PhD in Control and Computer Engineering
PhD Student: Andrea Avignone
PhD Student: Andrea Avignone
Context of the research activity
In the urban ecosystem a multitude of strongly intertwined systems coexists, varying from people sociality to transport systems. While each of these urban facets already represents in itself a complex system, their interconnection is definitively a challenging scenario. Urban Data Science entails the acquisition, integration, and analysis of big and heterogeneous data collections generated by a diversity of sources in urban spaces to profile the different facets and issues of the urban environment. It addresses the development of competences and technical infrastructure required to study and address urban challenges, from a data-driven perspective. It can unearth a rich spectrum of knowledge valuable for urban data creation, integration, enrichment, analysis, and exploration. Thus, urban data science plays a key role in achieving a smart and sustainable city. However, data analytics on urban data collections is still a daunting task, because they are generally too big and heterogeneous to be processed through machine learning techniques currently available. Thus, today’s urban data give rise to a lot of challenges that constitute a new inter-disciplinary field of data science research.
The PhD student will work on the study, design and development of proper data models and novel solutions and for the acquisition, integration, storage, management and analysis of big volumes of heterogeneous urban data. The research activity involves multidisciplinary knowledge and skills including database, machine learning techniques, and advanced programming. Different case studies in urban scenarios such as urban mobility, citizen-centric contexts, and healthy city will be considered to conduct the research activity. The objectives of the research activity consist in identifying the peculiar characteristics and challenges of each considered application domain and devise novel solutions for the management and analysis of urban data for each domain. More urban scenarios will be considered with the aim of exploring the different facets of urban data and evaluating how the proposed solutions perform on different data collections. More in detail, the following challenges will be addressed during the PhD:
- Adoption of proper data models. The storage of heterogeneous urban data collections requires the use of alternative data representations to the relational model such as NoSQL databases (e.g., MongoDB), also able to manage geo-referenced data.
- Design and development of algorithms for big data analytics. Huge volume of data demands the definition of novel data analytics strategies also exploiting recent analysis paradigms and cloud based platforms. Moreover, urban data is usually charaterized by spatio-temporal coordinates describing when and where data has been acquired, which entails the design of suitable data analytics methods.
Further information about the PhD program at Politecnico can be found here
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