Exploratory Trajectory Analysis for Massive Historical AIS Datasets

Anita Graser; Melitta Dragaschnig; Peter Widhalm; Hannes Koller; Norbert Brändle

doi:10.1109/MDM48529.2020.00059

Exploratory Trajectory Analysis for Massive Historical AIS Datasets

Anita Graser (Speaker)
, Melitta Dragaschnig
, Peter Widhalm
, Hannes Koller
, Norbert Brändle

Digital Resilient Cities

Research output: Chapter in Book or Conference Proceedings › Conference Proceedings with Oral Presentation › peer-review

Abstract

Data exploration is an essential task for gaining an understanding of the potential and limitations of novel datasets. This paper discusses the challenges related to exploring large Automatic Identification System (AIS) datasets. We address these challenges using trajectory-based analysis approaches implemented in distributed computing environments using Spark and GeoMesa. This approach enables the exploration of datasets that are too big to handle within conventional spatial database systems. We demonstrate our approach using a case study of 4 billion AIS records.

Original language	English
Title of host publication	Proceedings 21st IEEE International Conference on Mobile Data Management (MDM)
Pages	252-257
Number of pages	6
DOIs	https://doi.org/10.1109/MDM48529.2020.00059
Publication status	Published - 2020
Event	21st IEEE International Conference on Mobile Data Management (MDM) - Duration: 30 Jun 2020 → …

Conference

Conference	21st IEEE International Conference on Mobile Data Management (MDM)
Period	30/06/20 → …

Research Field

Former Research Field - Mobility Systems

Access to Document

10.1109/MDM48529.2020.00059Licence: Unspecified

Cite this

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title = "Exploratory Trajectory Analysis for Massive Historical AIS Datasets",

abstract = "Data exploration is an essential task for gaining an understanding of the potential and limitations of novel datasets. This paper discusses the challenges related to exploring large Automatic Identification System (AIS) datasets. We address these challenges using trajectory-based analysis approaches implemented in distributed computing environments using Spark and GeoMesa. This approach enables the exploration of datasets that are too big to handle within conventional spatial database systems. We demonstrate our approach using a case study of 4 billion AIS records.",

author = "Anita Graser and Melitta Dragaschnig and Peter Widhalm and Hannes Koller and Norbert Br{\"a}ndle",

year = "2020",

doi = "10.1109/MDM48529.2020.00059",

language = "English",

pages = "252--257",

booktitle = "Proceedings 21st IEEE International Conference on Mobile Data Management (MDM)",

note = "21st IEEE International Conference on Mobile Data Management (MDM) ; Conference date: 30-06-2020",

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T1 - Exploratory Trajectory Analysis for Massive Historical AIS Datasets

AU - Dragaschnig, Melitta

AU - Widhalm, Peter

AU - Koller, Hannes

AU - Brändle, Norbert

A2 - Graser, Anita

PY - 2020

Y1 - 2020

N2 - Data exploration is an essential task for gaining an understanding of the potential and limitations of novel datasets. This paper discusses the challenges related to exploring large Automatic Identification System (AIS) datasets. We address these challenges using trajectory-based analysis approaches implemented in distributed computing environments using Spark and GeoMesa. This approach enables the exploration of datasets that are too big to handle within conventional spatial database systems. We demonstrate our approach using a case study of 4 billion AIS records.

AB - Data exploration is an essential task for gaining an understanding of the potential and limitations of novel datasets. This paper discusses the challenges related to exploring large Automatic Identification System (AIS) datasets. We address these challenges using trajectory-based analysis approaches implemented in distributed computing environments using Spark and GeoMesa. This approach enables the exploration of datasets that are too big to handle within conventional spatial database systems. We demonstrate our approach using a case study of 4 billion AIS records.

U2 - 10.1109/MDM48529.2020.00059

DO - 10.1109/MDM48529.2020.00059

M3 - Conference Proceedings with Oral Presentation

SP - 252

EP - 257

BT - Proceedings 21st IEEE International Conference on Mobile Data Management (MDM)

T2 - 21st IEEE International Conference on Mobile Data Management (MDM)

Y2 - 30 June 2020

ER -

Exploratory Trajectory Analysis for Massive Historical AIS Datasets

Abstract

Conference

Research Field

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