Towards semantic‐aware multiple‐aspect trajectory similarity measuring-Reference-Cited by-同舟云学术

Towards semantic‐aware multiple‐aspect trajectory similarity measuring

Published:2019-06-18 Issue:5 Volume:23 Page:960-975
ISSN:1361-1682
Container-title:Transactions in GIS
language:en
Short-container-title:Transactions in GIS

Author:

Petry Lucas May¹^ORCID,Ferrero Carlos Andres¹²,Alvares Luis Otavio¹,Renso Chiara³,Bogorny Vania¹^ORCID

Affiliation:

1. Programa de Pós‐Graduação em Ciência da Computação Universidade Federal de Santa Catarina (UFSC) Florianopolis Brazil

2. Instituto Federal de Santa Catarina (IFSC) Lages Brazil

3. Istituto di scienza e tecnologie dell'informazione (ISTI) Consiglio Nazionale delle Ricerche (CNR) Pisa Italy

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Wiley

Subject

General Earth and Planetary Sciences

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/tgis.12542

Reference22 articles.

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2. Berndt D. J. &Clifford J. (1994).Using dynamic time warping to find patterns in time series. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (pp.359–370).Menlo Park CA:AAAI. Retrieved fromhttps://dl.acm.org/citation.cfm?id=3000887

3. CONSTAnT - A Conceptual Data Model for Semantic Trajectories of Moving Objects

4. Bollobás B. Das G. Gunopulos D. &Mannila H. (1997).Time‐series similarity problems and well‐separated geometric sets. In Proceedings of the 13th Annual Symposium on Computational Geometry (pp.454–456).New York NY:ACM Press.

5. Chen L. Özsu M. T. &Oria V. (2005).Robust and fast similarity search for moving object trajectories. In Proceedings of the 31st ACM SIGMOD International Conference on Management of Data (pp.491–502).New York NY:ACM.

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