Knowledge extraction from maritime spatiotemporal data: An evaluation of clustering algorithms on Big Data

In this paper we attempt to define the major trade routes which vessels of trade follow when travelling across the globe in a scalable, data-driven unsupervised way. For this, we exploit a large volume of historical AIS data, so as to estimate the location and connections of the major trade routes,...

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Bibliographic Details
Published in2017 IEEE International Conference on Big Data (Big Data) pp. 1682 - 1687
Main Authors Spiliopoulos, Giannis, Chatzikokolakis, Konstantinos, Zissis, Dimitrios, Biliri, Evmorfia, Papaspyros, Dimitrios, Tsapelas, Giannis, Mouzakitis, Spyros
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2017
Subjects
AIS
Algorithm design and analysis
Apache Spark
Artificial intelligence
Big Spatiotempotal Data
Clustering algorithms
Clustering Algorithms evaluation
Geospatial analysis
Ports (Computers)
Shipping routes
Spatiotemporal phenomena
Trajectory
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Summary:In this paper we attempt to define the major trade routes which vessels of trade follow when travelling across the globe in a scalable, data-driven unsupervised way. For this, we exploit a large volume of historical AIS data, so as to estimate the location and connections of the major trade routes, with minimal reliance on other sources of information. We address the challenges posed due to the volume of data by leveraging distributed computing techniques and present a novel MapReduce based algorithmic approach, capable of handling skewed and nonuniform geospatial data. In the direction, we calculate and compare the performance (execution time and compression ratio) and accuracy of several mature clustering algorithms and present preliminary results.
DOI:10.1109/BigData.2017.8258106