VesselVision: Fleet Safety Awareness over Streaming Vessel Trajectories
(Fig. left) Visualization of high-risk (in terms of CRI) areas of interest (Fig. right) Visualization of encountering vessels with their corresponding CRI Video Presentation: Contributors: Andreas Tritsarolis, Nikos Pelekis and Yannis Theodoridis Data Science Laboratory, University of Piraeus Related Papers: VesselVision - Fleet Safety Awareness over Streaming Vessel Trajectories (Demo Paper). In proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2023). [demo poster] Code Repository: https://github.com/DataStories-UniPi/VesselVision Contact: Andreas Tritsarolis <andrewt@unipi.gr>
The Piraeus AIS Dataset for Large-scale Maritime Data Analytics
AIS data collected by the University of Piraeus' AIS receiver Contributors: Andreas Tritsarolis; Yannis Kontoulis; Yannis Theodoridis Data Science Laboratory, University of Piraeus Abstract The advent of Big Data and streaming technologies has resulted in a swarm of voluminous, heterogeneous information, especially in the domains of Internet of Things (IoT) and transportation. Focusing on the maritime field, we present a dataset that contains vessel position information transmitted by vessels of different types and collected via the Automatic Identification System (AIS). The AIS dataset comes along with spatially and temporally correlated data about the vessels and the area of interest, including weather information. It covers a time span of over 2.5 years, from May 9th, 2017 to December 26th, 2019 and provides anonymised vessel positions within the wider area of the port of Piraeus…
ST-Visions
A Python-based library for interactive spatio-temporal data visualization. Overview ST_Visions (Spatio-Temporal Visualizations) is a python library, able to interactively visualize spatio-temporal data in a quick-and-easy way. Based upon the functionality of Bokeh, and further extending it, we are able to create powerful and cohesive visualizations (and/or online dashboards), for large or streaming spatio-temporal datasets. Paper: Link GitHub repository: Link Contributors: Andreas Tritsarolis, Christos Doulkeridis, Yannis Theodoridis and Nikos PelekisData Science Laboratory, University of Piraeus
MaSEC: Discovering Anchorages and Co-movement Patterns on Streaming Vessel Trajectories
Video Presentation: Contributors: Andreas Tritsarolis, Yannis Kontoulis, Nikos Pelekis and Yannis Theodoridis Data Science Laboratory, University of Piraeus Related Papers: MaSEC: Discovering Anchorages and Co-movement Patterns on Streaming Vessel Trajectories (SSTD’21 Best Demo Paper Award) GitHub repository: Link Contact: Andreas Tritsarolis <andrewt@unipi.gr>
i4Sea: Surveillance and Analysis of Marine Areas Movement using Big Data
The final review of the i4Sea project was successfully held on November 11, 2021. During this 3-year project (July 2018 - July 2021), an innovative platform for the collection and analysis of large marine surveillance data was developed. The vision of the project was to efficiently and quickly process, integrate and analyze these data in order to create (a) a “Combined Real-time Business Image” and (b) a “Combined Historic Image” of Marine Surveillance. More details about i4Sea are available in the project’s official web site.
Data Science Lecture Series, Fall semester, 2021-22
The fall 2021-22 edition of the Data Science lecture series, provided by the Data Science Lab, consists of the following lectures:@ Wed. 8/12/2021, 19:45-20:45: Prof. Athena Vakali (AUTH) - Bot Detection in Online Social Networks@ Mon. 10/1/2022 18:30-19:30: Dr. Nikos Giatrakos (TU Crete) - EasyFlinkCEP: Big Event Data Analytics for Everyone@ Wed. 12/1/2022, 18:30-19:30: Prof. Periklis Andritsos (Univ, Toronto) - An Inside Look at Customer Journey Analytics@ Mon. 21/2/2022, 18:30-19:30: Dr. Foteini Valeonti (UCL & USEUM.org) - Blockchain, NFTs and Culture Free participation via MS Teams (link)
Univ. Piraeus AIS Stream Visualization
Visualizing streaming AIS positions from vessels sailing in the wider area off the port of Piraeus (Saronic Gulf, Greece). Live Stream: http://www.datastories.org/unipi-ais/ Open-source (GitHub repository): https://github.com/DataStories-UniPi/unipi-ais Usage statistics about our AIS antenna can be found at MarineTraffic, VesselFinder, and AISHub networks- Related publications: MDM’2021, SSTD'2021, Data in Brief 2022- Related datasets: The Piraeus AIS Dataset for Large-scale Maritime Data Analytics- Funding grants: VesselAI, i4Sea Contributors: Andreas Tritsarolis, Yannis Kontoulis and Yannis TheodoridisData Science Laboratory, University of PiraeusCONTACT Andreas Tritsarolis: andrewt@unipi.gr
Hermoupolis – A Trajectory Generator for Simulating Generalized Mobility Patterns
During the last decade, the domain of mobility data mining has emerged providing many effective methods for the discovery of intuitive patterns representing collective behavior of trajectories of moving objects. Although a few real-world trajectory datasets have been made available recently, these are not sufficient for experimentally evaluating the various proposals, therefore, researchers betake to synthetic trajectory generators. This case is problematic because, on the one hand, real datasets are usually small, which hardens scalability experiments, and, on the other hand, synthetic dataset generators have not been designed to produce mobility pattern driven trajectories. Motivated by this observation, we present Hermoupolis, an effective generator of synthetic trajectories of moving objects that has the main objective that the resulted datasets support various types of mobility patterns (clusters, flocks, convoys, etc.), as…