Computing and Information Systems - Theses
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ItemDo personality traits drive online commitment to vote in social networks?( 2019)This study examines social network effects in a community election, and actor attributes effects in fostering commitment to vote behaviour. Across Western societies political participation is in decline posing major challenges for democracy. Since 2000,, political advocacy has undergone a rapid transformation led by a disruptive wave of IT-led innovation in infrastructure, predictive analytics, and online social networks. Political campaigns have harnessed these advances to target, influence, and mobilise partisan voters. Yet is political participation uniform across voters? Network interventions using online social networks and behavioural science are found to increase voter turnout, and reveal individual differences in political behaviour (Bakshy, Messing, & Adamic, 2015; Bond et al., 2012). In particular, extroverted individuals relative to other users may play an enhanced role (Messing, 2015). Our current picture of personality traits and political behaviour is largely offline. Few studies relate to online behaviour (Jordan, Pope, Wallis, & Iyer, 2015). Studies examine individual responses to general/targeted information against two-step flow communication models (Lazarsfeld, Berelson, & Gaudet, 1948). Yet opinion leaders and social networks still shape individual attitudes and behaviours (Contractor & DeChurch, 2014). How personality traits guide social interactions and social influence online during an election is unclear. In this research we examine the interaction of personality traits, internal political efficacy and human social motives to enact social influence during a community election. Our research model investigates whether personality traits drive commitment to vote behaviour by eliciting implementation intentions using an online vote plan. The attribute of gender, often ignored, is incorporated to determine influence processes operating within the network. Prior online political network analysis has relied on data collected from third-party platforms intended for other purposes. We overcome this limitation using a novel, mobile-first, social media app operating as a social network to better connect community members to political information, increase engagement, and improve transparency. The app enables unobtrusive data collection based on voluntary user interaction with online information. The app is supported by scalable graph-database architecture for behaviour tracking, real-time analytics, and increased granularity. All attribute-validated measurement instruments for personality traits, internal political efficacy, and commitment to vote are integrated into the app with only single responses recorded along with demographic information for each user. To understand how an individual’s attributes and their relationships with others affect commitment to vote, we adopt an Autologistic actor attribute model (ALAAM), a social influence model for statistical analysis of observed network data. With political disengagement endemic across Western democracies, new interventions that mitigate, or turn around current trends, are highly valued. Beyond political network analysis, social network research opportunities in national and international economic, institutional, and community settings exist. By deepening our understanding of small world interactions we hope to respond to the collective challenge of restoring the link between the meaning and purpose of voting, and help reinvigorate democracy.
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ItemScalable clustering of high dimensional data in non-disjoint axis-parallel subspaces( 2019)Clustering is the task of grouping similar objects together, where each group formed is called a cluster. Clustering is used to discover hidden patterns or underlying structures from the data, and has a wide range of applications in areas such as the Internet of Things (IoT), biology, medicine, marketing, business, and computing. Recent developments in sensor and storage technology have led to a rapid growth of data, both in terms of volume and dimensionality. This raises challenges for existing clustering algorithms and led to the development of subspace clustering algorithms that cope with the characteristics, volumes, and dimensionality of the datasets that are now available. In this thesis, we address the challenges of finding subspace clusters in high dimensional data to achieve subspace clustering with high quality and scalability. We provide a comprehensive literature review of existing algorithms, and identify the open challenges in subspace clustering of high dimensional data that we address in this thesis, namely: devising appropriate similarity measures, finding non-disjoint subspace clusters, and achieving scalability to high dimensional data. We further illustrate these challenges in a real-life application. We show that clustering can be used to construct a meaningful model of the pedestrian distribution in the City of Melbourne, Australia, in low dimensional space. However, we demonstrate that the clustering quality deteriorates rapidly as the number of dimensions (pedestrian observation points) increase. This also serves as a motivating example on why subspace clustering is needed and what challenges need to be addressed. We first address the challenge of measuring similarity between data points, which is a key challenge in analyzing high dimensional data that directly impacts the clustering results. We propose a novel method that generates meaningful similarity measures for subspace clustering. Our proposed method considers the similarity between any two points as the union of base similarities that are frequently observed in lower dimensional subspaces. This allows our method to first search for similarity in lower dimensional subspaces and aggregate these similarity values to determine the overall similarity. We show that this method can be applied for measuring similarity based on distance, density, and grids, which enables our similarity measurements to be used with different types of clustering algorithms, i.e., distance-based, density-based, and grid-based clustering algorithms. We then use our similarity measurement to build a subspace clustering algorithm that can find clusters in non-disjoint subspaces. Our proposed algorithm follows a bottom-up strategy. The first phase of our algorithm searches for base clusters in low dimensional subspaces. Subsequently, the second phase forms clusters in higher dimensional subspaces by aggregating these base clusters. The novelty of our proposed method is reflected in both phases. First, we show that our similarity measurement can be integrated in a subspace clustering algorithm. This not only helps prevent the false formation of clusters, but also significantly reduces the time and space complexity of the algorithm by pruning irrelevant subspaces at an early stage. Second, our algorithm transforms the common sequential aggregation of base clusters into a problem of frequent pattern mining. This enables efficient formation of clusters in high dimensional subspaces using FP-Trees. We then demonstrate that our proposed algorithm can outperform traditional subspace clustering algorithms using bottom-up strategies, as well as state-of-the-art algorithms with other clustering strategies, in terms of clustering quality and scalability to large volumes and high dimensionality of data. Subsequently, we evaluate the ability of our proposed subspace clustering algorithm to find clusters in datasets from different real-life applications. We conduct experiments on datasets from three different applications. First, we apply our proposed clustering algorithm to pedestrian measurements in the City of Melbourne, and construct a meaningful model that describes the profiles of the distributions of pedestrians that correspond to pedestrian activities at different times of the day. We then use our clustering algorithm to analyze the impacts of a major change in the public transport of Melbourne on the activities of pedestrians. In the second application, we evaluate the ability of the proposed algorithm to work with very high dimensional data. Specifically, we apply our algorithm on ten gene expression datasets, which comprise up to 10,000 dimensions. Next, we explore the ability of our algorithm to produce clustering results that can be used as an intermediate step that assists the construction of a more complicated model. Specifically, we use our clustering result to build an ensemble classification model, and show that this model improves the accuracy of predicting the car parking occupancy in the central business district (CBD) of the City of Melbourne. By applying our proposed methods in a wide range of applications on datasets with different sizes and dimensionalities, we demonstrate the ability of our algorithm to cluster high dimensional datasets that possess complex structures with high levels of noise and outliers to produce meaningful clustering results that can have practical impact.
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ItemOntologies in neuroscience and their application in processing questions( 2019)Neuroscience is a vast, multi-dimensional and complex field of study based on both its medical importance and unresolved issues regarding how brain and the nervous system work. This is because of the huge amount of brain disorders and their burden on people and society. Furthermore, scientist have been excited about the function and structure of brain, ever since it was discovered to be responsible for all our emotions, thoughts and behaviour. Ontologies are concepts whose origins go back to philosophy and the concern with the nature and relation of being. They have emerged as promising tools for assistance with neuroscience research recently and provide additional data on a field of study. They connect each entity or element to other ones through descriptive relationships. Ontologies seem to suit the complex, multi-dimensional and still incomplete nature of neuroscience very well because of their characteristics. The first study shines light on applications of ontologies in neuroscience. It incorporated a systematic literature review and methodically reviewed over 1000 research papers from eight databases and three journals. After scanning all documents, 208 of them were selected. Then, a full text analysis was performed on the selected documents. This study found eight major applications for ontologies in neuroscience, most of them consisted of several subcategories. The analysis not only demonstrated the current applications of ontologies in neuroscience, but also their potential future in this field. The second study was set to represent neuroscience questions and then, classify them using ontologies. For this purpose, a questions set was gathered from two research teams and analysed. This, results in a set of dimensions which represents questions. Then, a question hierarchy was formed based on dimensions and questions were classified according to that hierarchy. Two different approaches were used for the classification including an ontology-based approach and a statistical approach. The ontology-based approach exceeded the statistical approach by 15.73% better classification results. The last study was designed to tackle and resolve questions with the assistance of ontologies. It first proposed a set of templates that acted as a translation mechanism for changing questions into machine readable code. Templates were based on the question hierarchy presented in the previous study. Second, this study created an integrated collection of resources including two domain ontologies (NIFSTD and NeuroFMA) and a neuroimaging annotation application (Freesurfer). Subsequently, the code created using templates was executed upon the integrated resource (knowledge base) to find the appropriate answer. While processing the questions, ontologies were used for disambiguation purposes too. At the end, all parts created in this study along with the question classification method created in the previous study were merged as different modules of a question processing model. In conclusion, this thesis reviewed all current ontology applications in neuroscience in detail and demonstrated the extent to which they can assist scientists in classifying and resolving questions. The results of this thesis show that applications of ontologies in neuroscience are diverse and cover a wide range; they are steadily becoming more used in this field; and they can be powerful semantic tools in performing different tasks in neuroscience.
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ItemTowards Robust Representation of Natural Language Processing( 2019)There are many challenges in building robust natural language applications. Machine learning based methods require large volumes of annotated text data, and variations over text can lead to problems, namely: (1) language can be highly variable and expressed with different variations, such as lexical and syntactic. Robust models should be able to handle these variations. (2) A text corpus is heterogeneous, often making language systems domain-brittle. Solutions for domain adaptation and training with corpora comprised of multiple domains are required for language applications in the real world. (3) Many language applications tend to be biased to the demographic of the authors of documents the system is trained on, and lack model fairness. Demographic bias also causes privacy issues when a model is made available to others. In this thesis, I aim to build robust natural language models to tackle these problems, focusing on deep learning approaches which have shown great success in language processing via representation learning. I pose three basic research questions: how to learn representations that are robust to language variation, robust to domain variation, and robust to demographic variables. Each of these research questions is tackled using different approaches, including data augmentation, adversarial learning, and variational inference. For learning robust representations to language variation, I study lexical variation and syntactic variation. To be specific, a regularisation method is proposed to tackle lexical variation, and a data augmentation method is proposed to build robust models, using a range of language generation methods from both linguistic and machine learning perspectives. For domain robustness, I focus on multi-domain learning and investigate domain supervised and unsupervised learning, where domain labels may or may not be available. Two types of models are proposed, via adversarial learning and latent domain gating, to build robust models for heterogeneous text. For robustness to demographics, I show that demographic bias in the training corpus leads to model fairness problems with respect to the demographic of the authors, as well as privacy issues under inference attacks. Adversarial learning is adopted to mitigate bias in representation learning, to improve model fairness and privacy-preservation. To demonstrate the proposed approaches, a range of tasks are considered, including text classification and POS tagging. To evaluate the generalisation and robustness, both in-domain and out-of-domain experiments are conducted with two classes of language tasks: text classification and part-of-speech tagging. For multi-domain learning, multi-domain language identification and multi-domain sentiment classification are conducted, and I simulate domain supervised learning and domain unsupervised learning to evaluate domain robustness. I evaluate model fairness with different demographic attributes and apply inference attacks to test model privacy. The experiments show the advantages and the robustness of the proposed methods. Finally, I discuss the relations between the different forms of robustness, including their commonalities and differences. The limitations of this thesis are discussed in detail, including potential methods to address these shortcomings in future work, and potential opportunities to generalise the proposed methods to other language tasks. Above all, these methods of learning robust representations can contribute towards progress in natural language processing.
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ItemHealth Information Systems Enabled Transformation of Service Ecosystems: The Case of Indonesian Healthcare( 2019)Information and Communication Technology (ICT) has contributed significantly to the socio-economic development of societies. In particular, developing countries are now beginning to undertake ICT-enabled transformations that previously took place in the western world. However, while the proliferation of ICT is considered a crucial enabler of this transformation, ICT for Development (ICT4D) projects continue to fail as they do not achieve the anticipated societal impacts. Therefore, a holistic and systemic perspective of ICT4D research is needed to enhance the current understanding of these phenomena. This study addresses this knowledge gap through an in-depth investigation on how the structure of public health ecosystem in Indonesia is changed and transformed following Health Information Systems (HIS) introduction. A qualitative multiple case study was conducted across three district-level government. The analysis reveals the distinctive impacts of HIS introduction on the structural properties of the ecosystem, which include institutional rules, resources configuration, actors’ institutional logics, and practices. This study also identifies three mechanisms (adoption-incorporation, breaking-making, and self-reinforcing) of HIS enabled transformation which constitute two pathways (enslaving and emergence) of the ecosystem's transformation. The findings of this study offer theoretical contributions to ICT4D and service literature and practical contributions to HIS implementation in Indonesia. The transformation process of the ecosystem’s structure offers a systemic perspective of ICT4D, which overcomes the tendency to overemphasise the significance role of technology and agency in developing countries. The pathways of transformation complement those earlier studies investigating the reasons for numerous failures of the top-down technological transfer and the importance of inclusion, engagement, and empowerment of the societal groups in ICT4D. To service literature, this study offers insights into the origins and lifecycle of practices and how they emerge in the ecosystem, which shed light on the dynamic and evolving nature of ecosystem’s structure that currently has not been adequately understood. Finally, the results of this study advocate the autonomy of the district’s health providers, the inclusion and engagement of local actors, and the use of the incremental approach to HIS implementation in public health ecosystem.
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ItemPrivacy-Preserving Approaches to Analyzing Sensitive Trajectory Data( 2019)The evolution of smart devices and sensor-enabled vehicles has brought forward the capability of collecting large and rich datasets. The datasets provide unprecedented opportunities for devising the next generation of location-based decision systems. Analysing detailed continually updated information of a user's status such as location, speed and direction is vital in improving the safety, reliability, mobility and efficiency of any form of location-based services in smart cities. More generally, trajectory data is paramount for studying people's movement patterns, shopping behaviour and preferences (i.e., visited cafes, parks, and their sequence of points of interest). However, such fine-grained data raises significant concerns about the privacy of individuals, which in turn hinders the further development of next generation applications that benefit from trajectory data. Such data can reveal various sensitive information about individuals such as their home and workplace locations, whereabouts over time and health. Recent approaches to address such concerns use a strong privacy guarantee -- known as differential privacy. Their aim is to tackle a core privacy challenge: publishing modified datasets of individuals without compromising their privacy while not sacrificing the utility of the published data. However, the current approaches guaranteeing differential privacy are limited in scalability and utility for real applications which both are crucial for later usage or data analytics. In this thesis, we are concerned with publishing trajectory data which poses privacy risks due to its sequential nature. A key issue is that the known algorithms fail to preserve the utility of published trajectory data when perturbing it to satisfy differential privacy. Critical information of trajectory datasets such as total travel distances and frequent location patterns in trajectories cannot be fully preserved by the existing differentially private algorithms. This thesis investigates three research issues. First, it is known that simple histograms, which is widely studied under differential privacy, are insufficient to capture aggregated information for spatial data. Our first work shows how to use instead spatial histograms to provide accurate distribution of traffic counts with differential privacy guarantee. Spatial histograms must satisfy sequential constraints (spatial) and naively applying differential privacy can destroy sequential constraints. Our proposed algorithm computes new information about trajectory counts without destroying spatial constraints and hence, improves the utility of published data. We further refine the algorithm to improve the utility of the published data by incorporating the traffic distribution. Intuitively, dense regions gain more information about the trajectory counts compared to sparse regions. Since the density of different regions might be uneven, we need to directly use trajectory densities to accurately compute information about the trajectory distribution in the regions for efficiently scaling the added noise to ensure differential privacy. Spatial histogram data has limitations in terms of spatial queries. For example, we cannot ask queries such as ``how many trajectories start from location A and end at location B?''. To address this limitation, in our third work, instead of using count information from trajectories as in spatial histograms we use actual trajectory data. We introduce a graphical model to capture accurate statistics about the movement behaviours in trajectories. Using this model, our algorithm privately generates synthetic trajectories such that the noise is optimally added to capture the movement direction of a trajectory. Our algorithm preserves both the spatial and temporal information of trajectories in the generated dataset, requires less memory and computation than competing approaches, and preserves the properties of original trajectory data in terms of travelled distance, movement patterns and locations of interest. Our extensive theoretical and experimental analysis shows the significant improvement in the utility of published data generated by our algorithms.
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ItemPattern Recognition and Predictive Modelling in Smart Grids( 2019)Smart grids are a modification of traditional electric power grid to achieve a bidirectional, automatic, intelligent and adaptive power system. In smart grids the electricity distribution and power system management is improved by leveraging advanced two-way communications and integrating computing capabilities to achieve better reliability, stability, efficiency, and security of the power system. The smart grid introduces the two-way flow of data between electricity suppliers and customers to transfer real-time information and facilitate the near real-time balance of supply-demand management. In contrast to many other industries who have the capability to store and reserve their products, the electric power industry does not have such a capability to store a massive amount of electricity using today’s technologies. Therefore, due to the storage limitations of electricity, one of the crucial tasks of power system operation is to keep a balance between supply and demand at every moment. As a result, forecasting is an essential and important function in the electricity power grid. Recent advances in the energy industry, including smart grid and smart meters, provide new capabilities to electrical utilities for forecasting electricity demand, modelling customers’ usage profiles, optimizing unit commitment and preventing outages. These advances also introduce new challenges to the power grid, such as managing and analysing of large volumes of complex, high dimensional data in an efficient manner. So, utilities need to apply advanced data management and analytical models to extract actionable insights from this information. By leveraging better predictive and analytical models and the high volume of data, utility companies are able to produce a wide range of forecasts including: 1. Forecasting the amount of excess energy generation, the appropriate time to sell it, and the feasibility of transmitting it into the grid 2. Forecasting when and where contingencies are most likely to happen 3. Identifying the customers that are most likely to transfer energy back to the grid 4. Identifying the customers that are most likely to respond to demand reduction incentives and energy conservation programs 5. Considering the generation of distributed energy resources in the decision-making process to manage the commitment of conventional plants 6. Considering the integration of renewable energy resources into the power grid, which are inherently intermittent, weather dependent and unpredictable, to run a clean and reliable power system. To achieve these potential benefits, grid operators require accurate and efficient methods to mine patterns in customers and grid data, which can be integrated into their decision-making frameworks. In this thesis, we develop new predictive machine learning algorithms to help address the new challenges in the smart grid era. In the first part of this thesis, we focus on understanding customers’ energy consumption behaviour (demand analytics). Previously, information about customers’ energy consumption could be obtained only with coarse granularity (e.g., monthly or bimonthly), Nowadays, using advanced metering infrastructure (or smart meters), utility companies are able to retrieve it in near real-time. By leveraging smart meter data, we propose a hierarchical demand forecasting approach. We improve the aggregated level of electricity load forecasts by first segmenting the households into several clusters, forecasting the energy consumption of each cluster, and then aggregating those forecasts. The improvements provided by this strategy depend not only on the number of clusters, but also on the size of the clusters and selecting an appropriate clustering method. We also leverage deep learning techniques to improve forecast accuracy. Dealing with the high volume of time-series data (smart meter data) has motivated us to develop a new clustering algorithm for time-series data that is computationally efficient and accurate. In the second part of this thesis, we introduce our two new proposed clustering algorithms namely, “Fuzzy C-Shape plus (FCS+)” and “Fuzzy C-Shape plus plus (FCS++)” and we show that the two new algorithms outperform state-of-the-art shape-based clustering algorithms in terms of accuracy and efficiency. Improving accuracy is a primary goal in any forecasting task, which is especially challenging in multi-step prediction scenarios. In the third part of this thesis we propose a robust and accurate ensemble-based load forecasting framework to address some of the challenges associated with load forecasting, including unbalanced training load data, the non-stationary nature of the load data, and feature selection for predictive modelling. The performance of the proposed method is validated with real-life data from the power system in the Australian National Electricity Market, as well as through on-site implementation by the system operator. In practice, an understanding of the uncertainty in the forecasts that an operational power grid uses is crucial in order to operate the system in a secure manner in real-time and into the future. In the fourth part of this thesis, we propose a dynamic stochastic decision support tool, based on Dynamic Bayesian Belief Networks, to quantify the level of uncertainty in order to improve situational awareness and understand the risks to power system operators. The performance of the proposed method is validated on real-life data from the Australian power system, and through actual on-site implementation by the Australian system operator, the Australian Energy Market Operator.
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ItemPattern Aided Explainable Machine Learning( 2019)Interpretability has been recognized as an important property of machine learning models. Lack of interpretability brings challenges for the deployments of many black models such as random forest, support vector machine (SVM) and neural networks. One aspect of interpretability is the ability to provide explanations for the predictions of a model, and explanations help users to understand the logical reasoning behind a model, thus giving users greater confidence to accept/reject predictions. Explanations are useful and sometimes even mandatory in domains like medical analysis, marketing, and criminal investigations, where decisions based on the predictions may have severe consequences. Traditional classifiers can be categorized into interpretable models (or white-box models) and non-interpretable models (or black-box models). Interpretable models are the models whose internal structures or parameters are simple and can be easily explained, the examples of interpretable models are decision trees, linear models, logistic regression models. Non-interpretable models are the models whose are complex and difficult to explain, the examples of non-interpretable models are random forests, support vector machines and neural networks. Though the white-box models are intrinsically easy to interpret, they usually fail to achieve comparative accuracy as the black-box models. To facilitate the successful deployments of machine learning models when both of interpretability and accuracy are desired, there exist two directions of research: (1) increasing the accuracy of white-box models, and (2) increasing the interpretability of black-box models. Patterns are conjunctions of feature-value conditions, which are intrinsically easy to comprehend, and they have been shown to have good predictive power as well. The objectives of the thesis is to propose methods to utilize patterns to increase the accuracy of white-box models by interpretable feature engineering and building interpretable models, and help black-box models provide explanations. First, we discuss the pattern based interpretable feature engineering. Pattern based feature engineering extracts patterns from data, selects the representative patterns from the extracted candidates and then projects the instances in the original feature space into new pattern based feature space with a mapping function. The new pattern based features can be more discriminative, and meanwhile they are interpretable. Second, we propose a method to explain any classifier using contrast patterns. Given a model and a query instance being explained, the proposed method first generates a synthetic neighborhood around the query using random perturbations, then labels the synthetic instances using the model, finally the method mines contrast patterns from the synthetic neighborhood and selects top K patterns as the final explanations. The experiments show that the method is able to achieve high faithfulness such that the explanations truly reveal how a model ``thinks'', moreover the method is able to support scenarios where there exist multiple possible explanations. Third, we analyse why some instances are difficult to explain. We investigate the crucial process of generating synthetic neighbors for local explanations methods, as different synthetic neighbors can result in explanations of different quality, and in many cases, the random perturbation does work well. We analyze the relationship of local intrinsic dimensionality (LID) and the quality of explanations, and propose a LID based method to generate the synthetic neighbors such that the generated synthetic neighbors are more effective than the ones generated by other baseline methods. Then we propose an interpretable model that achieves comparable accuracy with the state of the art baselines using patterns. The proposed method is a pattern based partition-wise linear models method that can be trained together with expert explanations. It divides the data space into several partitions, and each partition is represented by a pattern and is associated with a local linear model. The overall prediction for a query is a linear combination of the local linear models in the activated partitions. The model is interpretable and is able to work with expert explanations as a loss function in terms of explanations is part of the ultimate loss function. The results show that the proposed method is able to make superior reliable predictions and achieve competitive accuracy comparing with the baseline methods. Finally, we show how to construct a model to make both accurate and reliable predictions by jointly learning explanations and class labels using multi-task learning in neural networks. We propose a neural network structure that is able to jointly train the class labels and the explanations where the explanations can be treated as another label information. We fit a neural network in the framework of multi-task learning. The neural network starts with a set of shared layers and then split into two separate layers where one is for class label and the other is for explanations. The experiments suggest that the proposed method is able to make reliable predictions. In summary, this work recognizes the importance of interpretability of machine learning models, and it utilizes patterns to help improve the interpretability of machine learning models through: interpretable feature generation, pattern based model-agnostic local explanation extraction, pattern based partition-wise linear models and joint learning framework with explanations and class labels. We also investigate why a particular instance is difficult to explain using local intrinsic dimensionality. All work is supported by theoretical analysis and empirical evaluations.
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ItemMemory-augmented neural networks for better discourse understanding( 2019)Discourse understanding, due to the multi-sentence nature of discourse, requires consideration of larger contexts, capturing long-range dependencies, and modelling the interactions of entities. While conventional models are unable to keep information stably over long timescales, memory-augmented models are better capable of storing and accessing knowledge, making them well-suited for discourse. In this thesis, we introduce a number of methods for improving memory-augmented models to better understand discourse, validating the utility of memory and establishing a firm base for future studies to build upon.
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ItemAccurate and Efficient Discovery of Process Models from Event Logs( 2019)Everyday, organizations deliver services and products to their customers by enacting their business processes, the quality and efficiency of which directly influence the customer experience. In competitive business environments, achieving a great customer experience is fundamental to be a successful company. For this reason, companies rely on programmatic business process management in order to discover, analyse, improve, automate, and monitor their business processes. One of the core activities of business process management is process discovery. The goal of process discovery is to generate a graphical representation of a business process, namely business process model, which is then used for analysis and optimization purposes. Traditionally, process discovery has been a time-consuming activity performed either by interviewing relevant process stakeholders and employees, or by observing process participants in action, or analysing process reference documentation. However, with the diffusion of information systems, and specialised software in organizational settings, a new form of process discovery is slowly emerging, which goes by the name of automated process discovery. Automated process discovery allows business analysts to exploit process’ execution data (recorded into so-called event logs) to automatically generate process models. Discovering high-quality process models is extremely important to reduce the time spent to enhance them and avoid mistakes during process analysis. The quality of a discovered process model depends on both the input data and the automated process discovery approach (APDA) that is applied. In this thesis, we provide a systematic literature review (SLR) and benchmark of the state-of-the-art APDAs. Our SLR analyses 34 APDAs, while our benchmark evaluates six representative APDAs on more than 20 real-life datasets and seven quality measures. Our SLR and benchmark highlight that existing APDAs are affected by one (or more) of the following three limitations: (i) they achieve limited accuracy; (ii) they are computationally inefficient to be used in practice; (iii) they discover syntactically incorrect process models. To address these limitations, we propose a novel APDA, namely Split Miner, that we assessed through our benchmark. The results of our evaluation show that Split Miner outperforms the state-of-the-art APDAs over multiple quality dimensions. Most of the APDAs we assessed in our benchmark, including Split Miner, require a number of input parameters. The quality of the discovered models depends on how these parameters are tuned. We have found that automated hyper-parameters optimization leads to considerable improvements in the quality of the models produced by an APDA (including Split Miner). The quality improvement APDAs achieve via hyper-parameters optimization comes, however, at the cost of longer execution times and higher computational requirements, due to the inefficiency of existing accuracy measures for APDAs (in particular precision) and the lack of efficient solution-space exploration techniques available for APDAs. This thesis tackles the problem of APDAs optimization in two parts. First, we propose a set of accuracy measures based on Markovian abstractions, and show that our Markovian accuracy measures are faster than existing accuracy measures and fulfil a set of desirable properties that state-of-the-art measures do not. Next, we propose an optimization framework powered by single-solution-based metaheuristics, which employ our Markovian accuracy measures to efficiently explore the solutionspace of APDAs based on directly-follows graphs (DFGs), in order to discover a process model with the highest accuracy. The evaluation of our optimization framework highlights its effectiveness in optimizing DFG-based APDAs, showing that it allows APDAs to explore their solution-space beyond the boundaries of hyper-parameter optimization and most of the times in a faster manner, ultimately discovering more accurate process models in less time, compared to hyper-parameters optimization. In order to foster reproducibility and reuse, all the artifacts designed and developed for this thesis are publicly available as open-source Java command-line applications. Split Miner, our core contribution to the field of automated process discovery, has also been integrated into Apromore, an open-source business process analytics platform used by academics and practitioners worldwide.