Atlante digitale della Periegesi di Pausania: metodologia, implementazione e sviluppi

La tesi consiste nella descrizione del complessivo background storico-letterario, archeologico e digitale necessario per la realizzazione di un Atlante digitale dell’antica Grecia antica sulla base della raccolta e analisi dei dati e delle informazioni contenute nella Periegesi di Pausania. Grazie all’impiego degli applicativi GIS, ed in particolare di ArcGIS online, è stato possibile creare un database georiferito contenente le informazioni e le descrizioni fornite dal testo; ogni identificazione di un sito storico è stata inoltre confrontata con lo stato attuale della ricerca archeologica, al fine di produrre uno strumento innovativo tanto per a ricerca storico-archeologica quanto per lo studio e la valutazione dell’opera di Pausania. Nello specifico il lavoro consiste in primo esempio di atlante digitale interamente basato sull’interpretazione di un testo classico attraverso un processo di georeferenziazione dei suoi contenuti. Per ogni sito identificato è stata infatti specificato il relativo passo di Pausania, collegando direttamente Il dato archeologico con la fonte letteraria. Per la definizione di una tassonomia efficace per l’analisi dei contenuti dell’opera o, si è scelto di associare agli elementi descritti da Pausania sette livelli (layers) all’interno della mappa corrispondenti ad altrettante categorie generali (città, santuari extraurbani, monumenti, boschi sacri, località, corsi d’acqua, e monti). Per ciascun elemento sono state poi inserite ulteriori informazioni all’interno di una tabella descrittiva, quali: fonte, identificazione, età di appartenenza, e stato dell’identificazione.

Data Mining in Clinical Practice for the Quantification of Motor Impairment in Parkinson's Disease

Advances in biomedical signal acquisition systems for motion analysis have led to lowcost and ubiquitous wearable sensors which can be used to record movement data in different settings. This implies the potential availability of large amounts of quantitative data. It is then crucial to identify and to extract the information of clinical relevance from the large amount of available data. This quantitative and objective information can be an important aid for clinical decision making. Data mining is the process of discovering such information in databases through data processing, selection of informative data, and identification of relevant patterns. The databases considered in this thesis store motion data from wearable sensors (specifically accelerometers) and clinical information (clinical data, scores, tests). The main goal of this thesis is to develop data mining tools which can provide quantitative information to the clinician in the field of movement disorders. This thesis will focus on motor impairment in Parkinson's disease (PD). Different databases related to Parkinson subjects in different stages of the disease were considered for this thesis. Each database is characterized by the data recorded during a specific motor task performed by different groups of subjects. The data mining techniques that were used in this thesis are feature selection (a technique which was used to find relevant information and to discard useless or redundant data), classification, clustering, and regression. The aims were to identify high risk subjects for PD, characterize the differences between early PD subjects and healthy ones, characterize PD subtypes and automatically assess the severity of symptoms in the home setting.

Applicability of Process Mining Techniques in Business Environments

This thesis analyses problems related to the applicability, in business environments, of Process Mining tools and techniques. The first contribution is a presentation of the state of the art of Process Mining and a characterization of companies, in terms of their "process awareness". The work continues identifying circumstance where problems can emerge: data preparation; actual mining; and results interpretation. Other problems are the configuration of parameters by not-expert users and computational complexity. We concentrate on two possible scenarios: "batch" and "on-line" Process Mining. Concerning the batch Process Mining, we first investigated the data preparation problem and we proposed a solution for the identification of the "case-ids" whenever this field is not explicitly indicated. After that, we concentrated on problems at mining time and we propose the generalization of a well-known control-flow discovery algorithm in order to exploit non instantaneous events. The usage of interval-based recording leads to an important improvement of performance. Later on, we report our work on the parameters configuration for not-expert users. We present two approaches to select the "best" parameters configuration: one is completely autonomous; the other requires human interaction to navigate a hierarchy of candidate models. Concerning the data interpretation and results evaluation, we propose two metrics: a model-to-model and a model-to-log. Finally, we present an automatic approach for the extension of a control-flow model with social information, in order to simplify the analysis of these perspectives. The second part of this thesis deals with control-flow discovery algorithms in on-line settings. We propose a formal definition of the problem, and two baseline approaches. The actual mining algorithms proposed are two: the first is the adaptation, to the control-flow discovery problem, of a frequency counting algorithm; the second constitutes a framework of models which can be used for different kinds of streams (stationary versus evolving).