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Skyline computation over multiple points and dimensions

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Abstract

Skyline is a technique in database management system for multi-criterion decision making based on dominance analysis. Skyline overcomes the limitation of relational databases by handling the criteria that are inversely proportional to each other. Traditional skyline operation is conceptualized over two dimensions only, and it finds out single interesting point. In this paper we extend the capability of skyline to work with multiple dimensions and to search the multiple interesting points from the given search space. The work furthermore ranks skyline points with respect to the multiple interesting points. However, we restrict the computational complexity within a fixed upper bound. Skyline is commonly applied on tourism industries, and we consider two different case studies from this domain and execute the proposed methodology over the real-life data. Comparative study is given based on different parameters, and statistical analysis is also performed to illustrate the efficacy of the proposed method over the existing methods.

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References

  1. Saliu O, Ruhe G (2005) Software release planning for evolving systems. Innov Syst Softw Eng 1:189–204

    Article  Google Scholar 

  2. Ray M, Mohapatra DP (2014) Multi-objective test prioritization via a genetic algorithm. Innov Syst Softw Eng 10:261–270

    Article  Google Scholar 

  3. Borzsonyi S, Kossmann D, Stocker K (2001) The skyline operator. In: International conference on data engineering (ICDE)

  4. Chomicki J, Godfrey P, Gryz J, Liang D (2005) Skyline with presorting. In: Intelligent information processing and web mining

  5. Ghosh P, Sen S (2015) Ranking skyline points by computing nearest neighbor of best skyline point. In: IEEE India international conference (INDICON)

  6. Tan KL, Eng PK, Ooi BC (2001) Efficient progressive skyline computation. In: Proceedings of the international conference on. Very large databases

  7. Eng PK, Ooi BC, Tan KL (2003) Indexing for progressive skyline computation. Data Knowl Eng

  8. Ghosh P, Sen S (2016) An alternative solution to skyline operation to reduce computational complexity. In: Second IEEE international conference on research in computational intelligence and communication networks

  9. Alami K, Maabout S (2020) A framework for multidimensional skyline queries over streaming data. Data Knowl Eng 127:101792

    Article  Google Scholar 

  10. Tang M, Yu Y, Aref WG, Malluhi QM, Ouzzani M (2019) Efficient parallel skyline query processing for high-dimensional data. In: International conference on data engineering (ICDE)

  11. Amiruzzaman M, Jamonnak S (2020) Multi-dimensional skyline query to find best shopping mall for customers. In: Conference on data science and machine learning applications (CDMA). pp 71–76

  12. Sharifzadeh M, Shahabi C (2008) The spatial skyline queries. In: Proceedings of VLDB

  13. Geng M, Shamshul Aerffn M, Morimoto Y (2012) A spatial skyline for a group of user. In: Third international conference on networking and computing. IEEE conference publication

  14. Huang Z, Lu H, Ooi BC, Tung AK (2006) Tung continuous skyline queries for moving objects. IEEE Trans Knowl Data Eng

  15. Godfrey P, Shipley R, Gryz J (2005) Maximal vector computation in large data sets. In: Proceedings of the 31st VLDB conference

  16. Zhang B, Lee KCK, Lee WC (2008) Location-dependent skyline query. In: Proceedings of MDM

  17. Papadias D, Tao Y, Fu G, Seege B (2003) An optimal and progressive algorithm for skyline queries. In: Proceedings of ACM SIGMOD

  18. Fort M, Sellarès JA, Valladares N (2020) Nearest and farthest spatial skyline queries under multiplicative weighted Euclidean distances. Knowl Based Syst 192:105299

    Article  Google Scholar 

  19. Papadias GD, Tao Y, Fu G, Seege B (2005) Progressive skyline computation in database systems. ACM Trans Database Syst 30(1):41–82

    Article  Google Scholar 

  20. Liu X, Yang D-N, Ye M, Lee W-C (2013) U-skyline: a new skyline query for uncertain databases. IEEE Trans Knowl Data Eng

  21. Papadias D, Tao Y, Mouratidis K, Hui CK (2005) Aggregate nearest neighbor queries in spatial databases. In: Proceedings of ICDT

  22. Rocha-Junior JB, Vlachou A, Doulkeridis C, Nrvg K (2011) Efficient execution plans for distributed skyline query. In: Processing on EDBT

  23. Chen L, Cui B, Lu H (2011) Constrained skyline query processing against distributed data sites. In: IEEE conference publication on TKDE

  24. Ghosh P, Goto T, Sen S (2018) Taxicab geometry based analysis on skyline for business intelligence. Int J Softw Innov (IJSI) 6(4):86–102

    Article  Google Scholar 

  25. Rudenko L, Endres M (2018) Real-time skyline computation on data streams. In: Benczúr A et al (eds) New trends in databases and information systems. Communications in computer and information science, vol 909. Springer, Cham

    Google Scholar 

  26. Sen S, Cortesi A, Chaki N (2016) Hyper-lattice algebraic model for data warehousing. Springer, Berlin, pp 1–63

    Book  Google Scholar 

  27. Sen S, Cortesi A, Chaki N (2016) ROLAP based data warehouse schema to XML schema conversion. In: Proceedings of IEEE international conference on industrial technology, ICIT 2016, pp 1736–174

  28. Hsu WT, Wen YT, Wei LY, Peng WC (2014) Skyline travel routes: exploring skyline for trip planning. In: International conference on mobile data management

  29. Chen Y-C, Lee C (2016) Skyline path queries with aggregate attributes. IEEE Access (Volume 4)

  30. Van Craenendonck T, Blockeel H (2017) Constraint-based clustering selection. Mach Learn 106:1497–1521

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. A.K.Choudhury School of IT, University of Calcutta, Kolkata, India

    Partha Ghosh & Soumya Sen

  2. Università Ca′ Foscari, Venice, Italy

    Agostino Cortesi

Authors
  1. Partha Ghosh
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  2. Soumya Sen
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  3. Agostino Cortesi
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Correspondence to Partha Ghosh.

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Ghosh, P., Sen, S. & Cortesi, A. Skyline computation over multiple points and dimensions. Innovations Syst Softw Eng 17, 141–156 (2021). https://doi.org/10.1007/s11334-020-00376-1

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  • DOI: https://doi.org/10.1007/s11334-020-00376-1

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