver D (2010) JoBS methods descriptions for the scientific study of basketball. Website. http://www.powerbasketball.com/theywin2.html
  16. 16.
    Papadias D, Tao Y, Fu G, Seeger B (2005) Progressive skyline computation in database systems. ACM Trans Database Syst 30(1):41–82CrossRefGoogle Scholar
  17. 17.
    Papadopoulos AN, Lyritsis A, Nanopoulos A, Manolopoulos Y (2007) Domination mining and querying. In: DaWaK, pp 145–156Google Scholar
  18. 18.
    Peng Y, Wong RC-W, Wan Q (2012) Finding top-k preferable products. IEEE Trans Knowl Data Eng 24(10):1774–1788CrossRefGoogle Scholar
  19. 19.
  20. 20.
    Pujowidianto NA, Lee LH, Chen C-H, Yap CM (2009) Optimal computing budget allocation for constrained optimization. In: Winter simulation conference, pp 584–589Google Scholar
  21. 21.
    Tan K-L, Eng P-K, Ooi BC (2001) Efficient progressive skyline computation. In: VLDB, pp 301–310Google Scholar
  22. 22.
    Wan Q, Wong RC-W, Ilyas IF, Özsu MT, Peng Y (2009) Creating competitive products. PVLDB 2(1):898–909Google Scholar
  23. 23.
    Wan Q, Wong RC-W, Peng Y (2011) Finding top-k profitable products. In: ICDE, pp 1055–1066Google Scholar
  24. 24.
    Wu T, Sun Y, Li C, Han J (2010) Region-based online promotion analysis. In: EDBT, pp 63–74Google Scholar
  25. 25.
    Wu T, Xin D, Mei Q, Han J (2009) Promotion analysis in multi-dimensional space. PVLDB 2(1):109–120Google Scholar
  26. 26.
    Yang Z, Li L, Kitsuregawa M (2008) Efficient querying relaxed dominant relationship between product Items based on rank aggregation. In: AAAI, pp 1261–1266Google Scholar
  27. 27.
    Yang Z, Wang B, Kitsuregawa M (2007) General dominant relationship analysis based on partial order models. In: SAC, pp 470–474Google Scholar
  28. 28.
    Yiu ML, Mamoulis N (2009) Multi-dimensional top-k dominating queries. VLDB J 18(3):695–718CrossRefGoogle Scholar
  29. 29.
    Zhang S, Mamoulis N, Cheung DW (2009) Scalable skyline computation using object-based space partitioning. In: SIGMOD conference, pp 483–494Google Scholar
  30. 30.
    Zhang Y, Jia Y, Jin W (2011) Promotional subspace mining with EProbe framework. In: CIKM, pp 2185–2188Google Scholar
  31. 31.
    Zhang Z, Lakshmanan LVS, Tung AKH (2009) On domination game analysis for microeconomic data mining. ACM Trans Knowl Discov Data 2(4):18:1–18:27Google Scholar
  32. 32.
    Zhou Y, Chakrabarty D, Lukose RM (2008) Budget constrained bidding in keyword auctions and online knapsack problems. In: WWW, pp 1243–1244Google Scholar
  33. 33.
    Zhu L, Li C, Tung AKH, Wang S (2012) Microeconomic analysis using dominant relationship analysis. Knowl Inf Syst 30(1):179–211CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Shen Ge
    • 1
    Email author
  • Leong Hou U
    • 2
  • Nikos Mamoulis
    • 1
  • David W. L. Cheung
    • 1
  1. 1.Department of Computer ScienceUniversity of Hong KongPokfulamHong Kong
  2. 2.Department of Computer and Information ScienceUniversity of MacauMacauPeople’s Republic of China

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