SERMAS: Magnetic Resonance Image Processing
UNICAL: Urban computing
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.
PNSC: Deep Learning
recognition and understanding of time-series data.
The objective of image recognition use case is to create a model able to recognise Varroa mites in images made by beekeepers. This model is going to be a part of a system supporting beekeepers in choosing type of treatment.
Time series analysis
Time series use case focus on analysis of data from electric vehicles and factories. Each of them can produce a massive amount of data from number of parameters. Such data, can contain information about potential issues and untypical situations, but due to high amount, they cannot be analysed manually by experts. Thus, unsupervised anomaly detection approaches must be used.
Computational Fluid Dynamics
PSNC also develops a model, which should support or replace computationally demanding CFD simulations with Deep Learning approaches. Such model could be use for faster prototyping.
INTEGRIS: Big Data Analytics
Nowadays Big Data applications are disrupting the market of the data processing industry in multiple areas/sectors, including Broadcasting, Social Media and Telecom. Most of the big data systems currently relies on the Hadoop ecosystem, which empowered the development of numerous powerful paradigms and tools such as HDFS, MapReduce, Spark, etc. Inevitably, those tools still have lacks and limitations, mainly due to the increasing amount of data involved and its distributed nature, such as frequent low performance scenarios, issue with small files, little to no interaction processing.
The proposed use case is based on the analysis of data from Telco sector. The system gathers data from the national cellular network, consisting of more than 120.000 cells. Starting from a data set on HDFS, containing information about mobile users’ distribution on the Italian territory, the core of the application allows quantitative analysis by aggregation and filtering functions mainly following a Map and Reduce model. The Hadoop tools are used in the use case, but the limitations abovementioned often occur. The aim of the ASPIDE project is to overcome these boundaries with the definition of new tools, programming paradigms, and methodologies.