Efficient AIS Data Processing for Environmentally Safe Shipping

Reducing ship accidents at sea is important to all economic, environmental, and cultural sectors of Greece. Despite an increase in traffic and national monitoring, ships formulate routes according to their best judgment risking an accident. In this study we take a dataset spanning in 3 years from the AIS (Automatic Identification System) network, which is transmitting in public a ship's identity and location with an interval of seconds, and we load it in a trajectory database supported by the Hermes Moving Objects Database (MOD) system. Presented analysis begins by extracting statistics for the dataset, both general (number of ships and position reports) as well as safety related ones. Simple queries on the dataset illustrate the capabilities of Hermes and allow to gain insight on how the ships move in the Greek Seas. Analysis of movement based on an Origin-Destination matrix between interesting areas in the Greek territory is presented. One of the newest challenges that emerged during this process is that the amount of the positioning data is becoming more and more massive. As a conclusion, a preliminary review of possible solutions to this challenge along with others such as dealing with the noise in AIS data is mentioned and we also briefly discuss the need for interdisciplinary cooperation.This research was partially supported by AMINESS project funded by the Greek government (www.aminess.eu). Cyril Ray was supported by a Short Term Scientific Mission performed at the University of Piraeus by the COST Action IC0903 on “Knowledge Discovery from Moving Objects” (http://www.move-cost.info). IMIS Hellas (www.imishel las.gr) kindly provided the AIS dataset for research purposes

Efficient AIS Data Processing for Environmentally Safe Shipping

Reducing ship accidents at sea is important to all economic, environmental, and cultural sectors of Greece. Despite an increase in traffic and national monitoring, ships formulate routes according to their best judgment risking an accident. In this study we take a dataset spanning in 3 years from the AIS (Automatic Identification System) network, which is transmitting in public a ship's identity and location with an interval of seconds, and we load it in a trajectory database supported by the Hermes Moving Objects Database (MOD) system. Presented analysis begins by extracting statistics for the dataset, both general (number of ships and position reports) as well as safety related ones. Simple queries on the dataset illustrate the capabilities of Hermes and allow to gain insight on how the ships move in the Greek Seas. Analysis of movement based on an Origin-Destination matrix between interesting areas in the Greek territory is presented. One of the newest challenges that emerged during this process is that the amount of the positioning data is becoming more and more massive. As a conclusion, a preliminary review of possible solutions to this challenge along with others such as dealing with the noise in AIS data is mentioned and we also briefly discuss the need for interdisciplinary cooperation.This research was partially supported by AMINESS project funded by the Greek government (www.aminess.eu). Cyril Ray was supported by a Short Term Scientific Mission performed at the University of Piraeus by the COST Action IC0903 on “Knowledge Discovery from Moving Objects” (http://www.move-cost.info). IMIS Hellas (www.imishel las.gr) kindly provided the AIS dataset for research purposes

In-DBMS Sampling-based Sub-trajectory Clustering

In this paper, we propose an efficient in-DBMS solution for the problem of sub-trajectory clustering and outlier detection in large moving object datasets. The method relies on a two-phase process: a voting-and-segmentation phase that segments trajectories according to a local density criterion and trajectory similarity criteria, followed by a sampling-and-clustering phase that selects the most representative sub-trajectories to be used as seeds for the clustering process. Our proposal, called S 2 T-Clustering (for Sampling-based Sub-Trajectory Clustering) is novel since it is the first, to our knowledge, that addresses the pure spatiotemporal sub-trajectory clustering and outlier detection problem in a real-world setting (by ‘pure’ we mean that the entire spatiotemporal information of trajectories is taken into consideration). Moreover, our proposal can be efficiently registered as a database query operator in the context of extensible DBMS (namely, PostgreSQL in our current implementation). The effectiveness and the efficiency of the proposed algorithm are experimentally validated over synthetic and real-world trajectory datasets, demonstrating that S 2 T-Clustering outperforms an off-the-shelf in-DBMS solution using PostGIS by several orders of magnitude