Unveiling movement uncertainty for robust trajectory similarity analysis
| Autor: | Vania Bogorny, Nikos Pelekis, Luis Otavio Alvares, Andre Salvaro Furtado, Yannis Theodoridis |
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| Rok vydání: | 2017 |
| Předmět: |
Movement (music)
media_common.quotation_subject Geography Planning and Development 0211 other engineering and technologies macromolecular substances 02 engineering and technology Library and Information Sciences computer.software_genre Movement Similarity Raw Trajectory Similarity Elliptical Trajectory Representation Dynamic Threshold Similarity Parameter free Similarity Measure Similarity (network science) Similarity analysis 020204 information systems 0202 electrical engineering electronic engineering information engineering Trajectory Quality (business) Data mining computer 021101 geological & geomatics engineering Information Systems Mathematics media_common |
| Zdroj: | International Journal of Geographical Information Science |
| ISSN: | 1362-3087 1365-8816 |
| DOI: | 10.1080/13658816.2017.1372763 |
| Popis: | Trajectory data analysis and mining require distance and similarity measures, and the quality of their results is directly related to those measures. Several similarity measures originally proposed for time-series were adapted to work with trajectory data, but these approaches were developed for well-behaved data, that usually do not have the uncertainty and heterogeneity introduced by the sampling process to obtain trajectories. More recently, similarity measures were proposed specifically for trajectory data, but they rely on simplistic movement uncertainty representations, such as linear interpolation. In this article we propose a new distance function, and a new similarity measure that uses an elliptical representation of trajectories, being more robust to the movement uncertainty caused by the sampling rate and the heterogeneity of this kind of data. Experiments using real data show that our proposal is more accurate and robust than related work. |
| Databáze: | OpenAIRE |
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