Luca Venturini

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Advances in genome sequencing and assembly technologies are generating many high-quality genome sequences, but assemblies of large, repeat-rich polyploid genomes, such as that of bread wheat, remain fragmented and incomplete. We have generated a new wheat whole-genome shotgun sequence assembly using a combination of optimized data types and an assembly(More)
Sensing the perception of citizens on urban security is a key point in Smart City management. To address non-emergency issues municipalities commonly acquire citizens' reports and then analyze them offline to perform targeted actions. However, since non-emergency data potentially scale towards Big Data there is a need for open standards and technologies to(More)
Nowadays, large volumes of data and measurements are being continuously generated by computer and telecommunication networks, but such volumes make it difficult to extract meaningful knowledge from them. This paper presents SaFe-NeC, an innovative methodology for analyzing network traffic by exploiting data mining techniques, i.e. clustering and(More)
Understanding the behavior of a network from a large scale traffic dataset is a challenging problem. Big data frameworks offer scalable algorithms to extract information from raw data, but often require a sophisticated fine-tuning and a detailed knowledge of machine learning algorithms. To streamline this process, we propose self-learning insightful network(More)
In this work, we attempt an exploratory analysis of spatio-temporal patterns of crime in San Francisco. We apply spectral analysis to the temporal evolution of all categories of crime, finding that many have a weekly or monthly periodicity, along with other components. We show that spatial distribution has weekly patterns as well. These results can improve(More)
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