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Data Mining and Knowledge Discovery

, Volume 33, Issue 5, pp 1393–1416 | Cite as

Multi-location visibility query processing using portion-based transactional modeling and pattern mining

  • Lakshmi GangumallaEmail author
  • P. Krishna Reddy
  • Anirban Mondal
Article
  • 247 Downloads
Part of the following topical collections:
  1. Journal Track of ECML PKDD 2019

Abstract

Visibility computation is critical in spatial databases for realizing various interesting and diverse applications such as defence-related surveillance, identifying interesting spots in tourist places and online warcraft games. Existing works address the problem of identifying individual locations for maximizing the visibility of a given target object. However, in case of many applications, a set of locations may be more effective than just individual locations towards maximizing the visibility of the given target object. In this paper, we introduce the Multi-Location Visibility (MLV) query. An MLV query determines the top-k query locations from which the visibility of a given target object can be maximized. We propose a portion-based transactional framework and coverage pattern mining based algorithm to process MLV queries. Our performance evaluation with real datasets demonstrates the effectiveness of the proposed scheme in terms of query processing time, pruning efficiency and target object visibility w.r.t. a recent existing scheme.

Keywords

Visibility query Transaction-modeling Pattern-mining Portion based transactional framework 

Notes

References

  1. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of VLDB, pp 487–499Google Scholar
  2. Ali D, Eunus M et al (2017) Efficient processing of maximum visibility facility selection query in spatial databases. Masters Thesis, BUETGoogle Scholar
  3. Asano T, Asano T, Guibas L, Hershberger J, Imai H (1986) Visibility of disjoint polygons. Algorithmica 1(1):49–63MathSciNetCrossRefzbMATHGoogle Scholar
  4. Beckmann N, Kriegel HP, Schneider R, Seeger B (1990) The R*-tree: an efficient and robust access method for points and rectangles. In: Proceedings of ACM SIGMODGoogle Scholar
  5. Bentley JL, Ottmann TA (1979) Algorithms for reporting and counting geometric intersections. IEEE Trans Comput 28(9):643–647CrossRefzbMATHGoogle Scholar
  6. Bentley JL, Wood D (1980) An optimal worst case algorithm for reporting intersections of rectangles. IEEE Trans Comput 29(7):571–577MathSciNetCrossRefGoogle Scholar
  7. Chvatal V (1979) A greedy heuristic for the set-covering problem. Math Oper Res 4(3):233–235MathSciNetCrossRefzbMATHGoogle Scholar
  8. Floriani L, Magillo P (2003) Algorithms for visibility computation on terrains: a survey. Environ Plan B Plan Des 30(5):709–728CrossRefGoogle Scholar
  9. Gangumalla L (2019) Datasets used in the paper. https://github.com/lakshmi9414/Datasets.git. Accessed 20 June 2019
  10. Gao Y, Liu Q, Miao X, Yang J (2015) Reverse k-nearest neighbor search in the presence of obstacles. Inf Sci 330:274–292CrossRefGoogle Scholar
  11. Gao Y, Zheng B (2009) Continuous obstructed nearest neighbor queries in spatial databases. In: Proceedings of ACM SIGMOD, pp 577–590Google Scholar
  12. Gao Y, Zheng B, Chen G, Lee WC, Lee KCK, Li Q (2009a) Visible reverse k-nearest neighbor queries. In: Proceedings ICDE, pp 1203–1206Google Scholar
  13. Gao Y, Zheng B, Lee WC, Chen G (2009b) Continuous visible nearest neighbor queries. In: Proceedings of EDBT, pp 144–155Google Scholar
  14. Garey MR, Johnson DS, Stockmeyer L (1974) Some simplified NP-complete problems. In: Proceedings of ACM symposium on theory of computing, pp 47–63Google Scholar
  15. Goodchild MF, Lee J (1990) Coverage problems and visibility regions on topographic surfaces. Ann Oper Res 18(1–4):175–186MathSciNetGoogle Scholar
  16. Gowtham Srinivas P et al (2015) Mining coverage patterns from transactional databases. J Intell Inf Syst 45(3):423–439CrossRefGoogle Scholar
  17. Haider CMR, Arman A, Ali ME, Choudhury FM (2016) Continuous maximum visibility query for a moving target. In: Proceedings of ADC, pp 82–94Google Scholar
  18. Han J, Cheng H, Xin D, Yan X (2007) Frequent pattern mining: current status and future directions. Data Min Knowl Discov 15(1):55–86MathSciNetCrossRefGoogle Scholar
  19. Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. SIGMOD Rec. 29(2):1–12CrossRefGoogle Scholar
  20. Irtiza Tripto N, Nahar M, Ali ME, Choudhury F, Culpepper J, Sellis T (2019) Top-k trajectories with the best view. GeoInformatica 141:1–41.  https://doi.org/10.1007/s10707-019-00343-4 Google Scholar
  21. Liu B, Hsu W, Ma Y (1999) Mining association rules with multiple minimum supports. In: Proceedings of ACM SIGKDD, pp 337–341Google Scholar
  22. Masud S, Choudhury FM, Ali ME, Nutanong S (2013) Maximum visibility queries in spatial databases. In: Proc. ICDE, pp. 637–648Google Scholar
  23. Morling K (2010) Geometric and engineering drawing 3E. Routledge, LondonCrossRefGoogle Scholar
  24. Mount DM (2004) Geometric intersection. CiteseerGoogle Scholar
  25. Nutanong S, Tanin E, Zhang R (2010) Incremental evaluation of visible nearest neighbor queries. IEEE TKDE 22(5):665–681Google Scholar
  26. Seeds MA, Backman D (2015) Stars and galaxies. Cengage Learning, BostonGoogle Scholar
  27. Shou L, Huang Z, Tan KL (2003) HDoV-tree: the structure, the storage, the speed. In: Proceedings of ICDE, pp 557–568Google Scholar
  28. Srinivas PG et al (2012) Discovering coverage patterns for banner advertisement placement. In: Proceedings of PAKDD, pp 133–144Google Scholar
  29. Tanton J (2005) Encyclopedia of mathematics. In: Tanton 2005 encyclopedia. Facts On File, incGoogle Scholar
  30. Tao Y, Papadias D, Shen Q (2002) Continuous nearest neighbor search. In: Proceedings of VLDB, pp 287–298Google Scholar
  31. Xu H, Li Z, Lu Y, Deng K, Zhou X (2010) Group visible nearest neighbor queries in spatial databases. In: Proceedings of WAIM, pp 333–344Google Scholar
  32. Zhang J, Papadias D, Mouratidis K, Zhu M (2004) Spatial queries in the presence of obstacles. In: Proceedings of EDBT, pp 366–384Google Scholar

Copyright information

© The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019

Authors and Affiliations

  • Lakshmi Gangumalla
    • 1
    Email author
  • P. Krishna Reddy
    • 1
  • Anirban Mondal
    • 2
  1. 1.IIITHyderabadIndia
  2. 2.Ashoka UniversityDelhiIndia

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