Program

Project

Contribution

H 2020

open MOS [12]

Contributes an energy consumption tool that uses real-time Big Data analysis to perform machine learning in order to assess and predict the energy consumption of a production line.

H 2020

SAFIRE [13]

Contributes a cloud-based situational analysis solution for factories, providing real-time reconfiguration services, including big data analytic capabilities that meet real-time requirements so that dynamic run-time reconfiguration decisions are made during production time, rather than pre-planned at production planning phase.

H 2020

TOREADOR

[14]

Contributes a model-based Big Data analysis-as-a-service (MBDAaaS) approach, providing models of the entire Big Data analysis process, and of its artefacts, to be easily tailored to domain-specific customer requirements.

FP7

Lean Big Data

[15]

Contributes a Big Data management system, considering a novel transactional NoSQL key-value data store, a distributed complex event processing (CEP) system, and a distributed SQL query engine, to improve the response time for unified analysis over multiple sources and large amounts of data, avoiding the inefficiencies and delays introduced by existing

extract-transfer-load approaches.

H 2020

CADENT [16]

Contributes

・ an examination of how big data is successfully exploited

・ an identification and categorization of primary decisions needed from Big Data intelligence

・ analysis in varying industries

Moreover, explores through a holistic approach the human, technological, managerial, and relational aspects that contribute to successful data-driven decisions.

H 2020

Jam [17]

Contributes prevention and constant analysis of real-time data approaches through a machine-learning algorithm, allowing companies to increase, improve and exploit knowledge.

H 2020

GDC [18]

Contributes an introduction in predictive analysis and “prescriptive” analysis, not only based on processing, but also

human-centred; as well as a unique, new business model supporting customers in understanding the past unstructured data to predict future activities in customers, investments and business development/growth perspective