Livrables
A report documenting the plan for the development of the pilots and the methodology and materials for the pilot trials.
Scalability and Robustness Experimental MethodologyA document describing the methodology, the performance criteria, the protocols for the experiments, and the technical infrastructure requirements.
BigDataGrapes Software Stack DesignA report of the overall design of the BigDataGrapes software stack, including functional specifications, communication standards, external tools and underlying frameworks.
Use Cases & Technical Requirements SpecificationA report presenting in detail the BigDataGrapes use cases and their interpretation with respect to technical and infrastructural requirements.
Data Management Plan & Support PackThis deliverable will be a report that will specify how data will be collected, processed, monitored, catalogued, and disseminated during the project lifetime. It will also include a Support Pack with guidelines for the project coordinator and the partners, explaining how they should practically apply the guidelines during their activities, which software tools and services they should use, and how they can align the project requirements with their institutions’ standard practices and systems.
Experimental Report on Projected DatasetsA document with the experimental results after testing the BigDataGrapes components over the two foreseen dataset projections (2020 and 2030).
Evaluation Report and KPI AssessmentA report on the results of the application piloting sessions, inline with the defined experimental protocols and in accordance with the evaluation methodology produced in the context of T8.2.
Annual Public ReportA report summarising the achievements and outcomes of the project for the reporting period, targeted to the general public. It will be made available via the project’s website.
Experimental Report on Current DatasetsA document with the experimental results after the execution of the methodology over the data sets that were contributed to the project by the data partners.
Experimental Protocols and Evaluation MethodologyA report describing the experiments to be conducted and their parameters, along with the methodology for assessing the results of the experiments in accordance with the piloting plan.
Dissemination and Awareness ReportA report documenting the various dissemination, awareness and outreach activities and results for the respective period.
A deployment of the Integrated BigDataGrapes Software Stack in the cloud, providing access and documentation for the APIs of the different components comprising the stack.
Data Ingestion and Integration ComponentsThe software components for carrying out data ingestion in the BigDataGrapes ecosystem, along with their documentation.
Methods and Tools for Distributed InferenceSoftware assets the implement the distributed inference mechanisms envisioned in the project. They will be accompanied by a report providing details on the adopted methodology and by the documentation of the software.
Resource Optimization Methods and AlgorithmsSoftware assets the implement the novel resource optimization techniques envisioned in the project. They will be accompanied by a report providing details on the adopted methodology and by the documentation of the software.
Data Modelling and Linking ComponentsA tool for creating, maintaining and linking semantic data, customized to serve the needs of the relevant grapevine-powered industries.
Uncertainty-aware Visual Analytics ComponentsSoftware assets the implement the novel uncertainty-aware visual analytics mechanisms envisioned in the project. They will be accompanied by a report providing details on the adopted methodologies and by the documentation of the software.
Linguistic Pipelines for Semantic EnrichmentA set of cooperating components carrying out different NLP tasks in an organized fashion in order to extract knowledge from unstructured text and use the results to semantically annotate relevant BigDataGrapes data.
Distributed Indexing ComponentsSoftware assets the implement the novel Big Data indexing mechanisms envisioned in the project. They will be accompanied by a report providing details on the adopted methodology and by the documentation of the software.
Methods and Tools for Predictive Analytics over Extremely Large DatasetsSoftware assets the implement the novel predictive analytics methods envisioned in the project. They will be accompanied by a report providing details on the adopted methodology and by the documentation of the software.
Analytics & Processing LayerSoftware assets the implement the novel distributed processing mechanisms envisioned in the project. They will be accompanied by a report providing details on the adopted methodology and by the documentation of the software.
Interactive Visualization ComponentsA library of interactive visualisation components adjusted to the handling of Big Data and incorporating interaction and parameterisation techniques. The software will be accompanied by a detailed documentation and a summary of the novel methods implemented within the components.
Trust-aware Decision Support SystemsDecision support component that incorporates the novel trust-aware recommendation components envisioned in the project. They will be accompanied by a report providing details on the adopted methodologies and by the documentation of the software.
A Farm Management System prototype incorporating the appropriate functionalities of the BigDataGrapes software stack, which will be used in the relevant piloting sessions.
Integration and Operation with real-life PracticesA dashboard targeting industry-level decision makers and practitioners that incorporates the appropriate functionalities of the BigDataGrapes software stack, which will be used in the relevant piloting sessions.
Setup and maintenance plan for the project’s web site, social media accounts, blogs, forums, etc.
Publications
Auteurs:
Alexiev, Vladimir
Publié dans:
Numéro 1, 2018
Éditeur:
European Commission
DOI:
10.5281/zenodo.1482757
Auteurs:
Zervas, Panagiotis; Konstantinidis, Sotiris; Koukourikos, Antonis
Publié dans:
Numéro 1, 2018
Éditeur:
European Commission
DOI:
10.5281/zenodo.1482751
Auteurs:
Ida Mele, Cristina Ioana Muntean, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Ophir Frieder
Publié dans:
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, Page(s) 2057-2060, ISBN 9781450380164
Éditeur:
ACM
DOI:
10.1145/3397271.3401268
Auteurs:
Leopoldo Soares de Melo Junior, Franco Maria Nardini, Chiara Renso, Jose Antonio Fernandes de Macedo
Publié dans:
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019, Page(s) 747-754, ISBN 978-1-7281-4550-1
Éditeur:
IEEE
DOI:
10.1109/icmla.2019.00133
Auteurs:
Leopoldo Melo, Franco Maria Nardini, Chiara Renso, Jose Antonio Macedo
Publié dans:
2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019, Page(s) 424-431, ISBN 978-1-7281-3798-8
Éditeur:
IEEE
DOI:
10.1109/ictai.2019.00066
Auteurs:
Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, Ophir Frieder
Publié dans:
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, Page(s) 529-538, ISBN 9781450380164
Éditeur:
ACM
DOI:
10.1145/3397271.3401094
Auteurs:
Franco Maria Nardini, Roberto Trani, Rossano Venturini
Publié dans:
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019, Page(s) 815-824, ISBN 9781450361729
Éditeur:
ACM
DOI:
10.1145/3331184.3331227
Auteurs:
Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, Ophir Frieder
Publié dans:
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, Page(s) 49-58, ISBN 9781450380164
Éditeur:
ACM
DOI:
10.1145/3397271.3401093
Auteurs:
Simon Gog, Giulio Ermanno Pibiri, Rossano Venturini
Publié dans:
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, Page(s) 2271-2280, ISBN 9781450380164
Éditeur:
ACM
DOI:
10.1145/3397271.3401432
Auteurs:
Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Salvatore Trani
Publié dans:
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, Page(s) 2033-2036, ISBN 9781450380164
Éditeur:
ACM
DOI:
10.1145/3397271.3401256
Auteurs:
Lucchese, Claudio; Nardini, Franco Maria; Perego, Raffaele; Trani, Roberto; Venturini, Rossano
Publié dans:
Numéro 24, 2018, ISBN 978-1-4503-6014-2
Éditeur:
Association for Computing Machinery (ACM)
DOI:
10.5281/zenodo.2668248
Auteurs:
Catena, Matteo; Frieder, Ophir; Tonellotto, Nicola
Publié dans:
Numéro 25, 2018, ISBN 978-1-4503-6014-2
Éditeur:
Association for Computing Machinery (ACM)
DOI:
10.5281/zenodo.2710863
Auteurs:
Claudio Lucchese, Franco Maria Nardini, Raffaele Perego, Salvatore Orlando, Salvatore Trani
Publié dans:
The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval - SIGIR '18, 2018, Page(s) 155-164, ISBN 9781-450356572
Éditeur:
Association for Computing Machinery (ACM )
DOI:
10.1145/3209978.3210048
Auteurs:
Bacco, Manlio; Catena, Matteo; de Cola, Tomaso; Gotta, Alberto; Tonellotto, Nicola
Publié dans:
Numéro 2, 2018, ISBN 978-1-5386-4727-1
Éditeur:
IEEE
DOI:
10.5281/zenodo.2705727
Auteurs:
Verbert, Katrien; Gutiérrez, Francisco; Htun, Nyi-Nyi
Publié dans:
Numéro 7, 2018, Page(s) 339-345, ISBN 978-1-4503-5941-2
Éditeur:
ACM Transactions on Information Systems
DOI:
10.5281/zenodo.3267211
Auteurs:
Matteo Catena, Nicola Tonellotto
Publié dans:
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019, Page(s) 937-940, ISBN 9781450361729
Éditeur:
ACM
DOI:
10.1145/3331184.3331297
Auteurs:
Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, Ophir Frieder
Publié dans:
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, Page(s) 1573-1576, ISBN 9781450380164
Éditeur:
ACM
DOI:
10.1145/3397271.3401262
Auteurs:
Giulio Ermanno Pibiri, Rossano Venturini
Publié dans:
Software: Practice and Experience, 2020, ISSN 0038-0644
Éditeur:
John Wiley & Sons Inc.
DOI:
10.1002/spe.2918
Auteurs:
Giulio Ermanno Pibiri, Rossano Venturini
Publié dans:
IEEE Transactions on Knowledge and Data Engineering, 2019, Page(s) 1-1, ISSN 1041-4347
Éditeur:
Institute of Electrical and Electronics Engineers
DOI:
10.1109/tkde.2019.2911288
Auteurs:
Giulio Ermanno Pibiri, Raffaele Perego, Rossano Venturini
Publié dans:
IEEE Transactions on Knowledge and Data Engineering, 2020, Page(s) 1-1, ISSN 1041-4347
Éditeur:
Institute of Electrical and Electronics Engineers
DOI:
10.1109/tkde.2020.2966609
Auteurs:
Giulio Ermanno Pibiri, Rossano Venturini
Publié dans:
ACM Computing Surveys, Numéro 53/6, 2021, Page(s) 1-36, ISSN 0360-0300
Éditeur:
Association for Computing Machinary, Inc.
DOI:
10.1145/3415148
Auteurs:
Lucchese, Claudio; Nardini, Franco Maria; Orlando, Salvatore; Perego, Raffaele; Silvestri, Fabrizio; Trani, Salvatore
Publié dans:
ACM Transactions on Intelligent Systems and Technology (TIST), Numéro 8, 2018, ISSN 2157-6904
Éditeur:
Association for Computing Machinery (ACM)
DOI:
10.5281/zenodo.2668361
Auteurs:
Francesco Lettich, Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto, Rossano Venturini
Publié dans:
IEEE Transactions on Parallel and Distributed Systems, 2018, Page(s) 1-1, ISSN 1045-9219
Éditeur:
Institute of Electrical and Electronics Engineers
DOI:
10.1109/tpds.2018.2860982
Auteurs:
Gutiérrez, Francisco; Verbert, Katrien; Seipp, Karsten; Ochoa, Xavier
Publié dans:
Journal of Computer Languages, Numéro 19, 2018, ISSN 2590-1184
Éditeur:
ELSEVIER
DOI:
10.5281/zenodo.3258001
Auteurs:
Ermanno Pibiri, Giulio; Venturini, Rossano
Publié dans:
ACM Transactions on Information Systems, Numéro 25, 2019, ISSN 1046-8188
Éditeur:
Association for Computing Machinary, Inc.
DOI:
10.5281/zenodo.3257995
Auteurs:
Gutiérrez, Francisco; Htun, Nyi-Nyi; Schlenz, Florian; Kasimati, Aikaterini; Verbert, Katrien
Publié dans:
Computers and Electronics in Agriculture, Numéro 15, 2019, ISSN 0168-1699
Éditeur:
Elsevier BV
DOI:
10.5281/zenodo.3267196
Auteurs:
Tonellotto, Nicola; Macdonald, Craig; Ounis, Iadh
Publié dans:
Foundations and Trends® in Information Retrieval, Numéro 37, 2018, Page(s) 319-500, ISSN 1554-0677
Éditeur:
NOW
DOI:
10.5281/zenodo.3268359
Auteurs:
Lorenzo Beretta, Franco Maria Nardini, Roberto Trani, Rossano Venturini
Publié dans:
String Processing and Information Retrieval - 26th International Symposium, SPIRE 2019, Segovia, Spain, October 7–9, 2019, Proceedings, Numéro 11811, 2019, Page(s) 267-273, ISBN 978-3-030-32685-2
Éditeur:
Springer International Publishing
DOI:
10.1007/978-3-030-32686-9_19
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