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.

Dynamics of coastal aquifers: data-driven forecasting and risk analysis

This work is focused on the study of saltwater intrusion in coastal aquifers, and in particular on the realization of conceptual schemes to evaluate the risk associated with it. Saltwater intrusion depends on different natural and anthropic factors, both presenting a strong aleatory behaviour, that should be considered for an optimal management of the territory and water resources. Given the uncertainty of problem parameters, the risk associated with salinization needs to be cast in a probabilistic framework. On the basis of a widely adopted sharp interface formulation, key hydrogeological problem parameters are modeled as random variables, and global sensitivity analysis is used to determine their influence on the position of saltwater interface. The analyses presented in this work rely on an efficient model reduction technique, based on Polynomial Chaos Expansion, able to combine the best description of the model without great computational burden. When the assumptions of classical analytical models are not respected, and this occurs several times in the applications to real cases of study, as in the area analyzed in the present work, one can adopt data-driven techniques, based on the analysis of the data characterizing the system under study. It follows that a model can be defined on the basis of connections between the system state variables, with only a limited number of assumptions about the "physical" behaviour of the system.

Applied deep learning and data science with a human-centric and data-centric approach

The term Artificial intelligence acquired a lot of baggage since its introduction and in its current incarnation is synonymous with Deep Learning. The sudden availability of data and computing resources has opened the gates to myriads of applications. Not all are created equal though, and problems might arise especially for fields not closely related to the tasks that pertain tech companies that spearheaded DL. The perspective of practitioners seems to be changing, however. Human-Centric AI emerged in the last few years as a new way of thinking DL and AI applications from the ground up, with a special attention at their relationship with humans. The goal is designing a system that can gracefully integrate in already established workflows, as in many real-world scenarios AI may not be good enough to completely replace its humans. Often this replacement may even be unneeded or undesirable. Another important perspective comes from, Andrew Ng, a DL pioneer, who recently started shifting the focus of development from “better models” towards better, and smaller, data. He defined his approach Data-Centric AI. Without downplaying the importance of pushing the state of the art in DL, we must recognize that if the goal is creating a tool for humans to use, more raw performance may not align with more utility for the final user. A Human-Centric approach is compatible with a Data-Centric one, and we find that the two overlap nicely when human expertise is used as the driving force behind data quality. This thesis documents a series of case-studies where these approaches were employed, to different extents, to guide the design and implementation of intelligent systems. We found human expertise proved crucial in improving datasets and models. The last chapter includes a slight deviation, with studies on the pandemic, still preserving the human and data centric perspective.

Data-driven and data-oriented methods for materials science and technologies

The discovery of new materials and their functions has always been a fundamental component of technological progress. Nowadays, the quest for new materials is stronger than ever: sustainability, medicine, robotics and electronics are all key assets which depend on the ability to create specifically tailored materials. However, designing materials with desired properties is a difficult task, and the complexity of the discipline makes it difficult to identify general criteria. While scientists developed a set of best practices (often based on experience and expertise), this is still a trial-and-error process. This becomes even more complex when dealing with advanced functional materials. Their properties depend on structural and morphological features, which in turn depend on fabrication procedures and environment, and subtle alterations leads to dramatically different results. Because of this, materials modeling and design is one of the most prolific research fields. Many techniques and instruments are continuously developed to enable new possibilities, both in the experimental and computational realms. Scientists strive to enforce cutting-edge technologies in order to make progress. However, the field is strongly affected by unorganized file management, proliferation of custom data formats and storage procedures, both in experimental and computational research. Results are difficult to find, interpret and re-use, and a huge amount of time is spent interpreting and re-organizing data. This also strongly limit the application of data-driven and machine learning techniques. This work introduces possible solutions to the problems described above. Specifically, it talks about developing features for specific classes of advanced materials and use them to train machine learning models and accelerate computational predictions for molecular compounds; developing method for organizing non homogeneous materials data; automate the process of using devices simulations to train machine learning models; dealing with scattered experimental data and use them to discover new patterns.

Data driven regularization models of non-linear ill-posed inverse problems in imaging

Imaging technologies are widely used in application fields such as natural sciences, engineering, medicine, and life sciences. A broad class of imaging problems reduces to solve ill-posed inverse problems (IPs). Traditional strategies to solve these ill-posed IPs rely on variational regularization methods, which are based on minimization of suitable energies, and make use of knowledge about the image formation model (forward operator) and prior knowledge on the solution, but lack in incorporating knowledge directly from data. On the other hand, the more recent learned approaches can easily learn the intricate statistics of images depending on a large set of data, but do not have a systematic method for incorporating prior knowledge about the image formation model. The main purpose of this thesis is to discuss data-driven image reconstruction methods which combine the benefits of these two different reconstruction strategies for the solution of highly nonlinear ill-posed inverse problems. Mathematical formulation and numerical approaches for image IPs, including linear as well as strongly nonlinear problems are described. More specifically we address the Electrical impedance Tomography (EIT) reconstruction problem by unrolling the regularized Gauss-Newton method and integrating the regularization learned by a data-adaptive neural network. Furthermore we investigate the solution of non-linear ill-posed IPs introducing a deep-PnP framework that integrates the graph convolutional denoiser into the proximal Gauss-Newton method with a practical application to the EIT, a recently introduced promising imaging technique. Efficient algorithms are then applied to the solution of the limited electrods problem in EIT, combining compressive sensing techniques and deep learning strategies. Finally, a transformer-based neural network architecture is adapted to restore the noisy solution of the Computed Tomography problem recovered using the filtered back-projection method.

Non-ambient solid-state characterization of crystalline molecular organic semiconductors

The research project is focused on the investigation of the polymorphism of crystalline molecular material for organic semiconductor applications under non-ambient conditions, and the solid-state characterization and crystal structure determination of the different polymorphic forms. In particular, this research project has tackled the investigation and characterization of the polymorphism of perylene diimides (PDIs) derivatives at high temperatures and pressures, in particular N,N’-dialkyl-3,4,9,10-perylendiimide (PDI-Cn, with n = 5, 6, 7, 8). These molecules are characterized by excellent chemical, thermal, and photostability, high electron affinity, strong absorption in the visible region, low LUMO energies, good air stability, and good charge transport properties, which can be tuned via functionalization; these features make them promising n-type organic semiconductor materials for several applications such as OFETs, OPV cells, laser dye, sensors, bioimaging, etc. The thermal characterization of PDI-Cn was carried out by a combination of differential scanning calorimetry, variable temperature X-ray diffraction, hot-stage microscopy, and in the case of PDI-C5 also variable temperature Raman spectroscopy. Whereas crystal structure determination was carried out by both Single Crystal and Powder X-ray diffraction. Moreover, high-pressure polymorphism via pressure-dependent UV-Vis absorption spectroscopy and high-pressure Single Crystal X-ray diffraction was carried out in this project. A data-driven approach based on a combination of self-organizing maps (SOM) and principal component analysis (PCA) is also reported was used to classify different π-stacking arrangements of PDI derivatives into families of similar crystal packing. Besides the main project, in the framework of structure-property analysis under non-ambient conditions, the structural investigation of the water loss in Pt- and Pd- based vapochromic potassium/lithium salts upon temperature, and the investigation of structure-mechanical property relationships in polymorphs of a thienopyrrolyldione endcapped oligothiophene (C4-NT3N) are reported.

Data-stream driven Fuzzy-granular approaches for system maintenance

Intelligent systems are currently inherent to the society, supporting a synergistic human-machine collaboration. Beyond economical and climate factors, energy consumption is strongly affected by the performance of computing systems. The quality of software functioning may invalidate any improvement attempt. In addition, data-driven machine learning algorithms are the basis for human-centered applications, being their interpretability one of the most important features of computational systems. Software maintenance is a critical discipline to support automatic and life-long system operation. As most software registers its inner events by means of logs, log analysis is an approach to keep system operation. Logs are characterized as Big data assembled in large-flow streams, being unstructured, heterogeneous, imprecise, and uncertain. This thesis addresses fuzzy and neuro-granular methods to provide maintenance solutions applied to anomaly detection (AD) and log parsing (LP), dealing with data uncertainty, identifying ideal time periods for detailed software analyses. LP provides deeper semantics interpretation of the anomalous occurrences. The solutions evolve over time and are general-purpose, being highly applicable, scalable, and maintainable. Granular classification models, namely, Fuzzy set-Based evolving Model (FBeM), evolving Granular Neural Network (eGNN), and evolving Gaussian Fuzzy Classifier (eGFC), are compared considering the AD problem. The evolving Log Parsing (eLP) method is proposed to approach the automatic parsing applied to system logs. All the methods perform recursive mechanisms to create, update, merge, and delete information granules according with the data behavior. For the first time in the evolving intelligent systems literature, the proposed method, eLP, is able to process streams of words and sentences. Essentially, regarding to AD accuracy, FBeM achieved (85.64+-3.69)%; eGNN reached (96.17+-0.78)%; eGFC obtained (92.48+-1.21)%; and eLP reached (96.05+-1.04)%. Besides being competitive, eLP particularly generates a log grammar, and presents a higher level of model interpretability.

Creazione e sviluppo di corpora multimediali. Nuove metodologie di ricerca nella traduzione audiovisiva

The construction and use of multimedia corpora has been advocated for a while in the literature as one of the expected future application fields of Corpus Linguistics. This research project represents a pioneering experience aimed at applying a data-driven methodology to the study of the field of AVT, similarly to what has been done in the last few decades in the macro-field of Translation Studies. This research was based on the experience of Forlixt 1, the Forlì Corpus of Screen Translation, developed at the University of Bologna’s Department of Interdisciplinary Studies in Translation, Languages and Culture. As a matter of fact, in order to quantify strategies of linguistic transfer of an AV product, we need to take into consideration not only the linguistic aspect of such a product but all the meaning-making resources deployed in the filmic text. Provided that one major benefit of Forlixt 1 is the combination of audiovisual and textual data, this corpus allows the user to access primary data for scientific investigation, and thus no longer rely on pre-processed material such as traditional annotated transcriptions. Based on this rationale, the first chapter of the thesis sets out to illustrate the state of the art of research in the disciplinary fields involved. The primary objective was to underline the main repercussions on multimedia texts resulting from the interaction of a double support, audio and video, and, accordingly, on procedures, means, and methods adopted in their translation. By drawing on previous research in semiotics and film studies, the relevant codes at work in visual and acoustic channels were outlined. Subsequently, we concentrated on the analysis of the verbal component and on the peculiar characteristics of filmic orality as opposed to spontaneous dialogic production. In the second part, an overview of the main AVT modalities was presented (dubbing, voice-over, interlinguistic and intra-linguistic subtitling, audio-description, etc.) in order to define the different technologies, processes and professional qualifications that this umbrella term presently includes. The second chapter focuses diachronically on various theories’ contribution to the application of Corpus Linguistics’ methods and tools to the field of Translation Studies (i.e. Descriptive Translation Studies, Polysystem Theory). In particular, we discussed how the use of corpora can favourably help reduce the gap existing between qualitative and quantitative approaches. Subsequently, we reviewed the tools traditionally employed by Corpus Linguistics in regard to the construction of traditional “written language” corpora, to assess whether and how they can be adapted to meet the needs of multimedia corpora. In particular, we reviewed existing speech and spoken corpora, as well as multimedia corpora specifically designed to investigate Translation. The third chapter reviews Forlixt 1's main developing steps, from a technical (IT design principles, data query functions) and methodological point of view, by laying down extensive scientific foundations for the annotation methods adopted, which presently encompass categories of pragmatic, sociolinguistic, linguacultural and semiotic nature. Finally, we described the main query tools (free search, guided search, advanced search and combined search) and the main intended uses of the database in a pedagogical perspective. The fourth chapter lists specific compilation criteria retained, as well as statistics of the two sub-corpora, by presenting data broken down by language pair (French-Italian and German-Italian) and genre (cinema’s comedies, television’s soapoperas and crime series). Next, we concentrated on the discussion of the results obtained from the analysis of summary tables reporting the frequency of categories applied to the French-Italian sub-corpus. The detailed observation of the distribution of categories identified in the original and dubbed corpus allowed us to empirically confirm some of the theories put forward in the literature and notably concerning the nature of the filmic text, the dubbing process and Italian dubbed language’s features. This was possible by looking into some of the most problematic aspects, like the rendering of socio-linguistic variation. The corpus equally allowed us to consider so far neglected aspects, such as pragmatic, prosodic, kinetic, facial, and semiotic elements, and their combination. At the end of this first exploration, some specific observations concerning possible macrotranslation trends were made for each type of sub-genre considered (cinematic and TV genre). On the grounds of this first quantitative investigation, the fifth chapter intended to further examine data, by applying ad hoc models of analysis. Given the virtually infinite number of combinations of categories adopted, and of the latter with searchable textual units, three possible qualitative and quantitative methods were designed, each of which was to concentrate on a particular translation dimension of the filmic text. The first one was the cultural dimension, which specifically focused on the rendering of selected cultural references and on the investigation of recurrent translation choices and strategies justified on the basis of the occurrence of specific clusters of categories. The second analysis was conducted on the linguistic dimension by exploring the occurrence of phrasal verbs in the Italian dubbed corpus and by ascertaining the influence on the adoption of related translation strategies of possible semiotic traits, such as gestures and facial expressions. Finally, the main aim of the third study was to verify whether, under which circumstances, and through which modality, graphic and iconic elements were translated into Italian from an original corpus of both German and French films. After having reviewed the main translation techniques at work, an exhaustive account of possible causes for their non-translation was equally provided. By way of conclusion, the discussion of results obtained from the distribution of annotation categories on the French-Italian corpus, as well as the application of specific models of analysis allowed us to underline possible advantages and drawbacks related to the adoption of a corpus-based approach to AVT studies. Even though possible updating and improvement were proposed in order to help solve some of the problems identified, it is argued that the added value of Forlixt 1 lies ultimately in having created a valuable instrument, allowing to carry out empirically-sound contrastive studies that may be usefully replicated on different language pairs and several types of multimedia texts. Furthermore, multimedia corpora can also play a crucial role in L2 and translation teaching, two disciplines in which their use still lacks systematic investigation.

Bioengineering of exercise: biomechanical and metabolic aspects

The field of research of this dissertation concerns the bioengineering of exercise, in particular the relationship between biomechanical and metabolic knowledge. This relationship can allow to evaluate exercise in many different circumstances: optimizing athlete performance, understanding and helping compensation in prosthetic patients and prescribing exercise with high caloric consumption and minimal joint loading to obese subjects. Furthermore, it can have technical application in fitness and rehabilitation machine design, predicting energy consumption and joint loads for the subjects who will use the machine. The aim of this dissertation was to further understand how mechanical work and metabolic energy cost are related during movement using interpretative models. Musculoskeletal models, when including muscle energy expenditure description, can be useful to address this issue, allowing to evaluate human movement in terms of both mechanical and metabolic energy expenditure. A whole body muscle-skeletal model that could describe both biomechanical and metabolic aspects during movement was identified in literature and then was applied and validated using an EMG-driven approach. The advantage of using EMG driven approach was to avoid the use of arbitrary defined optimization functions to solve the indeterminate problem of muscle activations. A sensitivity analysis was conducted in order to know how much changes in model parameters could affect model outputs: the results showed that changing parameters in between physiological ranges did not influence model outputs largely. In order to evaluate its predicting capacity, the musculoskeletal model was applied to experimental data: first the model was applied in a simple exercise (unilateral leg press exercise) and then in a more complete exercise (elliptical exercise). In these studies, energy consumption predicted by the model resulted to be close to energy consumption estimated by indirect calorimetry for different intensity levels at low frequencies of movement. The use of muscle skeletal models for predicting energy consumption resulted to be promising and the use of EMG driven approach permitted to avoid the introduction of optimization functions. Even though many aspects of this approach have still to be investigated and these results are preliminary, the conclusions of this dissertation suggest that musculoskeletal modelling can be a useful tool for addressing issues about efficiency of movement in healthy and pathologic subjects.

Measurement of the differential cross section of tt pairs in pp collision at sqrt(s) = 7TeV with the ATLAS detector at the LHC

In this thesis three measurements of top-antitop differential cross section at an energy in the center of mass of 7 TeV will be shown, as a function of the transverse momentum, the mass and the rapidity of the top-antitop system. The analysis has been carried over a data sample of about 5/fb recorded with the ATLAS detector. The events have been selected with a cut based approach in the "one lepton plus jets" channel, where the lepton can be either an electron or a muon. The most relevant backgrounds (multi-jet QCD and W+jets) have been extracted using data driven methods; the others (Z+ jets, diboson and single top) have been simulated with Monte Carlo techniques. The final, background-subtracted, distributions have been corrected, using unfolding methods, for the detector and selection effects. At the end, the results have been compared with the theoretical predictions. The measurements are dominated by the systematic uncertainties and show no relevant deviation from the Standard Model predictions.

Spatial and temporal characterisation of ground deformation recorded by geodetic techniques

A critical point in the analysis of ground displacements time series is the development of data driven methods that allow the different sources that generate the observed displacements to be discerned and characterised. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows reducing the dimensionality of the data space maintaining most of the variance of the dataset explained. Anyway, PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem, i.e. in recovering and separating the original sources that generated the observed data. This is mainly due to the assumptions on which PCA relies: it looks for a new Euclidean space where the projected data are uncorrelated. The Independent Component Analysis (ICA) is a popular technique adopted to approach this problem. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, I use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources, giving a more reliable estimate of them. Here I present the application of the vbICA technique to GPS position time series. First, I use vbICA on synthetic data that simulate a seismic cycle (interseismic + coseismic + postseismic + seasonal + noise) and a volcanic source, and I study the ability of the algorithm to recover the original (known) sources of deformation. Secondly, I apply vbICA to different tectonically active scenarios, such as the 2009 L'Aquila (central Italy) earthquake, the 2012 Emilia (northern Italy) seismic sequence, and the 2006 Guerrero (Mexico) Slow Slip Event (SSE).

Hybrid Artificial Intelligence to Extract Patterns and Rules from Argumentative and Legal Texts

This Thesis is composed of a collection of works written in the period 2019-2022, whose aim is to find methodologies of Artificial Intelligence (AI) and Machine Learning to detect and classify patterns and rules in argumentative and legal texts. We define our approach “hybrid”, since we aimed at designing hybrid combinations of symbolic and sub-symbolic AI, involving both “top-down” structured knowledge and “bottom-up” data-driven knowledge. A first group of works is dedicated to the classification of argumentative patterns. Following the Waltonian model of argument and the related theory of Argumentation Schemes, these works focused on the detection of argumentative support and opposition, showing that argumentative evidences can be classified at fine-grained levels without resorting to highly engineered features. To show this, our methods involved not only traditional approaches such as TFIDF, but also some novel methods based on Tree Kernel algorithms. After the encouraging results of this first phase, we explored the use of a some emerging methodologies promoted by actors like Google, which have deeply changed NLP since 2018-19 — i.e., Transfer Learning and language models. These new methodologies markedly improved our previous results, providing us with best-performing NLP tools. Using Transfer Learning, we also performed a Sequence Labelling task to recognize the exact span of argumentative components (i.e., claims and premises), thus connecting portions of natural language to portions of arguments (i.e., to the logical-inferential dimension). The last part of our work was finally dedicated to the employment of Transfer Learning methods for the detection of rules and deontic modalities. In this case, we explored a hybrid approach which combines structured knowledge coming from two LegalXML formats (i.e., Akoma Ntoso and LegalRuleML) with sub-symbolic knowledge coming from pre-trained (and then fine-tuned) neural architectures.

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

Towards Unstructured Knowledge Integration in Natural Language Processing

In the last decades, Artificial Intelligence has witnessed multiple breakthroughs in deep learning. In particular, purely data-driven approaches have opened to a wide variety of successful applications due to the large availability of data. Nonetheless, the integration of prior knowledge is still required to compensate for specific issues like lack of generalization from limited data, fairness, robustness, and biases. In this thesis, we analyze the methodology of integrating knowledge into deep learning models in the field of Natural Language Processing (NLP). We start by remarking on the importance of knowledge integration. We highlight the possible shortcomings of these approaches and investigate the implications of integrating unstructured textual knowledge. We introduce Unstructured Knowledge Integration (UKI) as the process of integrating unstructured knowledge into machine learning models. We discuss UKI in the field of NLP, where knowledge is represented in a natural language format. We identify UKI as a complex process comprised of multiple sub-processes, different knowledge types, and knowledge integration properties to guarantee. We remark on the challenges of integrating unstructured textual knowledge and bridge connections with well-known research areas in NLP. We provide a unified vision of structured knowledge extraction (KE) and UKI by identifying KE as a sub-process of UKI. We investigate some challenging scenarios where structured knowledge is not a feasible prior assumption and formulate each task from the point of view of UKI. We adopt simple yet effective neural architectures and discuss the challenges of such an approach. Finally, we identify KE as a form of symbolic representation. From this perspective, we remark on the need of defining sophisticated UKI processes to verify the validity of knowledge integration. To this end, we foresee frameworks capable of combining symbolic and sub-symbolic representations for learning as a solution.

Abusing data dominance in the digital market. The application of merger control and article 102 TFEU to data-related conducts.

The internet and digital technologies revolutionized the economy. Regulating the digital market has become a priority for the European Union. While promoting innovation and development, EU institutions must assure that the digital market maintains a competitive structure. Among the numerous elements characterizing the digital sector, users’ data are particularly important. Digital services are centered around personal data, the accumulation of which contributed to the centralization of market power in the hands of a few large providers. As a result, data-driven mergers and data-related abuses gained a central role for the purposes of EU antitrust enforcement. In light of these considerations, this work aims at assessing whether EU competition law is well-suited to address data-driven mergers and data-related abuses of dominance. These conducts are of crucial importance to the maintenance of competition in the digital sector, insofar as the accumulation of users’ data constitutes a fundamental competitive advantage. To begin with, part 1 addresses the specific features of the digital market and their impact on the definition of the relevant market and the assessment of dominance by antitrust authorities. Secondly, part 2 analyzes the EU’s case law on data-driven mergers to verify if merger control is well-suited to address these concentrations. Thirdly, part 3 discusses abuses of dominance in the phase of data collection and the legal frameworks applicable to these conducts. Fourthly, part 4 focuses on access to “essential” datasets and the indirect effects of anticompetitive conducts on rivals’ ability to access users’ information. Finally, Part 5 discusses differential pricing practices implemented online and based on personal data. As it will be assessed, the combination of an efficient competition law enforcement and the auspicial adoption of a specific regulation seems to be the best solution to face the challenges raised by “data-related dominance”.