OpenDataHub: an open dataset management system

This thesis presents a cloud-based software platform for sharing publicly available scientific datasets. The proposed platform leverages the potential of NoSQL databases and asynchronous IO technologies, such as Node.JS, in order to achieve high performances and flexible solutions. This solution will serve two main groups of users. The dataset providers, which are the researchers responsible for sharing and maintaining datasets, and the dataset users, that are those who desire to access the public data. To the former are given tools to easily publish and maintain large volumes of data, whereas the later are given tools to enable the preview and creation of subsets of the original data through the introduction of filter and aggregation operations. The choice of NoSQL over more traditional RDDMS emerged from and extended benchmark between relational databases (MySQL) and NoSQL (MongoDB) that is also presented in this thesis. The obtained results come to confirm the theoretical guarantees that NoSQL databases are more suitable for the kind of data that our system users will be handling, i. e., non-homogeneous data structures that can grow really fast. It is envisioned that a platform like this can lead the way to a new era of scientific data sharing where researchers are able to easily share and access all kinds of datasets, and even in more advanced scenarios be presented with recommended datasets and already existing research results on top of those recommendations.

Examining the cause of high inbreeding depression: analysis of whole-genome sequence data in 28 selfed progeny of Eucalyptus grandis.

2016

L'HBIM semantico per la catalogazione e la fruizione dei Beni Culturali. Il caso studio della Chiesa di Santa Maria delle Vergini (MC) e la piattaforma Inception.

Questa tesi di laurea si pone l’obiettivo di investigare alcune delle nuove frontiere offerte dalla crescita sincretica e multidisciplinare dei linguaggi digitali applicati all’architettura e ai beni culturali. Si approfondiranno i concetti teorici fondamentali dell’informazione digitale: il web semantico come ambiente di scambio, i metadata come informazioni sui dati, i LOD (Link Open Data) come standard e fine. Per l’ambito dei beni culturali verranno presentati i temi di ricerca e sviluppo nel campo della catalogazione e fruizione digitali: ontologie, dizionari normalizzati aperti, database (Catalogo Digitale), etc. Per l’ambito edilizio-architettonico verrà introdotto l’Heritage Building Information Modeling (HBIM) semantico come metodologia multidisciplinare focalizzata su rilievo geometrico, modellazione, archiviazione e scambio di tutte le informazioni utili alla conoscenza e conservazione dei beni storici. Il punto d’incontro tra i due mondi è individuato nella possibilità di arricchire le geometrie attraverso la definizione di una semantica (parametri-metadati) relazionata alle informazioni (valori-dati) presenti nei cataloghi digitali, creando di fatto un modello 3D per architetture storiche con funzione di database multidisciplinare. Sarà presentata la piattaforma web-based Inception, sviluppata dall’omonima startup incubata come spinoff dall’Università di Ferrara, che, tra le diverse applicazioni e potenzialità, verrà utilizzata come strumento per la condivisione e fruizione, garantendo la possibilità di interrogare geometrie e metadati in continuità con i principi LOD. Verrà definito un workflow generale (procedure Scan2BIM, modellazione geometrica, definizione script per l’estrazione automatica dei dati dal Catalogo Digitale, associazione dati-geometrie e caricamento in piattaforma) successivamente applicato e adattato alle precise necessità del caso studio: la Chiesa di S. Maria delle Vergini (MC), su commissione dell’ICCD referente al MiBACT.

The FOOT experiment: Trigger and Data Acquisition (TDAQ) development and data analysis

Hadrontherapy employs high-energy beams of charged particles (protons and heavier ions) to treat deep-seated tumours: these particles have a favourable depth-dose distribution in tissue characterized by a low dose in the entrance channel and a sharp maximum (Bragg peak) near the end of their path. In these treatments nuclear interactions have to be considered: beam particles can fragment in the human body releasing a non-zero dose beyond the Bragg peak while fragments of human body nuclei can modify the dose released in healthy tissues. These effects are still in question given the lack of interesting cross sections data. Also space radioprotection can profit by fragmentation cross section measurements: the interest in long-term manned space missions beyond Low Earth Orbit is growing in these years but it has to cope with major health risks due to space radiation. To this end, risk models are under study: however, huge gaps in fragmentation cross sections data are currently present preventing an accurate benchmark of deterministic and Monte Carlo codes. To fill these gaps in data, the FOOT (FragmentatiOn Of Target) experiment was proposed. It is composed by two independent and complementary setups, an Emulsion Cloud Chamber and an electronic setup composed by several subdetectors providing redundant measurements of kinematic properties of fragments produced in nuclear interactions between a beam and a target. FOOT aims to measure double differential cross sections both in angle and kinetic energy which is the most complete information to address existing questions. In this Ph.D. thesis, the development of the Trigger and Data Acquisition system for the FOOT electronic setup and a first analysis of 400 MeV/u 16O beam on Carbon target data acquired in July 2021 at GSI (Darmstadt, Germany) are presented. When possible, a comparison with other available measurements is also reported.

Integration of heterogeneous data sources and automated reasoning in healthcare and domotic IoT systems

In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources.

Late onset spinal cord ischemia after thoracoabdominal aortic aneurysm open repair

The aim of this study is to evaluate if spinal cord ischemia (SCI), especially its late presentation, and can be correlated to the results of intraoperative evoked potential monitoring (IOM). Methods. This study is a physician-initiated, retrospective, single-center, non-randomized study. Data from all patients undergoing a thoracoabdominal aortic aneurysm surgical repair (TAAA SR) between January 2016 and March 2020 IOM was collected and analyzed. Results. During the study period, 261 patients underwent TAAA SR with MEP/SSEPs monitoring [190 males, 73%; median age 65 (57-71)]. Thirty-seven patients suffered from SCI, for an overall rate of 14% (permanent 9%). When stratifying patients according to the SCI onset, 18 patients presented with an early (11 permanent) and 19 with a late SCI (<24h) (11 permanent). Of 261 patients undergoing TAAA SR with IOM, 15 were excluded due to changes in the upper extremity motor evoked potentials. For the remaining 246, the association between SCI and IOM was investigated: only irreversible IOM loss without peripheral changes have been found to be a risk factor for late onset SCI (p=.006). Furthermore, given that no statistical differences were found between the two groups when no IOM changes were recorded (p=.679), this situation cannot reliably rule out any SCI in our cohort. Independent risk factors for late spinal cord ischemia onset found at multivariate analysis were smoking history (p=.008), BMI>28 (p=.048) and TAAA extent II (p=.009). The irreversible MEP change without peripheral showed a trend of significance (p=.052). Conclusions. Evoked potential intraoperative monitoring is an important adjunct during thoracoabdominal aortic open repair to predict and possibly prevent spinal cord ischemia. Irreversible IOM loss without peripheral changes was predictive of late SCI, therefore more attention should be paid to the postoperative management of this subgroup of patients.

Implementazione del Sistema Informativo 3D: sviluppo di un lessico codificato per una funzionale e produttiva comparazione dei dati

Il progetto del dottorato di ricerca ha permesso di estendere il lavoro svolto sul Sistema Informativo 3D del Cantiere della Fontana del Nettuno al fine di definire la struttura concettuale e i relativi contenuti tematici di una piattaforma open source in grado di sviluppare la documentazione di restauro, sia per opere complesse caratterizzate dalla presenza di molti materiali costitutivi, sia per interventi più semplici nel quale preservare memoria e fornire libero accesso ai dati. Il confronto tra il SI del Cantiere della Fontana del Nettuno con le attuali metodologie utilizzate in campo nazionale ed internazionale ha permesso di ampliare i lessici necessari per la documentazione grafica e testuale da effettuare su diverse classi di materiali, creando delle cartelle integrate da combinare in base ai materiali costitutivi delle opere da restaurare. Il lavoro ha permesso la redazione di un lessico specifico per i diversi materiali costitutivi fornendo una banca dati informatizzata di facile consultazione.

Data-driven multivariate flood hazard modeling

The present Dissertation shows how recent statistical analysis tools and open datasets can be exploited to improve modelling accuracy in two distinct yet interconnected domains of flood hazard (FH) assessment. In the first Part, unsupervised artificial neural networks are employed as regional models for sub-daily rainfall extremes. The models aim to learn a robust relation to estimate locally the parameters of Gumbel distributions of extreme rainfall depths for any sub-daily duration (1-24h). The predictions depend on twenty morphoclimatic descriptors. A large study area in north-central Italy is adopted, where 2238 annual maximum series are available. Validation is performed over an independent set of 100 gauges. Our results show that multivariate ANNs may remarkably improve the estimation of percentiles relative to the benchmark approach from the literature, where Gumbel parameters depend on mean annual precipitation. Finally, we show that the very nature of the proposed ANN models makes them suitable for interpolating predicted sub-daily rainfall quantiles across space and time-aggregation intervals. In the second Part, decision trees are used to combine a selected blend of input geomorphic descriptors for predicting FH. Relative to existing DEM-based approaches, this method is innovative, as it relies on the combination of three characteristics: (1) simple multivariate models, (2) a set of exclusively DEM-based descriptors as input, and (3) an existing FH map as reference information. First, the methods are applied to northern Italy, represented with the MERIT DEM (∼90m resolution), and second, to the whole of Italy, represented with the EU-DEM (25m resolution). The results show that multivariate approaches may (a) significantly enhance flood-prone areas delineation relative to a selected univariate one, (b) provide accurate predictions of expected inundation depths, (c) produce encouraging results in extrapolation, (d) complete the information of imperfect reference maps, and (e) conveniently convert binary maps into continuous representation of FH.

Deployment of a data analysis workflow of the ATLAS experiment on HPC systems

LHC experiments produce an enormous amount of data, estimated of the order of a few PetaBytes per year. Data management takes place using the Worldwide LHC Computing Grid (WLCG) grid infrastructure, both for storage and processing operations. However, in recent years, many more resources are available on High Performance Computing (HPC) farms, which generally have many computing nodes with a high number of processors. Large collaborations are working to use these resources in the most efficient way, compatibly with the constraints imposed by computing models (data distributed on the Grid, authentication, software dependencies, etc.). The aim of this thesis project is to develop a software framework that allows users to process a typical data analysis workflow of the ATLAS experiment on HPC systems. The developed analysis framework shall be deployed on the computing resources of the Open Physics Hub project and on the CINECA Marconi100 cluster, in view of the switch-on of the Leonardo supercomputer, foreseen in 2023.

Studio ed integrazione di Grafana per la visualizzazione di flussi di dati generati dalla piattaforma semantica SEPA

Questo elaborato mostra lo sviluppo di un plugin per la visualizzazione in Grafana di eventi provenienti dalla piattaforma semantica SEPA (SPARQL Event Processing Architecture). La principale funzione svolta dal SEPA è quella di notificare in modo asincrono i propri client rispetto al cambiamento dei risultati di una query che interroga il sottostante grafo RDF. La piattaforma trova il suo utilizzo in quei contesti caratterizzati da dati dinamici, eterogenei e non strutturati e viene impiegata principalmente come strumento per abilitare l’interoperabilità in domini come per esempio l’Internet of Things. Nasce quindi l’esigenza di disporre di strumenti per il monitoraggio e la visualizzazione di dati real-time. Grafana risulta in questo caso lo strumento ideale data la sua flessibilità, che affiancata alla sua natura open source, lo rende particolarmente interessante per lo sviluppo della soluzione proposta da VAIMEE, spinoff dell’Università di Bologna, ospitato presso il CesenaLab, luogo dove è stato svolto questo lavoro di tesi.

PIATTAFORMA DI DATA VISUALIZATION GUIDATA DALLA MODELLAZIONE STATISTICA DEI DATI Applicazione a dati sanitari di pazienti oncologici della regione Emila-Romagna

Una gestione, un’analisi e un’interpretazione efficienti dei big data possono cambiare il modello lavorativo, modificare i risultati, aumentare le produzioni, e possono aprire nuove strade per l’assistenza sanitaria moderna. L'obiettivo di questo studio è incentrato sulla costruzione di una dashboard interattiva di un nuovo modello e nuove prestazioni nell’ambito della Sanità territoriale. Lo scopo è quello di fornire al cliente una piattaforma di Data Visualization che mostra risultati utili relativi ai dati sanitari in modo da fornire agli utilizzatori sia informazioni descrittive che statistiche sulla attuale gestione delle cure e delle terapie somministrate. Si propone uno strumento che consente la navigazione dei dati analizzando l’andamento di un set di indicatori di fine vita calcolati a partire da pazienti oncologici della Regione Emilia Romagna in un arco temporale che va dal 2010 ad oggi.

Double generalized linear model for tissue culture proportion data: a Bayesian perspective

Joint generalized linear models and double generalized linear models (DGLMs) were designed to model outcomes for which the variability can be explained using factors and/or covariates. When such factors operate, the usual normal regression models, which inherently exhibit constant variance, will under-represent variation in the data and hence may lead to erroneous inferences. For count and proportion data, such noise factors can generate a so-called overdispersion effect, and the use of binomial and Poisson models underestimates the variability and, consequently, incorrectly indicate significant effects. In this manuscript, we propose a DGLM from a Bayesian perspective, focusing on the case of proportion data, where the overdispersion can be modeled using a random effect that depends on some noise factors. The posterior joint density function was sampled using Monte Carlo Markov Chain algorithms, allowing inferences over the model parameters. An application to a data set on apple tissue culture is presented, for which it is shown that the Bayesian approach is quite feasible, even when limited prior information is available, thereby generating valuable insight for the researcher about its experimental results.

Prevalence of cannabis use in Brazil: Data from the I Brazilian National Alcohol Survey (BNAS)

Aims: To estimate the prevalence of cannabis use in the last 12 months in the Brazilian population and to examine its association with individual and geographic characteristics. Design: Cross-sectional survey with a national probabilistic sample. Participants: 3006 individuals aged 14 to 65 years. Measurements: Questionnaire based on well established instruments, adapted to the Brazilian population. Findings: The 12-month prevalence of cannabis use was 2.1% (95%Cl 1.3-2.9). Male gender, better educational level, unemployment and living in the regions South and Southeast were independently associated with higher 12-month prevalence of cannabis use. Conclusion: While the prevalence of cannabis use in Brazil is lower than in many countries, the profile of those who are more likely to have used it is similar. Educational and prevention policies should be focused on specific population groups. (C) 2009 Elsevier Ltd. All rights reserved.

A decade of Taiwanese migration in Australia: Comparisons with Mainland Chinese and Hong Kong settlers

The arrival of Taiwanese migrants to Australia represents the second major wave of Chinese immigration to this nation. Many who entered Australia did so as business migrants. They were typically well educated, affluent professionals, managers, &/or entrepreneurs who were looking for new business opportunities as well as a lifestyle characterized by open space, clean air, a good education for their children, & personal & political safety. Yet, the settlement experiences of many Taiwanese migrants, despite their affluence & (business) skills, have been typified by stress & hardship, particularly in making adjustments in social, business, & economic relationships. A review of statistical data compiled from census & government reports in Australia has revealed that after a decade Down Under, the Taiwanese settler group was still characterized by high unemployment, even when compared to other Chinese migrant groups from Hong Kong & Mainland China. It is suggested that the Taiwanese migrants' persistent high nonparticipation in Australia's labor force is indicative & poignant of their highly distinctive, albeit not exclusive in the broader Chinese migrant terms, experience of migration settlement. There seems to be an increasing number of Taiwanese settlers returning to resettle in Taiwan in recent years, because of perceived better employment & business opportunities or for family & personal reasons. Recent interviews with Taiwanese settlers have also suggested that the most recent arrivals, being more aware of the obstacles in achieving work or business satisfaction during settlement, seem less likely to commit themselves to lifelong settlement in Australia. 16 Tables, 1 Figure, 37 References. Adapted from the source document.

A trajetória de um serviço público em álcool e outras drogas no município de Vitória : o caso do CPTT

Comunicação oral apresentada na 18th World Conference of Social Work realizada em 2006 em Munique, Alemanha.