Document - Towards in-memory sub-trajectory similarity search

2020

Conference article Open Access

Towards in-memory sub-trajectory similarity search

Alamdari I., Nanni M., Trasarti R., Pedreschi D.

Mobility Data Mining Trajectory similarity Spark

Spatial-temporal trajectory data contains rich information aboutmoving objects and has been widely used for a large numberof real-world applications. However, the complexity of spatial-temporal trajectory data, on the one hand, and the fast collectionof datasets, on the other hand, has made it challenging to ef-ficiently store, process, and query such data. In this paper, wepropose a scalable method to analyze the sub-trajectory simi-larity search in an in-memory cluster computing environment.Notably, we have extended Apache Spark with efficient trajectoryindexing, partitioning, and querying functionalities to supportthe sub-trajectory similarity query. Our experiments on a realtrajectory dataset have shown the efficiency and effectiveness ofthe proposed method.

Source: EDBT/ICDT 2020 Joint Conference - International Workshop in Big Mobility Data Analytics, Copenhagen, Denmark, 30th March - 2nd April, 2020

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@inproceedings{oai:it.cnr:prodotti:447159,
	title = {Towards in-memory sub-trajectory similarity search},
	author = {Alamdari I. and Nanni M. and Trasarti R. and Pedreschi D.},
	booktitle = {EDBT/ICDT 2020 Joint Conference - International Workshop in Big Mobility Data Analytics, Copenhagen, Denmark, 30th March - 2nd April, 2020},
	year = {2020}
}