Welcome to Intelligent Data-Intensive Systems

AI for improving productivity

Despite the recent spotlight of AI solutions in applications that are mainly computer science related (e.g., computer vision), AI solutions are not as popular in other domains such as Natural Sciences. Utilising AI methods in such domains can bear several advantages. Complex datasets and information present in such domains can be abstracted through learning of representative patterns or simpler latent representations. Furthermore, execution of complex workflows can be optimised for efficient and effective cloud computing through the use of discovered knowledge from AI methods.

AI for data-intensive research

In this day and age where a plethora of data samples is available at ease, data-intensive research, systems and applications are gaining popularity. Discovering knowledge from voluminous datasets either as an outcome of visualisation or statistical analysis and machine learning can bear significant advantages in multiple domains. Furthermore, advancements in "Next-generation data engineering" such as Information Fusion can lead to better performance in discovering representative patterns.

Catalogues & vocabularies

Generalised catalogues and vocabularies result in achieving better communication of research advancements. In addition, they can serve as a channel to promote open science and reproducible experiments, methods, evaluations and results. Furthermore, they can be utilised in order to facilitate for optimisation of systems and method development.