Use cases

 

The new technological advances in Magnetic Resonance Imaging (MRI) allow for the collection of vast amounts of imaging data in shorter acquisition times. However, raw imaging data rarely provides the metrics of interest for research studies or even for clinical use. This is especially true for Diffusion-Weighted Imaging (DWI) of the brain. In this modality, large numbers of volumetric images must be processed to provide information about the structural connectivity of the brain and metrics associated with white-matter integrity. This image processing is computationally expensive, taking several hours for only one subject in a standard computer. However, most of the processing steps are highly parallelisable, not only per-subject but also in a per-volumetric-pixel fashion. Therefore, this kind of processing will significantly benefit from HPC and Exascale infrastructures, making it possible to decrease the processing times dramatically. This will open the possibility for more efficient and productive research in neuroimaging, enabling the use of more complex processing algorithms currently limited by computational power. Moreover, faster processing of MRI images will lead to a more rapid diagnosis of neural disorders.

 

 

Urban computing is the process of acquisition, integration, and analysis of big and heterogeneous urban data to tackle the major issues that cities face today, including air pollution, energy consumption, traffic flows, human mobility, environmental preservation, commercial activities and savings in public spending.

Data gathered from social media, such as posts from Twitter and Facebook or photos from Instagram and Flickr, are frequently geotagged. Such data can be analyzed for extracting valuable information about user mobility, including the most frequent sets of places visited by users and the most common trajectories followed by users.

The analysis of user trajectories through RoIs (Regions of interest) is highly valuable in many scenarios:

• tourism agencies and municipalities can discover the most visited touristic places and the time of year when such places are visited;
• transport operators can discover the places and routes where is it more likely to serve passengers and crowed areas where more transport facilities need to be allocated.
• business analysts can analyze the flow of the users – including a departure and arrival points, path, transportations, waiting time – to suggest the best place where to open a business activity.