Abstract
Over the past five to seven years the analysis of trajectory data has established itself as an independent research discipline within the area of data mining. In this article we provide an overview on data characteristics, state-of-the-art preprocessing and analysis methods of trajectory data. We conclude the article with a collection of challenges that arise due to the increasing variety of spatiotemporal data sources and which have to be solved for the application of spatiotemporal analysis methods in practice.
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Allen JF (1984) Towards a general theory of action and time. Artif Intell 23(2):123–154
Alvares LO, Bogorny V, Kuijpers B, de Macedo JAF, Moelans B, Vaisman A (2007) A model for enriching trajectories with semantic geographical information. In: Proc of the 15th annual ACM international symposium on advances in geographic information systems (GIS’07). ACM, New York, pp 1–8
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 G, Andrienko N, Wrobel S (2007) Visual analytics tools for analysis of movement data. SIGKDD Explor Newsl 9(2):38–46
Andrienko N, Andrienko G, Pelekis N, Spaccapietra S (2008) Basic concepts of movement data. In: Giannotti F, Pedreschi D (eds) Mobility, data mining and privacy. Springer, Berlin, Chap 1
Andrienko G, Andrienko N, Rinzivillo S, Nanni M, Pedreschi D, Giannotti F (2009) Interactive visual clustering of large collections of trajectories. In: Proc of the IEEE symposium on visual analytics science and technology (VAST’09). IEEE, New York, pp 3–10
Benetis R, Jensen CS, Karciauskas G, Saltenis S (2006) Nearest and reverse nearest neighbor queries for moving objects. VLDB J 15(3):229–249
Benkert M, Gudmundsson J, Hübner F, Wolle T (2008) Reporting flock patterns. Comput Geom 41(3):111–125
Bonchi F, Lakshmanan LV, Wang H (2011) Trajectory anonymity in publishing personal mobility data. SIGKDD Explor Newsl 13(1):30–42
Buchin M, Driemel A, van Kreveld M, Sacristán V (2010) An algorithmic framework for segmenting trajectories based on spatio-temporal criteria. In: Proc of the 18th SIGSPATIAL international conference on advances in geographic information systems (ACM GIS’10). ACM, New York, pp 202–211
Chen R, Fung BCM, Desai BC (2011) Differentially private trajectory data publication. CoRR abs/1112.2020
Claramunt C, Jiang B (2000) A representation of relationships in temporal spaces. In: Innovations in GIS VII: geocomputation. Taylor & Francis, London, pp 41–53
Claramunt C, Jiang B (2001) An integrated representation of spatial and temporal relationships between evolving regions. Geogr Syst 3:411–428
Dwork C (2006) Differential privacy. In: Proc of the 33rd international colloquium on automata, languages and programming (ICALP’06). Lecture notes in computer science. Springer, Berlin, pp 1–12
Egenhofer MJ (1991) Reasoning about binary topological relations. In: Günther O, Schek HJ (eds) Proc of the 2nd international symposium on advances in spatial databases (SSD). Springer, Berlin, pp 143–160
Forlizzi L, Güting RH, Nardelli E, Schneider M (2000) A data model and data structures for moving objects databases. In: Proc of the 2000 ACM SIGMOD international conference on management of data (SIGMOD’00). ACM, New York, pp 319–330
Giannotti F, Nanni M, Pedreschi D (2006) Efficient mining of temporally annotated sequences. In: Proc of the 6th SIAM international conference on data mining (SDM’06). SIAM, Philadelphia, pp 346–357
Giannotti F, Nanni M, Pinelli F, Pedreschi D (2007) Trajectory pattern mining. In: Proc of the 13th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’07). ACM, New York, pp 330–339
Güting RH, Schneider M (2005) Moving objects databases. Morgan Kaufmann, San Mateo
Hägerstrand T (1970) What about people in regional science? Pap Reg Sci Assoc 24:7–21
Hecker D, Körner C, Stange H, Schulz D, May M (2011) Modeling micro-movement variability in mobility studies. In: Geertman S, Reinhardt W, Toppen F (eds) Advancing geoinformation science for a changing world. Lecture notes in geoinformation and cartography. Springer, Berlin, pp 121–140
Hwang SY, Liu YH, Chiu JK, Lim EP (2005) Mining mobile group patterns: a trajectory-based approach. In: Proc of the 9th Pacific-Asia conference on advances in knowledge discovery and data mining (PAKDD’05). Lecture notes in computer science, vol 3518. Springer, Berlin, pp 713–718
Kang J, Yong HS (2010) Mining spatio-temporal patterns in trajectory data. J Inf Process Syst 6(4):521–536
Körner C (2012) Modeling visit potential of geographic locations based on mobility data. PhD thesis, University of Bonn
Laasonen K (2005) Clustering and prediction of mobile user routes from cellular data. In: Proc of 9th European conference on principles and practice of knowledge discovery in databases (PKDD’05). Springer, Berlin, pp 569–576
Laube P, Imfeld S (2002) Analyzing relative motion within groups of trackable moving point objects. In: Proc of the 2nd international conference on geographic information science (GIScience’02). Springer, London, pp 132–144
Lei PR, Shen TJ, Peng WC, Su IJ (2011) Exploring spatial-temporal trajectory model for location prediction. In: Proceedings of the 2011 IEEE 12th international conference on mobile data management (MDM’11). IEEE Computer Society, Washington, pp 58–67
Liang B, Haas ZJ (2003) Predictive distance-based mobility management for multidimensional PCS networks. IEEE/ACM Trans Netw 11(5):718–732
Liao L, Fox D, Kautz H (2007) Extracting places and activities from GPS traces using hierarchical conditional random fields. Int J Robot Res 26(1):119–134
Liou SC, Huang YM (2005) Trajectory predictions in mobile networks. Int J Inf Technol 11(11):109–122
Marketos G, Frentzos E, Ntoutsi I, Pelekis N, Raffaetà A, Theodoridis Y (2008) Building real-world trajectory warehouses. In: Proc of the seventh ACM international workshop on data engineering for wireless and mobile access (MobiDE’08). ACM, New York, pp 8–15
Monreale A, Pinelli F, Trasarti R, Giannotti F (2009) WhereNext: a location predictor on trajectory pattern mining. In: Proc of the 15th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’09). ACM, New York, pp 637–646
Monreale A, Andrienko G, Andrienko N, Giannotti F, Pedreschi D, Rinzivillo S, Wrobel S (2010) Movement data anonymity through generalization. Trans Data Priv 3(2):91–121
Muckell J, Hwang JH, Lawson CT, Ravi SS (2010) Algorithms for compressing GPS trajectory data: an empirical evaluation. In: Proc of the 18th SIGSPATIAL international conference on advances in geographic information systems (ACM GIS’10). ACM, New York, pp 402–405
Nanni M, Pedreschi D (2006) Time-focused clustering of trajectories of moving objects. J Intell Inf Syst 27(3):267–289
Nanni M, Kuijpers B, Körner C, May M, Pedreschi D (2008) Spatiotemporal data mining. In: Giannotti F, Pedreschi D (eds) Mobility, data mining and privacy. Springer, Berlin, Chap 10
Panagiotakis C, Pelekis N, Kopanakis I, Ramasso E, Theodoridis Y (2011) Segmentation and sampling of moving object trajectories based on representativeness. IEEE Trans Knowl Data Eng 24:1328–1343
Pelekis N, Andrienko G, Andrienko N, Kopanakis I, Marketos G, Theodoridis Y (2011) Visually exploring movement data via similarity-based analysis. J Intel Inf Syst 1–49
Pelekis N, Frentzos E, Giatrakos N, Theodoridis Y (2011) HERMES: a trajectory db engine for mobility-centric applications. Int J Knowl-Based Organ, in press
Rinzivillo S, Pedreschi D, Nanni M, Giannotti F, Andrienko N, Andrienko G (2008) Visually driven analysis of movement data by progressive clustering. Inf Vis 7(3):225–239
Saltenis S, Jensen CS, Leutenegger ST, Lopez MA (2000) Indexing the positions of continuously moving objects. In: Proc of the 2000 ACM SIGMOD international conference on management of data (SIGMOD’00). ACM, New York, pp 331–342
Samarati P (2001) Protecting respondents’ identities in microdata release. IEEE Trans Knowl Data Eng 13:1010–1027
Schuessler N, Axhausen KW (2009) Processing raw data from global positioning systems without additional information. Transp Res Rec 2105:28–36
Schulz D, Bothe S, Körner C (2012) Human mobility from GSM data—a valid alternative to GPS? In: Proc of the mobile data challenge workshop
Shekhar S, Raju VR, Celik M (2009) Spatial and spatio-temporal data mining: recent advances. In: Kargupta H, Han J, Yu P, Motwani R, Kumar V (eds) Next generation of data mining. Chapman & Hall/CRC, London, Chap 26
Stopher PR (2009) Collecting and processing data from mobile technologies. In: Transport survey methods—keeping up with a changed world. Emerald Group Publishing Limited, Bingley, Chap 21
Tao Y, Papadias D (2002) Time-parameterized queries in spatio-temporal databases. In: Proc of the 2002 ACM SIGMOD international conference on management of data (SIGMOD’02). ACM, New York, pp 334–345
Tao Y, Papadias D, Sun J (2003) The TPR*-tree: an optimized spatio-temporal access method for predictive queries. In: Proc of 29th international conference on very large data bases (VLDB’03). Morgan Kaufmann, San Mateo, pp 790–801
Wachowicz M, Ong R, Renso C, Nanni M (2011) Finding moving flock patterns among pedestrians through collective coherence. Int J Geogr Inf Sci 25(11):1849–1864
Wang Y, Lim EP, Hwang SY (2003) On mining group patterns of mobile users. In: Proc of the 14th international conference on database and expert systems applications (DEXA’03). Springer, Berlin, pp 287–296
Yan Z, Chakraborty D, Parent C, Spaccapietra S, Aberer K (2011) SeMiTri: a framework for semantic annotation of heterogeneous trajectories. In: Proc of the 14th international conference on extending database technology (EDBT’11). ACM, New York, pp 259–270
Yan Z, Giatrakos N, Katsikaros V, Pelekis N, Theodoridis Y (2011) SeTraStream: semantic-aware trajectory construction over streaming movement data. In: Proc of the 12th international symposium on advances in spatial and temporal databases (SSTD’11), pp 367–385
Ying JJC, Lee WC, Weng TC, Tseng VS (2011) Semantic trajectory mining for location prediction. In: Proc of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems (ACM GIS’11). ACM, New York, pp 34–43
Zhou C, Frankowski D, Ludford P, Shekhar S, Terveen L (2007) Discovering personally meaningful places: an interactive clustering approach. ACM Trans Inf Syst 25(3)
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Körner, C., May, M. & Wrobel, S. Spatiotemporal Modeling and Analysis—Introduction and Overview. Künstl Intell 26, 215–221 (2012). https://doi.org/10.1007/s13218-012-0215-2
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DOI: https://doi.org/10.1007/s13218-012-0215-2