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Expertise & Skills

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  1. Attività

TrustwOrthy model-awaRE Analytics Data platfORm

Progetto
The TOREADOR project is aimed at overcoming some major hurdles that until now have prevented many European companies from reaping the full benefits of Big Data analytics (BDA). Companies and organisations in Europe have become aware of the potential competitive advantage they could get by timely and accurate Big Data analytics, but lack the IT expertise and budget to fully exploit BDA. To overcome this hurdle, TOREADOR takes a model-based BDA-as-a-service (MBDAaaS) approach, providing models of the entire Big Data analysis process and of its artefacts. TOREADOR open, suitable-for-standardisation models will support substantial automation and commoditisation of Big Data analytics, while enabling it to be easily tailored to domain-specific customer requirements. Besides models for representing BDA, TOREADOR will deliver an architectural framework and a set of components for model-driven set-up and management of Big Data analytics processes. Once TOREADOR MBDAaaS will become widespread, price competition on Big Data services will ensue, driving costs of Big Data analytics well within reach of EU organizations (including SMEs) that do not have either in-house Big Data expertise or budget for expensive data consultancy. Activities supported and automatised by TOREADOR will include (i) planning Big Data sources preparation (ii) negotiating machine-readable Service Level Agreements for BDA detailing privacy, timing, and accuracy needs (iii) choosing data management and algorithm parallelisation strategies (iv) ensuring auditing and assessment of legal compliance (for example, to privacy regulations) of BDA enactment. TOREADOR framework will address automatically all major problems of on-demand data preparation, including handling Big Data opacity, diversity, security, and privacy compliance, and will support abstract modelling of the BDA life cycle from distributed data acquisition/storage to the design and parallel deployment of analytics and presentation of results.
  • Dati Generali
  • Pubblicazioni

Dati Generali

Partecipanti

DAMIANI ERNESTO   Responsabile scientifico  

Dipartimenti coinvolti

Dipartimento di Informatica Giovanni Degli Antoni   Principale  

Tipo

H20_RIA - Horizon 2020_Research & Innovation Action/Innovation Action

Finanziatore

EUROPEAN COMMISSION
Organizzazione Esterna Ente Finanziatore

Capofila

CONSORZIO INTERUNIVERSITARIO NAZIONALE PER L'INFORMATICA - CINI

Periodo di attività

Febbraio 10, 2017 - Dicembre 31, 2018

Durata progetto

22 mesi

Pubblicazioni

Pubblicazioni (8)

Fast ML-based Next-Word Prediction for Hybrid Languages 
INTERNET OF THINGS
ELSEVIER
2024
Articolo
Open Access
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Towards AI-Powered Multiple Cloud Management 
IEEE INTERNET COMPUTING
INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS (IEEE) COMPUTER SOCIETY
2019
Articolo
Reserved Access
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Big Data Semantics 
JOURNAL ON DATA SEMANTICS
SPRINGER
2018
Articolo
Reserved Access
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Few are as Good as Many: An Ontology-Based Tweet Spam Detection Approach 
IEEE ACCESS
INSTITUTE OF ELECTRICAL, ELECTRONICS ENGINEERS (IEEE)
2018
Articolo
Open Access
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K-Means Clustering in Dual Space for Unsupervised Feature Partitioning in Multi-view Learning 
IEEE
2019
Contributo in Atti di convegno
Partially Open Access
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Semantic Support for Model Based Big Data Analytics-as-a-Service (MBDAaaS) 
ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING
SPRINGER
2019
Contributo in Atti di convegno
Open Access
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A Fast and Incremental Development Life Cycle for Data Analytics as a Service 
IEEE
2018
Contributo in Atti di convegno
Reserved Access
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Big data assurance evaluation: An sla-based approach 
IEEE
2018
Contributo in Atti di convegno
Reserved Access
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Realizzato con VIVO | Progettato da Cineca | 25.11.5.0