Time-Aware Sub-Trajectory Clustering in Hermes@PostgreSQL

Tampakis, Panagiotis; Pelekis, Nikos; Andrienko, Natalia; Andrienko, Gennady; Fuchs, Georg; Theodoridis, Yannis

doi:10.1109/icde.2018.00181

Cited by 12 publications

(6 citation statements)

References 10 publications

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“…In addition, this richer information model allows us to compare whole trajectories or sub-trajectories without being restricted to the spatial or temporal dimension only. Existing approaches either require trajectories of equal length only or apply dynamic time warping techniques (Lee et al 2007, Etemad et al 2019 or employ a combined spatio-temporal representation when indexing trajectories (Tampakis et al 2018)…”

Section: Related Workmentioning

confidence: 99%

A distributed framework for extracting maritime traffic patterns

Kontopoulos

Varlamis

Tserpes

2020

International Journal of Geographical Information Science

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All the modern surveillance systems take advantage of the Automatic Identification System (AIS), a compulsory tracking system for many types of vessels. Ships that carry AIS transponders on board transmit their position and status in order to alert nearby vessels and ground stations, but this information can well be used to identify events of interest and support decision making. The detection of anomalies (i.e. unexpected sailing behavior) in vessels' trajectories is such an event, which is of utmost importance. Approaches for detecting such anomalies vary from extracting normality models to searching for individual cases, such as AIS switch-off or collision avoidance maneuvers. The current research work follows the former method; it employs sparse historic AIS data and polynomial interpolation in order to extract shipping lanes. It modifies the DB-Scan clustering algorithm in order to achieve more coherent trajectory clusters, which are then composed to create the shipping lanes. The proposed approach implements distributed processing on Apache Spark in order to improve processing speed and scalability and is evaluated using real-world AIS data collected from terrestrial AIS receivers. The evaluation shows that the biggest part (i.e. more than 90%) of any future vessel trajectory falls within the extracted shipping lanes.

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Section: Related Workmentioning

confidence: 99%

A distributed framework for extracting maritime traffic patterns

Kontopoulos

Varlamis

Tserpes

2020

International Journal of Geographical Information Science

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“…From our knowledge, this is the first approach that builds a composite model of speed, direction and position for trajectories, which is then used to directly detect deviations of any of the three features or any combination of them. It is also expected to provide a richer model for the comparison of whole trajectories or sub-trajectories than the techniques that employ equal length sub-trajectories, or dynamic time warping and spatial distances to compare trajectories [19,20] or techniques that combine spatial and temporal dimensions for indexing trajectories [21].…”

Section: Related Workmentioning

confidence: 99%

Uncovering Hidden Concepts from AIS Data: A Network Abstraction of Maritime Traffic for Anomaly Detection

Kontopoulos

Varlamis

Tserpes

2020

Lecture Notes in Computer Science

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The compulsory use of Automatic Identification System (AIS) for many vessel types, which has been enforced by naval regulations, has opened new opportunities for maritime surveillance. AIS transponders are rich sources of information that everyone can collect using an RF receiver and provide real-time information about vessels' position. Properly taking advantage of AIS data, can uncover potential illegal behavior, offer real-time alerts and notify the authorities for any kind of anomalous vessel behavior. In this article, we extend an existing network abstraction of maritime traffic, that is based on nodes (called way-points) that correspond to naval areas of long stays or major turns for vessels (e.g. ports, capes, offshore platforms etc.) and edges (called traversals) that correspond to the routes followed by vessels between two consecutive way-points. The current work, focuses on the connections of this network abstraction and enriches them with semantic information about the different ways that vessels employ when traversing an edge. For achieving this, it proposes an alternative of the popular density based clustering algorithm DB-Scan, which modifies the proximity parameter (i.e. epsilon) of the algorithm. The proposed alternative employs in tandem the difference in (i) speed, (ii) course and (iii) position for defining the distance between two consecutive vessel positions (two consecutive AIS signals received from the same vessel). The results show that this combination performs significantly better than using only the spatial distance and, more importantly, results in clusters that have very interesting properties. The enriched network model can be processed and further examined with data mining techniques, even in an unsupervised manner, in order to identify anomalies in vessels' trajectories. Experimental results on a real dataset show the network's potential for detecting trajectory outliers and uncovering deviations on a vessel's route.

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“…These common subtrajectories are then clustered and each cluster is represented by a pathlet, which is a point sequence that is not necessarily a subsequence of an actual trajectory. A different approach is presented in QuT-Clustering [31] and [35], where the goal is, given a temporal period of interest W , to efficiently retrieve already clustered subtrajectories, that temporally intersect W . To achieve this, a hierarchical structure, called ReTraTree (Representative Trajectory Tree) that effectively indexes a dataset for subtrajectory clustering purposes, is built and utilized.…”

Section: Problem Definition 1 Future Location Prediction (Flp)mentioning

confidence: 99%

Online Long-Term Trajectory Prediction Based on Mined Route Patterns

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Tampakis

Georgiou

et al. 2020

Lecture Notes in Computer Science

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In this paper, we present a Big data framework for the prediction of streaming trajectory data by exploiting mined patterns of trajectories, allowing accurate long-term predictions with low latency. In particular, to meet this goal we follow a two-step methodology. First, we efficiently identify the hidden mobility patterns in an offline manner. Subsequently, the trajectory prediction algorithm exploits these patterns in order to prolong the temporal horizon of useful predictions. The experimental study is based on real-world aviation and maritime datasets.

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Time-Aware Sub-Trajectory Clustering in Hermes@PostgreSQL

Cited by 12 publications

References 10 publications

A distributed framework for extracting maritime traffic patterns

A distributed framework for extracting maritime traffic patterns

Uncovering Hidden Concepts from AIS Data: A Network Abstraction of Maritime Traffic for Anomaly Detection

Online Long-Term Trajectory Prediction Based on Mined Route Patterns

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