Deliverables
Deployment of a long reach transport network solution interconnecting the rolling stock/ sensing devices with the OSS. This will be based on the INITIATE optical network infrastructure that will be coupled with LTE and FSO links.
On-board network architecture and dimensioningDeployment of a heterogeneous network infrastructure comprising WiFi/LiFi/LTE technologies interconnecting the on-board devices (sensors, smartphones etc) with the gateway.
Data analytics scenariosThis deliverable will describe the data analytics scenarios defined in Task 5.1. The deliverable will include both the data availability and the analytics perspectives.
Operations optimisationThis deliverable will describe the work done in Task 6.4 related to the railway operations.
Set-up public websiteThis is for the set up, and going live, of a public website for the project. This will be continuously updated with news and results throughout the life of the project.
Visual analytics of railway data and modelsThis deliverable will describe the work done in Task 5.4 concerning visual analytics in railways and knowledge extraction from data analytics algorithms, including achievements for the scenarios defined in D5.1.
Dissemination and Exploitation activities reportReport summarising the results/impacts of all the dissemination, communication and exploitation activities
The Data Transactions model in railways ecosystemsThis deliverable reports the outcomes of Task 4.1 and provides an overview of the main business scenarios and use cases for railway ecosystems that will be suitable for implementation with self-executing contracts technology. The deliverable will also describe the main advantages that may introduce operational efficiency in processes and relationship between the actors involved in the data exchange.
Legal aspects for smart contract adoptionThis deliverable reports the outcome of task 4.4 and includes a general overview of the legal aspects related to smart contracts adoption in railway ecosystems and a set of specific recommendations and guidelines for actors that approach the paradigm within their processes.
IN2DREAMS services, use cases and requirementsThis deliverable will provide the definition of the ecosystem that the IN2DREAMS is aiming to address. The railway services, use cases and the requirements that IN2DREAMS demonstrator will support will be described in detail and an analysis on how these influence the KPIs that the overall architecture can support will be given.
This deliverable reports the outcome of task 4.3 with regards to the design and implementation of the smart contract. The deliverable consists of the release of a working example of a smart contract and related documentation which will describe the requirements and the business logic of the smart contract.
Analytics platformThis deliverable will describe the data analytics platform defined in Task 6.1, as an open source platform to complement WP3. The algorithms of tasks 6.2, 6.3 and 6.4 will be included also in this repository.
Rule-based and Visual analytics knowledge extraction demonstratorThis deliverable will describe the demonstrators that will be developed to show the potentialities of the results of Task 5.2 and 5.4.
Publications
Author(s):
Udo Schlegel, Wolfgang Jentner, Juri Buchmueller, Eren Cakmak, Giuliano Castiglia, Renzo Canepa, Simone Petralli, Luca Oneto, Daniel A. Keim, Davide Anguita
Published in:
Recent Advances in Big Data and Deep Learning - Proceedings of the INNS Big Data and Deep Learning Conference INNSBDDL2019, held at Sestri Levante, Genova, Italy 16-18 April 2019, Issue 1, 2020, Page(s) 206-215, ISBN 978-3-030-16840-7
Publisher:
Springer International Publishing
DOI:
10.1007/978-3-030-16841-4_22
Author(s):
Luca Oneto, Irene Buselli, Paolo Sanetti, Renzo Canepa, Simone Petralli, Davide Anguita
Published in:
Recent Advances in Big Data and Deep Learning - Proceedings of the INNS Big Data and Deep Learning Conference INNSBDDL2019, held at Sestri Levante, Genova, Italy 16-18 April 2019, Issue 1, 2020, Page(s) 136-141, ISBN 978-3-030-16840-7
Publisher:
Springer International Publishing
DOI:
10.1007/978-3-030-16841-4_14
Author(s):
Roberto Spigolon, Luca Oneto, Dimitar Anastasovski, Nadia Fabrizio, Marie Swiatek, Renzo Canepa, Davide Anguita
Published in:
Recent Advances in Big Data and Deep Learning - Proceedings of the INNS Big Data and Deep Learning Conference INNSBDDL2019, held at Sestri Levante, Genova, Italy 16-18 April 2019, Issue 1, 2020, Page(s) 120-125, ISBN 978-3-030-16840-7
Publisher:
Springer International Publishing
DOI:
10.1007/978-3-030-16841-4_12
Author(s):
Luca Oneto, Irene Buselli, Alessandro Lulli, Renzo Canepa, Simone Petralli, Davide Anguita
Published in:
Recent Advances in Big Data and Deep Learning - Proceedings of the INNS Big Data and Deep Learning Conference INNSBDDL2019, held at Sestri Levante, Genova, Italy 16-18 April 2019, Issue 1, 2020, Page(s) 142-151, ISBN 978-3-030-16840-7
Publisher:
Springer International Publishing
DOI:
10.1007/978-3-030-16841-4_15
Author(s):
Stefanos Gogos, Guillaume Pelletier, Simona Soldi, Davide Anguita, Nadia Fabrizio, Markos Anastasopoulos
Published in:
Project Repository Journal, Issue Issue 3, every 3 months, 2019, Page(s) 88-91, ISSN 2632-4067
Publisher:
European Dissemination Media Agency
Author(s):
Charlotte DUCUING
Published in:
European Journal of Risk Regulation, Issue 10/2, 2019, Page(s) 315-329, ISSN 1867-299X
Publisher:
Lexxion
DOI:
10.1017/err.2019.39
Author(s):
Luca Oneto, Irene Buselli, Alessandro Lulli, Renzo Canepa, Simone Petralli, Davide Anguita
Published in:
International Journal of Data Science and Analytics, 2018, ISSN 2364-415X
Publisher:
Springer International Publishing
DOI:
10.1007/s41060-018-00171-z
Author(s):
Ducuing, Charlotte ; Oneto, Luca ; Petralli, Simone
Published in:
ESANN 2019 – Proceedings - 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2019
Publisher:
ESANN
Author(s):
Alessandro Lulli, Luca Oneto, Renzo Canepa, Simone Petralli, Davide Anguita
Published in:
2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), 2018, Page(s) 371-380, ISBN 978-1-5386-5090-5
Publisher:
IEEE
DOI:
10.1109/dsaa.2018.00048
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