Abstract
Fleet tracking technology collects real-time information about geolocation of vehicles as well as driving-related data. This information is typically used for location monitoring as well as for analysis of routes, vehicles and drivers. From an operational point of view, the geolocation simply identifies the state of a vehicle in terms of positioning and navigation. From a management point of view, the geolocation may be used to infer the state of a vehicle in terms of process (e.g., driving, fueling, maintenance, or lunch break). Meaningful information may be extracted from these inferred states using process mining. An innovative methodology for inferring process states from geolocation data is proposed in this paper. Also, it is presented the potential of applying process mining techniques on geolocation data for process discovery. © 2020 The Authors. Published by Elsevier B.V.

Abstract
Machine Learning algorithms are often too complex to be studied from a purely analytical point of view. Alternatively, with a reasonably large number of datasets one can empirically observe the behavior of a given algorithm in different conditions and hypothesize some general characteristics. This knowledge about algorithms can be used to choose the most appropriate one given a new dataset. This very hard problem can be approached using metalearning. Unfortunately, the number of datasets available may not be sufficient to obtain reliable meta-knowledge. Additionally, datasets may change with time, by growing, shrinking and editing, due to natural actions like people buying in a e-commerce site. In this paper we propose dataset morphing as the basis of a novel methodology that can help overcome these drawbacks and can be used to better understand ML algorithms. It consists of manipulating real datasets through the iterative application of gradual transformations (morphing) and by observing the changes in the behavior of learning algorithms while relating these changes with changes in the meta features of the morphed datasets. Although dataset morphing can be envisaged in a much wider framework, we focus on one very specific instance: the study of collaborative filtering algorithms on binary data. Results show that the proposed approach is feasible and that it can be used to identify useful metafeatures to predict the best collaborative filtering algorithm for a given dataset. © Springer Nature Switzerland AG 2019.

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