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Top-k trajectories with the best view

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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.

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Notes

  1. http://www.bikely.com

  2. http://gpswaypoints.net

  3. https://research.microsoft.com/en-us/projects/geolife/

References

  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 155–164

  2. Asano T, Asano T, Guibas L, Hershberger J, Imai H (1986) Visibility of disjoint polygons. Algorithmica 1(1):49–63

    Article  Google Scholar 

  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 27–35

  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 167–175

  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 491–502

  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 900–911

  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 255–266

  8. Choudhury FM, Ali ME, Masud S, Nath S, Rabban IE (2014) Scalable visibility color map construction in spatial databases. Inf Syst 42:89–106

    Article  Google Scholar 

  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):787–799

    Article  Google Scholar 

  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 111–120

  11. Gao Y, Zheng B (2009) Continuous obstructed nearest neighbor queries in spatial databases. In: SIGMOD Conference. ACM, pp 577–590

  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):2176–2195

    Article  Google Scholar 

  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):371–396

    Article  Google Scholar 

  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 144–155

  15. Gu Y, Yu X, Yu G (2014) . Method for continuous reverse k-nearest neighbor queries in obstructed spatial databases 25:1806–1816

    Google Scholar 

  16. Guttman A (1984) R-trees: a dynamic index structure for spatial searching, vol 14. ACM

  17. Haider CMR, Arman A, Ali ME, Choudhury FM (2016) Continuous maximum visibility query for a moving target. In: Australasian database conference. Springer, pp 82–94

  18. Heffernan PJ, Mitchell JSB (1995) An optimal algorithm for computing visibility in the plane. SIAM J Comput 24(1):184–201

    Article  Google Scholar 

  19. Kalashnikov DV, Prabhakar S, Hambrusch SE (2004) Main memory evaluation of monitoring queries over moving objects. Distrib Parallel Datab 15(2):117–135

    Article  Google Scholar 

  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):201–225

    Article  Google Scholar 

  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 1018–1029

  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, VLDB

  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 828–839

  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 637–648

  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 533–544

  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 634–645

  27. Nutanong S, Tanin E, Zhang R (2007) Visible nearest neighbor queries. Springer, Berlin, pp 876–883

    Google Scholar 

  28. Nutanong S, Tanin E, Zhang R (2010) Incremental evaluation of visible nearest neighbor queries. IEEE Trans Knowl Data Eng 22(5):665–681

    Article  Google Scholar 

  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 802–813

    Chapter  Google Scholar 

  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 197–215

  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 168–179

  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 2–11

  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 156–167

  34. Shang S, Ding R, Zheng K, Jensen CS, Kalnis P, Zhou X (2014) Personalized trajectory matching in spatial networks. VLDB J 23(3):449–468

    Article  Google Scholar 

  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 557–568

  36. Song Z, Roussopoulos N (2001) K-nearest neighbor search for moving query point. In: International symposium on spatial and temporal databases. Springer, pp 79–96

  37. Stewart AJ, Karkanis T (1998) Computing the approximate visibility map, with applications to form factors and discontinuity meshing. Springer, Vienna, pp 57–68

    Google Scholar 

  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 14–23

  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 334–345

  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 287–298

  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 287–298

    Chapter  Google Scholar 

  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):260–290

    Article  Google Scholar 

  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 Engineering

  44. Xia C, Hsu D, Tung AKH (2004) A fast filter for obstructed nearest neighbor queries. Springer, Berlin, pp 203–215

    Google Scholar 

  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 631–642

  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 314–320

  47. Zhang C, Shou L, Chen K, Chen G (2012) See-to-retrieve: efficient processing of spatio-visual keyword queries. In: SIGIR, pp 681–690

  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):934–946

    Article  Google Scholar 

  49. Zhang J, Papadias D, Mouratidis K, Zhu M (2004) Spatial queries in the presence of obstacles. Springer, Berlin, pp 366–384

    Google Scholar 

  50. Zheng K, Shang S, Yuan NJ, Yang Y (2013) Towards efficient search for activity trajectories. In: ICDE. IEEE Computer Society, pp 230–241

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Authors and Affiliations

  1. Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

    Nafis Irtiza Tripto, Mahjabin Nahar & Mohammed Eunus Ali

  2. RMIT University, Melbourne, Australia

    Farhana Murtaza Choudhury & J. Shane Culpepper

  3. Swinburne University of Technology, Melbourne, Australia

    Timos Sellis

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  1. Nafis Irtiza Tripto
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  2. Mahjabin Nahar
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  3. Mohammed Eunus Ali
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  6. Timos Sellis
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Correspondence to Mohammed Eunus Ali.

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Tripto, N.I., Nahar, M., Ali, M.E. et al. Top-k trajectories with the best view. Geoinformatica 23, 621–661 (2019). https://doi.org/10.1007/s10707-019-00343-4

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