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Valerio Antonini

Student

Project Title

Graph Analytics for Spatio-Temporal data

Project Description

Large amounts of spatio-temporal data are increasingly collected and analysed in several domains, including neuroscience, epidemiology, transportation, social sciences, health, climate science and Earth sciences. Spatio-temporal data differ from relational data since the spatial and temporal attribute becomes a crucial feature which must be properly exploited, making the task even more demanding. Due to the presence of these two coordinates, the observations are not independent or identically distributed. The samples can be related or linked in some spatial regions of specific temporal moments. An additional challenge is the dynamic state of the observations: they can change properties or class depending on time and space. The traditional machine learning models are not properly suitable for mining patterns from these relations, leading to poor performances and misleading interpretation. Thus, the emerging field of spatio-temporal data mining (STDM) proposes new methods for addressing the analysis of event-based data. In order to take full advantage of the relations emerging in time and space, the most accurate way to process these data is by designing them as a graph. A graph is a network of nodes and edges which can have different weights defining the importance and type of the relation. The first challenge is to find the most accurate construction of the graph to represent patterns among data. Neighbourhood graph construction is crucial for the quality of the analysis in graph-based methods. This task can be addressed according to several techniques, each of them having own strengths and weaknesses. In the following stages other challenges can be community detection, nodes centrality, clustering, anomaly detection, frequent pattern mining, relationship mining, change detection, predictive learning and links prediction. The research seeks to find new efficient methods to collect, represent and process spatio temporal data by using the combination of graph-based techniques with unsupervised, supervised and semi-supervised machine learning.