Getting value and intelligence from data across Big Data, Analytics & Advanced Parallel Computing
Our objective is the technological development of Big Data and Advanced Parallel Computation for its application in real scenarios. Nowadays our society has focused on the data that is involved in our daily lives through mobile phones, homes, cities, cars... We aim to contribute in a safe and efficient way to a data-driven society, optimizing resource management and facilitating access to them, and creating and adapting new tools able to use the parallel computing paradigms, especially in data analysis.
From the perspective of one of the world's largest IT consulting and leader in digital transformation, we are guided by technological ethics to prevent the misuse of data-driven applications and ensure data privacy and auditability.
Our main vision is to carry out research on engineering and analysis of large and complex data, with semantic technologies and state-of-the-art technologies such as Natural Language Processing, blockchain, parallelizing data processing to ease the execution of experiments with transparent access to Cloud and HPC; and researching on future technologies such as New Quantum Computing and Future Neuromorphic Computing solutions.
Our Unit provides a unique point of contact with the R&D carried out in the Big Data and APC fields in Europe through our participation in the many projects funded under the umbrella of the BDV PPP, engagement with the Big Data Value Association (BDVA), and the European AI on-demand platform (AI4EU).
- Big Data: Covering most of the technical aspects of the Data Value Chain to get value from the data.
- Data Analytics: Creating data processing pipelines (real-time and batch), including the use of Machine Learning to enable multiple applications to real-world scenarios. Especial focus on Natural Language Processing pipelines (i.e. sentiment analysis, opinion mining).
- Semantics: Application of Knowledge Graphs and Linked Data as enablers for data analytics.
- APC: Enabling the use of future exascale systems for smart orchestration of applications looking at several aspects (profiles, data movement, resources status, etc.), and the development of the parallel Complex Event Processing engine for real-time data analysis, also available for low-power computing devices.