Abstract
Data analysis and knowledge discovery in trajectory databases is an emerging field with a growing number of applications such as managing traffic, planning tourism infrastructures, analyzing professional sport matches or better understanding wildlife. A well-known collection of patterns which can occur for a subset of trajectories of moving objects exists. In this paper, we study the popular places pattern, that is, locations that are visited by many moving objects. We consider two criteria, strong and weak, to establish either the exact number of times that an object has visited a place during its complete trajectory or whether it has visited the place, or not. To solve the problem of reporting popular places, we introduce the popularity map. The popularity of a point is a measure of how many times the moving objects of a set have visited that point. The popularity map is the subdivision, into regions, of a plane where all the points have the same popularity. We propose different algorithms to efficiently compute and visualize popular places, the so-called popular regions and their schematization, by taking advantage of the parallel computing capabilities of the graphics processing units. Finally, we provide and discuss the experimental results obtained with the implementation of our algorithms.
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Andersson M, Gudmundsson J, Laube P, Wolle T (2008) Reporting leaders and followers among trajectories of moving point objects. GeoInformatica 12(4):497–528
Andrienko, GL, Andrienko NV (2008) A visual analytics approach to exploration of large amounts of movement data. In: Sebillo M, Vitiello G, Schaefer G (eds) VISUAL, lecture notes in computer science, vol 5188, Springer, pp 1–4
Benkert M, Djordjevic B, Gudmundsson J, Wolle T (2010) Finding popular places. Int J Comput Geom Appl (IJCGA) 20(1):19–42
Benkert M, Gudmundsson J, Hübner F, Wolle T (2008) Reporting flock patterns. Comput Geom 41(3):111–125
Bogorny V, Shekhar S (2010) Spatial and spatio-temporal data mining. In: Proceedings of IEEE international conference on data mining ICDM’2010, p 1217
Coll N, Fort M, Madern N, Sellarès JA (2007) Multi-visibility maps of triangulated terrains. Int J Geogr Inf Sci 21(10):1115–1134
Dartmouth College (2008) CRAWDAD. http://crawdad.cs.dartmouth.edu/. Accessed Apr 2013
Dodge S, Weibel R, Lautenschutz AK (2008) Towards a taxonomy of movement patterns. Inf Vis 7(3–4):240–252
Fang W, Lu M, Xiao X, He B, Luo Q (2009) Frequent itemset mining on graphics processors. In: DaMoN ’09: proceedings of the fifth international workshop on data management on new hardware. ACM, New York, NY, USA, pp 34–42
Fort M, Sellarès JA, Valladares N (2010) Computing popular places using graphics processors. In: Proceedings of SSTDM’10 in cooperation with IEEE ICDM’10, IEEE Computer Society, pp 233–241
Fort M, Sellarès JA, Valladares N (2010) Computing popularity maps with graphics hardware. In: Proceedings of the 27th European workshop on computational geometry, pp 233–240
Gajentaan A, Overmars M (1995) On a class of \(O(n^2)\) problems in computational geometry. Comput Geom Theory Appl 5:165–185
Giannotti F, Nanni M, Pedreschi D, Pinelli F (2007) Trajectory pattern mining. In: Proceedings of 13th ACM SIGKDD, Sant Jose, California, USA, pp 330–339
Gonzalez RC, Woods RE (2008) Digital image processing, 3rd edn. Prentice-Hall, Inc., Englewood Cliffs, NJ
Gudmundsson J, van Kreveld M, Speckmann B (2004) Efficient detection of motion patterns in spatio-temporal data sets. In: Pfoser D, Cruz IF, Ronthaler M (eds) GIS. ACM, New York, pp 250–257
Gudmundsson J, van Kreveld MJ (2006) Computing longest duration flocks in trajectory data. In: de By RA, Nittel S (eds) GIS. ACM, New York, pp 35–42
Gudmundsson J, van Kreveld MJ, Speckmann B (2007) Efficient detection of patterns in 2D trajectories of moving points. GeoInformatica 11(2):195–215
Laube P, Imfeld S, Weibel R (2005) Discovering relative motion patterns in groups of moving point objects. Int J Geogr Inf Sci 19(6):639–668
Laube P, van Kreveld M, Imfield S (2004) Finding REMO—detecting relative motion patterns in geospatial lifelines. Developments in spatial data handling: 11th international symposium on spatial data handling, pp 201–215
Leite PJS, Teixeira JMXN, de Farias TSMC, Teichrieb V, Kelner J (2009) Massively parallel nearest neighbor queries for dynamic point clouds on the GPU. In: SBAC-PAD. IEEE Computer Society, pp 19–25
Li X, Han J, Lee JG, Gonzalez H (2007) Traffic density-based discovery of hot routes in road networks. In: SSTD 2007, LNCS, vol 4605, pp 441–459
Liao Z-H, Peng W-C (2012) Clustering spatial data with a geographic constraint: exploring local search. Knowl Inf Syst 31(1):153–170
Lu E, Lee WC, Tseng V (2010) Mining fastest path from trajectories with multiple destinations in road networks. Knowl Inf Syst 1–29
NVIDIA (2011) CUDA programming guide 4.0. Technical report, NVIDIA Corporation
NVIDIA (2011) NVIDIA CUDA C programming best practices guide 4.0. Technical report, NVIDIA Corporation
Ong R, Wachowicz M, Nanni M, Renso C (2010) From pattern discovery to pattern interpretation in movement data. In: ICDM’10, pp 527–534
Owens JD, Luebke D, Govindaraju N, Harris M, Krger J, Lefohn AE, Purcell TJ (2007) A survey of general-purpose computation on graphics hardware. Comput Graph Forum 26(1):80–113
Pelekis N, Kopanakis I, Kotsifakos EE, Frentzos E, Theodoridis Y (2011) Clustering uncertain trajectories. Knowl Inf Syst 28(1):117–147
Rinzivillo S, Pedreschi D, Nanni M, Giannotti F, Andrienko NV, Andrienko GL (2008) Visually driven analysis of movement data by progressive clustering. Inf Vis 7(3–4):225–239
Sacharidis D, Patroumpas Kl, Terrovitis M, Kantere V, Potamias M, Mouratidis K, Sellis T (2008) On-line discovery of hot motion paths. In: EDBT’08, March 2008, France, pp 392–402
Segal M, Akeley K (1994) The design of the openGL graphics interface. Technical report, Silicon Graphics Computer Systems
Siddiqi K, Pizer S (2008) Medial representations: mathematics. Algorithms and applications. Springer, Berlin
Theodoridis Y (2011) R-Tree portal. http://www.chorochronog.org/. Accessed Apr 2013
Tietbohl A, Bogorny V, Kuijpers B, Alvares LO (2008) A Clustering-based approach for discovering interesting places in trajectories, In: SAC’08. March 2008, Brazil
Trasarti R, Pinelli F, Nanni M, Giannotti F (2011) Mining mobility user profiles for car pooling. In: KDD’011, pp 1190–1198
Wilensky U (1999) NetLogo. http://ccl.northwestern.edu/netlogo/models/BirdBreeder. Accessed Apr 2013
Acknowledgments
We thank the reviewers for their suggestions and comments. Authors are partially supported by the Spanish MCI grant TIN2010-20590-C02-02.
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Fort, M., Sellarès, J.A. & Valladares, N. Computing and visualizing popular places. Knowl Inf Syst 40, 411–437 (2014). https://doi.org/10.1007/s10115-013-0639-5
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DOI: https://doi.org/10.1007/s10115-013-0639-5