Pollination ecology and reproductive success in isolated populations of flowering plants: Primula apennina Widmer, Dictamnus albus L. and Convolvulus lineatus L.

This research focuses on reproductive biology and pollination ecology of entomophilous angiosperms, with particular concern to reproductive success in small and isolated populations of species that occur at their distribution limits or are endemic. I considered three perennial herbs as model species: Primula apennina Widmer, Dictamnus albus L. and Convolvulus lineatus L. I carried out field work on natural populations and performed laboratory analyses on specific critical aspects (resource allocation, pollen viability, stigmatic receptivity, physiological self-incompatibility, seed viability), through which I analysed different aspects related to plant fitness, such as production of viable seed, demographic structure of populations, type and efficiency of plant-pollinator system, and limiting factors.

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).