Definition
The trajectory of a moving object is typically modeled as a sequence of consecutive locations in a multi-dimensional (generally two or three dimensional) Euclidean space. Such data types arise in many applications where the location of a given object is measured repeatedly over time. Typical trajectory data are obtained during a tracking procedure with the aid of various sensors. Here also lies the main obstacle of such data; they may contain a significant amount of outliers or in other words incorrect data measurements (unlike for example, stock data which contain no errors whatsoever). An example of two trajectories is shown in Fig. 1.
Examples of 2D trajectories
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References
Agrawal R, Faloutsos C, Swami A (1993) Efficient similarity search in sequence databases. In: Proceedings of FODO, pp 69–84
Berndt D, Clifford J (1994) Using dynamic time warping to find patterns in time series. In: AAAI workshop on knowledge discovery in databases, pp 229–248
Bollobás B, Das G, Gunopulos D, Mannila H (1997) Time-series similarity problems and wellseparated geometric sets. In: Proceedings of SCG
Chen L, Ng RT (2004) On the marriage of lp-norms and edit distance. In: Proceedings of VLDB, pp 792–803
Das G, Gunopulos D, Mannila H (1997) Finding similar time series. In: Proceedings of PKDD, pp 88–100
Faloutsos C, Jagadish HV, Mendelzon A, Milo T (1997) Signature technique for similarity-based queries. In: Proceedings of SEQUENCES
Faloutsos C, Ranganathan M, Manolopoulos I (1994) Fast subsequence matching in time series databases. In: Proceedings of ACM SIGMOD, May 1994
Goldin D, Kanellakis P (1995) On similarity queries for time-series data. In: Proceedings of CP ’95, Sept 1995
Keogh E (2002) Exact indexing of dynamic time warping. In: Proceedings of VLDB
Keogh E, Chakrabarti K, Mehrotra S, Pazzani M (2001) Locally adaptive dimensionality reduction for indexing large time series databases. In: Proceedings of ACM SIGMOD, pp 151–162
Levenshtein V (1966) Binary codes capable of correcting deletions, insertions, and reversals. Sov Phys Dokl 10 10:707–710
Perng S, Wang H, Zhang S, Parker DS (2000) Landmarks: a new model for similarity-based pattern querying in time series databases. In: Proceedings of IEEE ICDE, pp 33–42
Qu Y, Wang C, Wang XS (1998) Supporting fast search in time series for movement patterns in multiple scales. In: Proceedings of ACM CIKM, pp 251–258
Rafiei D, Mendelzon A (2000) Querying time series data based on similarity. IEEE Trans Knowl Data Eng 12(5):675–693
Vlachos M, Kollios G, Gunopulos D (2002) Discovering similar multidimensional trajectories. In: Proceedings of IEEE ICDE, pp 673–684
Zhu Y, Shasha D (2003) Query by humming: a time series database approach. In: Proceedings of ACM SIGMOD
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Kollios, G., Vlachos, M., Gunopulos, D. (2016). Trajectories, Discovering Similar. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-23519-6_1401-2
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DOI: https://doi.org/10.1007/978-3-319-23519-6_1401-2
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