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Top-k trajectories with the best view

  • Nafis Irtiza Tripto
  • Mahjabin Nahar
  • Mohammed Eunus AliEmail author
  • Farhana Murtaza Choudhury
  • J. Shane Culpepper
  • Timos Sellis
Article
  • 142 Downloads

Abstract

The widespread availability of GPS and the growing popularity of location based social networking applications such as Flickr, Yelp, etc., enable more and more users to share their route activities or trajectories. At the same time, the recent advancement in large-scale 3D modeling has inspired applications that combine visibility and spatial queries, which in turn can be integrated with user trajectories to provide answers for many real-life user queries, such as “How can I choose the route which provides the best view of a historic site?”. In this work, we propose and investigate the k Aggregate Maximum Visibility Trajectory (k AMVT) query and its variants. Given sets of targets, obstacles, and trajectories, the k AMVT query finds top-k trajectories that provide the best view of the targets. We extend the k AMVT query to incorporate different weights (or preferences) with trajectories and targets. To provide an efficient solution to our problem, we employ obstacle and trajectory pruning mechanisms. We also employ an effective target ordering technique, which can further improve query efficiency. Furthermore, we extend the proposed queries to introduce preferences on trajectories in situations where smaller trajectories are preferred due to time constraints, or trajectories closer to the query user are preferred. To verify the efficiency and effectiveness of our solutions, we conduct an extensive experimental study using large synthetic and real datasets.

Keywords

Spatial databases Query processing Obstacles Trajectories Visibility 

Notes

References

  1. 1.
    Asano T, Asano T, Guibas L, Hershberger J, Imai H (1985) Visibility-polygon search and euclidean shortest paths. In: Proceedings of the 26th annual symposium on foundations of computer science, SFCS. IEEE Computer Society, Washington, pp 155–164Google Scholar
  2. 2.
    Asano T, Asano T, Guibas L, Hershberger J, Imai H (1986) Visibility of disjoint polygons. Algorithmica 1(1):49–63CrossRefGoogle Scholar
  3. 3.
    Ben-Moshe B, Hall-Holt O, Katz MJ, Mitchell JSB (2004) Computing the visibility graph of points within a polygon. In: Proceedings of the twentieth annual symposium on computational geometry, SCG ’04. ACM, New York, pp 27–35Google Scholar
  4. 4.
    Bittner J (2002) Efficient construction of visibility maps using approximate occlusion sweep. In: Proceedings of the 18th spring conference on computer graphics, SCCG ’02. ACM, New York, pp 167–175Google Scholar
  5. 5.
    Chen L, Ȯzsu MT, Oria V (2005) Robust and fast similarity search for moving object trajectories. In: Proceedings of the ACM SIGMOD international conference on management of data. Baltimore, Maryland, USA, June 14-16, pp 491–502Google Scholar
  6. 6.
    Chen Z, Shen HT, Zhou X (2011) Discovering popular routes from trajectories. In: Proceedings of the 27th international conference on data engineering, ICDE 2011, April 11-16. Hannover, Germany, pp 900–911Google Scholar
  7. 7.
    Chen Z, Shen HT, Zhou X, Zheng Y, Xie X (2010) Searching trajectories by locations: an efficiency study. In: Proceedings of ACM SIGMOD international conference on management of data, SIGMOD ’10. ACM, New York, pp 255–266Google Scholar
  8. 8.
    Choudhury FM, Ali ME, Masud S, Nath S, Rabban IE (2014) Scalable visibility color map construction in spatial databases. Inf Syst 42:89–106CrossRefGoogle Scholar
  9. 9.
    Ding X, Chen L, Gao Y, Jensen CS, Bao H (2018) Ultraman: a unified platform for big trajectory data management and analytics. Proceedings of the VLDB Endowment 11(7):787–799CrossRefGoogle Scholar
  10. 10.
    Erikson C, Manocha D, Baxter WV III (2001) Hlods for faster display of large static and dynamic environments. In: Proceedings of symposium on interactive 3D graphics. ACM, pp 111–120Google Scholar
  11. 11.
    Gao Y, Zheng B (2009) Continuous obstructed nearest neighbor queries in spatial databases. In: SIGMOD Conference. ACM, pp 577–590Google Scholar
  12. 12.
    Gao Y, Zheng B, Chen G, Li Q, Chen C, Chen G (2010) Efficient mutual nearest neighbor query processing for moving object trajectories. Inform Sci 180(11):2176–2195CrossRefGoogle Scholar
  13. 13.
    Gao Y, Zheng B, Chen G, Li Q, Guo X (2011) Continuous visible nearest neighbor query processing in spatial databases. VLDB J 20(3):371–396CrossRefGoogle Scholar
  14. 14.
    Gao Y, Zheng B, Lee WC, Chen G (2009) Continuous visible nearest neighbor queries. In: Proceedings of the 12th international conference on extending database technology: advances in database technology, EDBT ’09. ACM, New York, pp 144–155Google Scholar
  15. 15.
    Gu Y, Yu X, Yu G (2014) . Method for continuous reverse k-nearest neighbor queries in obstructed spatial databases 25:1806–1816Google Scholar
  16. 16.
    Guttman A (1984) R-trees: a dynamic index structure for spatial searching, vol 14. ACMGoogle Scholar
  17. 17.
    Haider CMR, Arman A, Ali ME, Choudhury FM (2016) Continuous maximum visibility query for a moving target. In: Australasian database conference. Springer, pp 82–94Google Scholar
  18. 18.
    Heffernan PJ, Mitchell JSB (1995) An optimal algorithm for computing visibility in the plane. SIAM J Comput 24(1):184–201CrossRefGoogle Scholar
  19. 19.
    Kalashnikov DV, Prabhakar S, Hambrusch SE (2004) Main memory evaluation of monitoring queries over moving objects. Distrib Parallel Datab 15(2):117–135CrossRefGoogle Scholar
  20. 20.
    Kim DS, Yoo KH, Chwa KY, Shin SY (1998) Efficient algorithms for computing a complete visibility region in three-dimensional space. Algorithmica 20 (2):201–225CrossRefGoogle Scholar
  21. 21.
    Lee KC, Lee WC, Zheng B (2009) Fast object search on road networks. In: Proceedings of the 12th international conference on extending database technology: advances in database technology. ACM, pp 1018–1029Google Scholar
  22. 22.
    Levandoski JJ, Khalefa ME, Mokbel MF (2011) The caredb context and preference-aware database system. In: 5th International workshop on personalized access, profile management, and context awareness in databases, PersDB-in conjunction with very large data bases, VLDBGoogle Scholar
  23. 23.
    Levandoski JJ, Mokbel MF, Khalefa ME (2010) Flexpref: a framework for extensible preference evaluation in database systems. In: IEEE 26th International conference on data engineering (ICDE). IEEE, pp 828–839Google Scholar
  24. 24.
    Masud S, Choudhury FM, Ali ME, Nutanong S (2013) Maximum visibility queries in spatial databases. In: 29th International conference on data engineering (ICDE). IEEE, pp 637–648Google Scholar
  25. 25.
    Mouratidis K, Lin Y, Yiu ML (2010) Preference queries in large multi-cost transportation networks. In: IEEE 26th International conference on data engineering (ICDE). IEEE, pp 533–544Google Scholar
  26. 26.
    Mouratidis K, Papadias D, Hadjieleftheriou M (2005) Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring. In: Proceedings of ACM SIGMOD international conference on management of data. ACM, pp 634–645Google Scholar
  27. 27.
    Nutanong S, Tanin E, Zhang R (2007) Visible nearest neighbor queries. Springer, Berlin, pp 876–883Google Scholar
  28. 28.
    Nutanong S, Tanin E, Zhang R (2010) Incremental evaluation of visible nearest neighbor queries. IEEE Trans Knowl Data Eng 22(5):665–681CrossRefGoogle Scholar
  29. 29.
    Papadias D, Zhang J, Mamoulis N, Tao Y (2003) Query processing in spatial network databases. In: Proceedings of the 29th international conference on very large data bases, vol 29. VLDB Endowment, pp 802–813Google Scholar
  30. 30.
    Rabban IE, Abdullah K, Ali ME, Cheema MA (2015) Visibility color map for a fixed or moving target in spatial databases. In: International symposium on spatial and temporal databases. Springer, pp 197–215Google Scholar
  31. 31.
    Rocha-Junior JB, Nørvåg K (2012) Top-k spatial keyword queries on road networks. In: Proceedings of the 15th international conference on extending database technology. ACM, pp 168–179Google Scholar
  32. 32.
    Shafique S, Ali ME (2016) Recommending most popular travel path within a region of interest from historical trajectory data. In: Proceedings of the 5th ACM SIGSPATIAL international workshop on mobile geographic information systems. ACM, pp 2–11Google Scholar
  33. 33.
    Shang S, Ding R, Yuan B, Xie K, Zheng K, Kalnis P (2012) User oriented trajectory search for trip recommendation. In: Proceedings of the 15th international conference on extending database technology, EDBT. ACM, New York, pp 156–167Google Scholar
  34. 34.
    Shang S, Ding R, Zheng K, Jensen CS, Kalnis P, Zhou X (2014) Personalized trajectory matching in spatial networks. VLDB J 23(3):449–468CrossRefGoogle Scholar
  35. 35.
    Shou L, Huang Z, Tan KL (2003) Hdov-tree: the structure, the storage, the speed. In: Proceedings on 19th international conference on data engineering. IEEE, pp 557–568Google Scholar
  36. 36.
    Song Z, Roussopoulos N (2001) K-nearest neighbor search for moving query point. In: International symposium on spatial and temporal databases. Springer, pp 79–96Google Scholar
  37. 37.
    Stewart AJ, Karkanis T (1998) Computing the approximate visibility map, with applications to form factors and discontinuity meshing. Springer, Vienna, pp 57–68Google Scholar
  38. 38.
    Suri S, O’Rourke J (1986) Worst-case optimal algorithms for constructing visibility polygons with holes. In: Proceedings of the second annual symposium on computational geometry, SCG ’86. ACM, New York, pp 14–23Google Scholar
  39. 39.
    Tao Y, Papadias D (2002) Time-parameterized queries in spatio-temporal databases. In: Proceedings of the 2002 ACM SIGMOD international conference on management of data. ACM, pp 334–345Google Scholar
  40. 40.
    Tao Y, Papadias D, Shen Q (2002) Continuous nearest neighbor search. In: VLDB’02: Proceedings of the 28th international conference on very large databases. Elsevier, pp 287–298Google Scholar
  41. 41.
    Tao Y, Papadias D, Shen Q (2002) Continuous nearest neighbor search. In: Proceedings of the 28th international conference on very large data bases, VLDB ’02. VLDB Endowment, pp 287–298Google Scholar
  42. 42.
    Tsai YHR, Cheng LT, Osher S, Burchard P, Sapiro G (2004) Visibility and its dynamics in a pde based implicit framework. J Comput Phys 199(1):260–290CrossRefGoogle Scholar
  43. 43.
    Wang S, Bao Z, Culpepper JS, Sellis T, Cong G (2017) Reverse k nearest neighbor search over trajectories. IEEE Transactions on Knowledge and Data EngineeringGoogle Scholar
  44. 44.
    Xia C, Hsu D, Tung AKH (2004) A fast filter for obstructed nearest neighbor queries. Springer, Berlin, pp 203–215Google Scholar
  45. 45.
    Yu X, Pu KQ, Koudas N (2005) Monitoring k-nearest neighbor queries over moving objects. In: Proceedings on 21st international conference on data engineering, iCDE. IEEE, pp 631–642Google Scholar
  46. 46.
    Zarei A, Ghodsi M (2005) Efficient computation of query point visibility in polygons with holes. In: Proceedings of the twenty-first annual symposium on computational geometry, SCG ’05. ACM, New York, pp 314–320Google Scholar
  47. 47.
    Zhang C, Shou L, Chen K, Chen G (2012) See-to-retrieve: efficient processing of spatio-visual keyword queries. In: SIGIR, pp 681–690Google Scholar
  48. 48.
    Zhang D, Ding M, Yang D, Liu Y, Fan J, Shen HT (2018) Trajectory simplification: an experimental study and quality analysis. Proc VLDB Endowment 11(9):934–946CrossRefGoogle Scholar
  49. 49.
    Zhang J, Papadias D, Mouratidis K, Zhu M (2004) Spatial queries in the presence of obstacles. Springer, Berlin, pp 366–384Google Scholar
  50. 50.
    Zheng K, Shang S, Yuan NJ, Yang Y (2013) Towards efficient search for activity trajectories. In: ICDE. IEEE Computer Society, pp 230–241Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Nafis Irtiza Tripto
    • 1
  • Mahjabin Nahar
    • 1
  • Mohammed Eunus Ali
    • 1
    Email author
  • Farhana Murtaza Choudhury
    • 2
  • J. Shane Culpepper
    • 2
  • Timos Sellis
    • 3
  1. 1.Bangladesh University of Engineering and TechnologyDhakaBangladesh
  2. 2.RMIT UniversityMelbourneAustralia
  3. 3.Swinburne University of TechnologyMelbourneAustralia

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