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Tesi etd-05042020-081533


Thesis type
Tesi di dottorato di ricerca
Author
ISFAHANIALAMDARI, OMID
URN
etd-05042020-081533
Title
Scalable Processing and Mining of Big Mobility Data
Settore scientifico disciplinare
INF/01
Corso di studi
INFORMATICA
Supervisors
tutor Prof. Pedreschi, Dino
correlatore Dott. Trasarti, Roberto
Parole chiave
  • AIS
  • Apache Spark
  • Trajectory Annotation
  • Similarity Search
  • Trajectory
  • Big Mobility Data
Data inizio appello
07/05/2020;
Consultabilità
Parziale
Data di rilascio
07/05/2023
Riassunto analitico
Spatial-temporal trajectory data contains rich information about moving objects and has been widely used for a large number of real-world applications. However, the complexity of spatial-temporal trajectory data, on the one hand, and the fast collection of datasets, on the other hand, has made it challenging to efficiently store, process, and query such data.
In this thesis, we proposed two scalable methods to analyze big mobility data in the in-memory cluster computing environment. Particularly, we have extended Apache Spark with efficient trajectory indexing, partitioning and querying functionalities to support trajectory data analytics. We proposed distributed methods to the important problems of sub-trajectory similarity search and vessel trajectory annotation.
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