Deliverables Documents, reports (14) VesselAI HPC Hw/Sw Requirements and Specifications Requirements and Specifications for VesselAI HPC HwSw Final VesselAI Methodology This deliverable based on the feedback received by the projects end users will update the VesselAI methodology as well as any main components needed Any updates in the work implemented in D11 will also be considered finalising preliminary user requirements and integrated VesselAIs value chain VesselAI Models, AI methods and Tools- v1.00 These deliverables will include the research design and delivery of all modules of the AI services including the data feed module the Model development module the model serving module as well as the resulting trained models Plan for Dissemination, Communication and Stakeholder Engagement This deliverable is related to the provision of a detailed plan for all dissemination communication community building and awareness creation activities that will be scheduled for the first reporting period of the project Specifications of the AI On-Demand platform extensions and research activities-V2.00 These deliverables will include the specifications of the Vessel AI services as extensions to AI On demand existing services as well as all contributions to the ongoing AI4EU research activities Specifications of the AI On-Demand platform extensions and research activities These deliverables will include the specifications of the Vessel AI services as extensions to AI On demand existing services as well as all contributions to the ongoing AI4EU research activities VesselAI Validation and Evaluation Framework This deliverable is related to the documentation of the evaluation framework and validation methodology defining the various practices for obtaining feedback from endusers and including a set of testcases to be executed by the endusers VesselAI Extreme-scale data processing, management services and semantics - v1.00 These series of deliverables will report the scientific foundations the design requirements and final services of the VesselAI project on the involved data services technologies as studied and researched within tasks T21T25 including the Data Ingestion and Management Module the Semantic Enrichment Module the Triplestore and Reasoning Engine and the Distributed Query Execution Engine State-of-the-art analysis and data sources This deliverable will include a thorough stateofplay analysis of the current landscape in terms of HPC BigData AI and advanced data processing techniques for extreme scale analytics and vessel models in particular including components tools and methodologies eventually promoting the most appropriate ones for the needs of VesselAI Moreover a definition of the VesselAI value chain the corresponding stakeholders and important information sources to be exploited will also be included The needs coming from the projects end users will also constitute a main output of this deliverable as a result of a 1st iteration of interviews and surveys separate part of this deliverable will deal with the definition of content IPR handling and sharing throughout the platform VesselAI Methodology and MVP The integrated project methodology as well as the Most Valuable Product MVP of VesselAI will be reported in the terms of this deliverable VesselAI Technology Requirements & User Stories This deliverable is related to the activities to be performed under Tasks 51 and will provide the user stories as well as the technology requirements for the VesselAI solution in alignment with the AI4EU platform as envisioned by the different user groups and will flesh out the concept VesselAI Platform Components, Services & APIs Architecture D52 will include the architecture diagrams and all design documents regarding the platform the components and the tobedeveloped VesselAI services while it will also define and describe the API interfaces that will be implemented Dissemination, Communication and Stakeholder Engagement Report and Plan - Interim Version This deliverable is related to the provision of a detailed plan for all dissemination communication community building and awareness creation activities that will be scheduled for the second reporting period of the project while it will also summarise all activities performed during the first reporting period of VesselAI VesselAI Pilots Readiness Documentation and Execution Scenarios Documentation of the set of scenarios realising the test cases in the given context and scope of the enduserorganisation that will run during each pilot including the actors involved the evaluation indicators the overall time plan and a detailed analysis of the required data sources per pilot and the an initial analysis on how each is envisioned to be leveraged in the scope of the pilot Open Research Data Pilot (1) Data Management Handling Plan This deliverable is related to the detailed plan about which data will be collected and generated during the project and how it will be shared and opened This information is essential to decide the best sustainability model for project results and disseminate according to this plan the open data provided by the project The deliverable will be updated at end of the project Websites, patent fillings, videos etc. (1) Project's Website and Web 2.0 Channels This deliverable is related to the provision of the Website of the project and will set up all needed web and social channels that will be used during the project for communication Publications Conference proceedings (11) MaSEC: Discovering Anchorages and Co-movement Patterns on Streaming Vessel Trajectories Author(s): Tritsarolis Andreas, Kontoulis Yannis, Pelekis Nikos, Theodoridis Yannis Published in: 17th International Symposium on Spatial and Temporal Databases (SSTD '21), 2021 Publisher: Association for Computing Machinerυ DOI: 10.1145/3469830.3470909 Methodology for Approval of Autonomous Ship Systems CONOPS Author(s): Hagaseth Marianne, Rødseth Ørnulf Jan, Meland Per Håkon, Wille Egil, Meling Pia, Murray Brian Published in: 21st International Conference on Computer and IT Applications in the Maritime Industries (COMPIT'22), Pontignano, Italy., 2022 Publisher: COMPIT DOI: 10.5281/zenodo.6792507 ST_VISIONS: A Python Library for Interactive Visualization of Spatio-temporal Data Author(s): Tritsarolis Andreas, Doulkeridis Christos, Pelekis Nikos, Theodoridis Yannis Published in: 2021 22nd IEEE International Conference on Mobile Data Management (MDM), 2021 Publisher: IEEE DOI: 10.1109/mdm52706.2021.00048 Vessel Collision Risk Assessment using AIS Data: A Machine Learning Approach Author(s): Tritsarolis Andreas, Chondrodima Eva, Pelekis Nikos, Theodoridis Yannis Published in: 3rd IEEE International Maritime Big Data Workshop of the 23rd MDM Conference, Paphos, Cyprus, June 6, 2022, Issue June 6, 2022, 2022 Publisher: IEEE DOI: 10.5281/zenodo.6795554 Data-driven digital twins for the maritime domain Author(s): Troupiotis Alexandros, Kaliorakis Manolis, Zissis Dimitris, Mouzakitis Spiros, Tsapelas Giannis, Artikis Alexander, Chondrodima Eva, Theodoridis Yannis Published in: 20th International Conference on Ship and Maritime Research (NAV 2022), Genova- La Spezia, Italy, 15-17 June 2022, Issue 15-17 June 2022, 2022 Publisher: Associazione Italiana di Tecnica Navale DOI: 10.5281/zenodo.6795530 Optimizing complex event forecasting Author(s): Stavropoulos Vasileios, Alevizos Elias, Giatrakos Nikos, Artikis Alexander Published in: 16th ACM International Conference on Distributed and Event-Based Systems (DEBS'22), Copenhagen, Denmark, June 27 - 30, 2022, Issue June 27 - 30, 2022, 2022 Publisher: Association for Computing Machinery DOI: 10.1145/3524860.3539810 MARVEL Workshop - DATAWEEK2022 - The challenges of the extreme-scale multi-modal analytics applications Author(s): Sotiris Ioannidis, Manfredo Atzori, Nikolaos Passalis, Paulo Figueiras Published in: MARVEL Workshop - DATAWEEK2022 - The challenges of the extreme-scale multi-modal analytics applications., Issue June 19 2022, 2022 Publisher: Big Data Value Association and the EUHubs4Data project DOI: 10.5281/zenodo.6667599 Machine Learning Models for Vessel Traffic Flow Forecasting: An Experimental Comparison Author(s): Mandalis Petros, Chondrodima Eva, Kontoulis Yannis, Pelekis Nikos, Theodoridis Yannis Published in: 3rd IEEE International Maritime Big Data Workshop of the 23rd MDM Conference, Paphos, Cyprus, June 6, 2022, Issue June 6, 2022, 2022 Publisher: IEEE DOI: 10.5281/zenodo.6795546 Machine Learning Models for Vessel Route Forecasting: An Experimental Comparison Author(s): Chondrodima Eva, Mandalis Petros, Pelekis Nikos, Theodoridis Yannis Published in: 23rd IEEE International Conference on Mobile Data Management (MDM), June 6 – 9, 2022, Issue June 6 – 9, 2022, 2022 Publisher: IEEE DOI: 10.5281/zenodo.6795539 Bridging the Chasm between Science and Reality Author(s): Kersten Martin, Koutsourakis Panagiotis, Niels Nes, Zhang Ying Published in: Conference on Innovative Data Systems Research 2021 (CIDR), 2021 Publisher: CIDR DOI: 10.5281/zenodo.6782789 Detecting representative trajectories from global AIS datasets Author(s): Zygouras Nikolas, Spiliopoulos Giannis, Zissis Dimitris Published in: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA, 19-22 September 2021, 2021 Publisher: IEEE DOI: 10.1109/itsc48978.2021.9564657 Peer reviewed articles (3) ATSC-NEX: Automated Time Series Classification With Sequential Model-Based Optimization and Nested Cross-Validation Author(s): Tahkola Mikko, Guangrong Zou Published in: IEEE Access, Issue Volume 10, 2022, Page(s) 39299-39312, ISSN 2169-3536 Publisher: Institute of Electrical and Electronics Engineers Inc. DOI: 10.1109/access.2022.3166525 The Piraeus AIS dataset for large-scale maritime data analytics Author(s): Tritsarolis Andreas, Kontoulis Yannis, Theodoridis Yannis Published in: Data in Brief, Issue Volume 41, April 2022, 2022, Page(s) Pages 107940, ISSN 2352-3409 Publisher: Elsevier BV DOI: 10.1016/j.dib.2021.107782 A Survey on Big Data Processing Frameworks for Mobility Analytics Author(s): Doulkeridis Christos, Vlachou Akrivi, Pelekis Nikos, Theodoridis Yannis Published in: ACM SIGMOD Record, Issue Volume 50, Issue 2, 2021, Page(s) 18-29, ISSN 0163-5808 Publisher: Association for Computing Machinary, Inc. DOI: 10.1145/3484622.3484626 Searching for OpenAIRE data... There was an error trying to search data from OpenAIRE No results available
