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I-BiDaaS

Industrial-Driven Big Data as a Self-Service Solution.

Consortium

12 members

Start Year

2018-2020

Scope

European

Funding Framework

Horizon 2020 – H2020-ICT-2017-1

Budget

1 472 000,00€

Duration

36 months

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 780787. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the Directorate-General Communications Networks, Content and Technology, Data, Administration and Finance. Neither the European Union nor the granting authority can be held responsible for them.

About I-BiDaaS

I-BiDaaS – Industrial Big-Data-as-a-Self-Service is a Horizon 2020 research project that develops an integrated platform enabling enterprises to easily access and use advanced big data analytics tools. The project aims to simplify the adoption of big data technologies by providing a self-service environment for capturing, processing, analysing, and visualising large and heterogeneous datasets from multiple sources. Through innovative technologies combining batch and real-time data processing, predictive analytics, and intuitive visualisation tools, I-BiDaaS helps organisations extract actionable insights from complex data streams and improve decision-making processes.

The project focuses on:

Developing a full-stack platform that allows enterprises to analyse and process big data without requiring advanced technical expertise.

Combining distributed processing, predictive analytics, and real-time event processing to support complex data analysis and decision-making.

Applying big data analytics to real industrial challenges in sectors such as manufacturing, finance, and telecommunications.

Testing the platform through industrial experiments using large real-world datasets to validate performance and usability.

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I-BiDaaS Key Objectives

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Identify Industry Challenges and Use Cases

Define the key challenges faced by data providers and translate them into real-world application scenarios.

Develop Data Processing and Integration Frameworks

Establish mechanisms to manage and process streaming data from multiple data providers.

Analyse the State of the Art

Review and synthesise existing technologies and approaches to position the project within the current research and innovation landscape.

Define Experimental Test Cases

Design and implement test scenarios to evaluate system performance under real operational conditions.

Design the System Architecture

Develop a three-layer technical architecture supporting data processing, analytics, and system integration.

Validate System Performance and Functionality

Apply verification and validation methods to ensure the reliability, scalability, and effectiveness of the proposed solutions.

Role of EPBS in I-BiDaaS

Research and Innovation in Data-Driven Decision Making

Contribute expertise in digital transformation, innovation management, and the business applications of big data technologies.

Development of Business Use Cases

Support the design and analysis of industrial use cases demonstrating how big data analytics can improve operational and strategic decisions.

Evaluation of Platform Impact and Adoption

Analyse how the I-BiDaaS platform can support organisations in adopting data-driven practices and improving competitiveness.

Dissemination and Knowledge Transfer

Contribute to publications, dissemination activities, and stakeholder engagement to promote project outcomes across academia and industry.

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GIORGOS DEMETRIOU

Associate Dean of Research and Innovation

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Senior Research Development and Implementation Expert

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Principal Research Development and Implementation Expert

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Principal Research Development and Implementation Expert

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Languages - English, Spanish, French, Croatian

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Research and Digital Learning Implementation Expert

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sofia hamika

Research and Digital Learning Implementation Expert

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chrysostomos dimitriou

Research and Digital Learning Implementation Expert

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Research Development and Implementation Expert

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Assistant Professor in Smart Health and Social Innovation

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Research and Digital Learning Implementation Expert

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