Big Data to Enable Global Disruption of the Grapevine-powered Industries

Projektinformationen

BigDataGrapes

ID Finanzhilfevereinbarung: 780751

DOI

10.3030/780751

Projekt abgeschlossen

EK-Unterschriftsdatum 20 Oktober 2017

Startdatum 1 Januar 2018

Enddatum 31 Dezember 2020

Finanziert unter

• INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)

Gesamtkosten

€ 4 441 500,00

EU-Beitrag

€ 4 441 500,00

Koordiniert durch

AGROKNOW IKE
Greece

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

Documents, reports (11)

Piloting Plan

A report documenting the plan for the development of the pilots and the methodology and materials for the pilot trials.

Scalability and Robustness Experimental Methodology

A document describing the methodology, the performance criteria, the protocols for the experiments, and the technical infrastructure requirements.

BigDataGrapes Software Stack Design

A report of the overall design of the BigDataGrapes software stack, including functional specifications, communication standards, external tools and underlying frameworks.

Use Cases & Technical Requirements Specification

A report presenting in detail the BigDataGrapes use cases and their interpretation with respect to technical and infrastructural requirements.

Data Management Plan & Support Pack

This 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 Datasets

A document with the experimental results after testing the BigDataGrapes components over the two foreseen dataset projections (2020 and 2030).

Evaluation Report and KPI Assessment

A 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 Report

A 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 Datasets

A 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 Methodology

A 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 Report

A report documenting the various dissemination, awareness and outreach activities and results for the respective period.

Demonstrators, pilots, prototypes (12)

Integrated Software Stack and APIs

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 Components

The software components for carrying out data ingestion in the BigDataGrapes ecosystem, along with their documentation.

Methods and Tools for Distributed Inference

Software 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 Algorithms

Software 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 Components

A tool for creating, maintaining and linking semantic data, customized to serve the needs of the relevant grapevine-powered industries.

Uncertainty-aware Visual Analytics Components

Software 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 Enrichment

A 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 Components

Software 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 Datasets

Software 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 Layer

Software 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 Components

A 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 Systems

Decision 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.

Open Research Data Pilot (2)

Integration and Operation with real-life Software Systems

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 Practices

A 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.

Websites, patent fillings, videos etc. (1)

Website and Social Media Presence

Setup and maintenance plan for the project’s web site, social media accounts, blogs, forums, etc.

Veröffentlichungen

Other (2)

BigDataGrapes D3.1 - Data Modelling and Linking Components

Autoren: Alexiev, Vladimir
Veröffentlicht in: Ausgabe 1, 2018
Herausgeber: European Commission
DOI: 10.5281/zenodo.1482757

BigDataGrapes D3.2 - Data Ingestion & Integration Components

Autoren: Zervas, Panagiotis; Konstantinidis, Sotiris; Koukourikos, Antonis
Veröffentlicht in: Ausgabe 1, 2018
Herausgeber: European Commission
DOI: 10.5281/zenodo.1482751

Conference proceedings (15)

Topic Propagation in Conversational Search

Autoren: Ida Mele, Cristina Ioana Muntean, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Ophir Frieder
Veröffentlicht in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, Seite(n) 2057-2060, ISBN 9781450380164
Herausgeber: ACM
DOI: 10.1145/3397271.3401268

An Empirical Comparison of Classification Algorithms for Imbalanced Credit Scoring Datasets

Autoren: Leopoldo Soares de Melo Junior, Franco Maria Nardini, Chiara Renso, Jose Antonio Fernandes de Macedo
Veröffentlicht in: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019, Seite(n) 747-754, ISBN 978-1-7281-4550-1
Herausgeber: IEEE
DOI: 10.1109/icmla.2019.00133

KNORA-IU: Improving the Dynamic Selection Prediction in Imbalanced Credit Scoring Problems

Autoren: Leopoldo Melo, Franco Maria Nardini, Chiara Renso, Jose Antonio Macedo
Veröffentlicht in: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019, Seite(n) 424-431, ISBN 978-1-7281-3798-8
Herausgeber: IEEE
DOI: 10.1109/ictai.2019.00066

Training Curricula for Open Domain Answer Re-Ranking

Autoren: Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, Ophir Frieder
Veröffentlicht in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, Seite(n) 529-538, ISBN 9781450380164
Herausgeber: ACM
DOI: 10.1145/3397271.3401094

Fast Approximate Filtering of Search Results Sorted by Attribute

Autoren: Franco Maria Nardini, Roberto Trani, Rossano Venturini
Veröffentlicht in: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019, Seite(n) 815-824, ISBN 9781450361729
Herausgeber: ACM
DOI: 10.1145/3331184.3331227

Efficient Document Re-Ranking for Transformers by Precomputing Term Representations

Autoren: Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, Ophir Frieder
Veröffentlicht in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, Seite(n) 49-58, ISBN 9781450380164
Herausgeber: ACM
DOI: 10.1145/3397271.3401093

Efficient and Effective Query Auto-Completion

Autoren: Simon Gog, Giulio Ermanno Pibiri, Rossano Venturini
Veröffentlicht in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, Seite(n) 2271-2280, ISBN 9781450380164
Herausgeber: ACM
DOI: 10.1145/3397271.3401432

Query-level Early Exit for Additive Learning-to-Rank Ensembles

Autoren: Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Salvatore Trani
Veröffentlicht in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, Seite(n) 2033-2036, ISBN 9781450380164
Herausgeber: ACM
DOI: 10.1145/3397271.3401256

Efficient and Effective Query Expansion for Web Search

Autoren: Lucchese, Claudio; Nardini, Franco Maria; Perego, Raffaele; Trani, Roberto; Venturini, Rossano
Veröffentlicht in: Ausgabe 24, 2018, ISBN 978-1-4503-6014-2
Herausgeber: Association for Computing Machinery (ACM)
DOI: 10.5281/zenodo.2668248

Efficient Energy Management in Distributed Web Search

Autoren: Catena, Matteo; Frieder, Ophir; Tonellotto, Nicola
Veröffentlicht in: Ausgabe 25, 2018, ISBN 978-1-4503-6014-2
Herausgeber: Association for Computing Machinery (ACM)
DOI: 10.5281/zenodo.2710863

Selective Gradient Boosting for Effective Learning to Rank

Autoren: Claudio Lucchese, Franco Maria Nardini, Raffaele Perego, Salvatore Orlando, Salvatore Trani
Veröffentlicht in: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval - SIGIR '18, 2018, Seite(n) 155-164, ISBN 9781-450356572
Herausgeber: Association for Computing Machinery (ACM )
DOI: 10.1145/3209978.3210048

Performance Analysis of WebRTC-based Video Streaming over Power Constrained Platforms

Autoren: Bacco, Manlio; Catena, Matteo; de Cola, Tomaso; Gotta, Alberto; Tonellotto, Nicola
Veröffentlicht in: Ausgabe 2, 2018, ISBN 978-1-5386-4727-1
Herausgeber: IEEE
DOI: 10.5281/zenodo.2705727

PHARA: an augmented reality grocery store assistant

Autoren: Verbert, Katrien; Gutiérrez, Francisco; Htun, Nyi-Nyi
Veröffentlicht in: Ausgabe 7, 2018, Seite(n) 339-345, ISBN 978-1-4503-5941-2
Herausgeber: ACM Transactions on Information Systems
DOI: 10.5281/zenodo.3267211

Multiple Query Processing via Logic Function Factoring

Autoren: Matteo Catena, Nicola Tonellotto
Veröffentlicht in: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019, Seite(n) 937-940, ISBN 9781450361729
Herausgeber: ACM
DOI: 10.1145/3331184.3331297

Expansion via Prediction of Importance with Contextualization

Autoren: Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, Ophir Frieder
Veröffentlicht in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, Seite(n) 1573-1576, ISBN 9781450380164
Herausgeber: ACM
DOI: 10.1145/3397271.3401262

Peer reviewed articles (10)

Practical trade‐offs for the prefix‐sum problem

Autoren: Giulio Ermanno Pibiri, Rossano Venturini
Veröffentlicht in: Software: Practice and Experience, 2020, ISSN 0038-0644
Herausgeber: John Wiley & Sons Inc.
DOI: 10.1002/spe.2918

On Optimally Partitioning Variable-Byte Codes

Autoren: Giulio Ermanno Pibiri, Rossano Venturini
Veröffentlicht in: IEEE Transactions on Knowledge and Data Engineering, 2019, Seite(n) 1-1, ISSN 1041-4347
Herausgeber: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tkde.2019.2911288

Compressed Indexes for Fast Search of Semantic Data

Autoren: Giulio Ermanno Pibiri, Raffaele Perego, Rossano Venturini
Veröffentlicht in: IEEE Transactions on Knowledge and Data Engineering, 2020, Seite(n) 1-1, ISSN 1041-4347
Herausgeber: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tkde.2020.2966609

Techniques for Inverted Index Compression

Autoren: Giulio Ermanno Pibiri, Rossano Venturini
Veröffentlicht in: ACM Computing Surveys, Ausgabe 53/6, 2021, Seite(n) 1-36, ISSN 0360-0300
Herausgeber: Association for Computing Machinary, Inc.
DOI: 10.1145/3415148

X-CLEaVER: Learning Ranking Ensembles by Growing and Pruning Trees

Autoren: Lucchese, Claudio; Nardini, Franco Maria; Orlando, Salvatore; Perego, Raffaele; Silvestri, Fabrizio; Trani, Salvatore
Veröffentlicht in: ACM Transactions on Intelligent Systems and Technology (TIST), Ausgabe 8, 2018, ISSN 2157-6904
Herausgeber: Association for Computing Machinery (ACM)
DOI: 10.5281/zenodo.2668361

Parallel Traversal of Large Ensembles of Decision Trees

Autoren: Francesco Lettich, Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto, Rossano Venturini
Veröffentlicht in: IEEE Transactions on Parallel and Distributed Systems, 2018, Seite(n) 1-1, ISSN 1045-9219
Herausgeber: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tpds.2018.2860982

Towards a visual guide for communicating uncertainty in Visual Analytics☆

Autoren: Gutiérrez, Francisco; Verbert, Katrien; Seipp, Karsten; Ochoa, Xavier
Veröffentlicht in: Journal of Computer Languages, Ausgabe 19, 2018, ISSN 2590-1184
Herausgeber: ELSEVIER
DOI: 10.5281/zenodo.3258001

Handling Massive N-Gram Datasets Efficiently

Autoren: Ermanno Pibiri, Giulio; Venturini, Rossano
Veröffentlicht in: ACM Transactions on Information Systems, Ausgabe 25, 2019, ISSN 1046-8188
Herausgeber: Association for Computing Machinary, Inc.
DOI: 10.5281/zenodo.3257995

A Review of Visualisations in Agricultural Decision Support Systems: an HCI Perspective

Autoren: Gutiérrez, Francisco; Htun, Nyi-Nyi; Schlenz, Florian; Kasimati, Aikaterini; Verbert, Katrien
Veröffentlicht in: Computers and Electronics in Agriculture, Ausgabe 15, 2019, ISSN 0168-1699
Herausgeber: Elsevier BV
DOI: 10.5281/zenodo.3267196

Efficient Query Processing for Scalable Web Search

Autoren: Tonellotto, Nicola; Macdonald, Craig; Ounis, Iadh
Veröffentlicht in: Foundations and Trends® in Information Retrieval, Ausgabe 37, 2018, Seite(n) 319-500, ISSN 1554-0677
Herausgeber: NOW
DOI: 10.5281/zenodo.3268359

Book chapters (1)

An Optimal Algorithm to Find Champions of Tournament Graphs

Autoren: Lorenzo Beretta, Franco Maria Nardini, Roberto Trani, Rossano Venturini
Veröffentlicht in: String Processing and Information Retrieval - 26th International Symposium, SPIRE 2019, Segovia, Spain, October 7–9, 2019, Proceedings, Ausgabe 11811, 2019, Seite(n) 267-273, ISBN 978-3-030-32685-2
Herausgeber: Springer International Publishing
DOI: 10.1007/978-3-030-32686-9_19