@conference {257,
	title = {Aligning Partially-Ordered Process-Execution Traces and Models Using Automated Planning},
	booktitle = {28th International Conference on Automated Planning and Scheduling (ICAPS 2018)},
	year = {2018},
	abstract = {Conformance checking is the problem of verifying if the actual executions of business processes, which are recorded by information systems in dedicated event logs, are compliant with a process model that encodes the process{\textquoteright} constraints. Within conformance checking, alignment-based techniques can exactly pinpoint where deviations are observed. Existing alignment-based techniques rely on the assumption of a perfect knowledge of the order with which process{\textquoteright} activities were executed in reality. However, experience shows that, due to logging errors and inaccuracies, it is not always possible to determine the exact order with which certain activities were executed. This paper illustrates an alignment-based technique where the perfect knowledge assumption of the execution{\textquoteright}s order is removed. The technique transforms the problem of alignment-based conformance checking into a planning problem encoded in PDDL, for which planners can find a correct solution in a finite amount of time. We implemented the technique as a software tool that is integrated with state-of-the-art planners. To showcase its practical relevance and scalability, we report on experiments with a real-life case study and several synthetic ones of increasing complexity.},
	keywords = {Automated Planning, Conformance Checking, PDDL, Process Mining, Trace Alignment},
	url = {https://www.aaai.org/ocs/index.php/ICAPS/ICAPS18/paper/view/17739/16951},
	author = {Massimiliano de Leoni and Giacomo Lanciano and Andrea Marrella}
}
@article {242,
	title = {Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models},
	journal = {Royal Society open science},
	volume = {4},
	year = {2017},
	pages = {160950},
	abstract = {The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.},
	doi = {https://doi.org/10.1098/rsos.160950},
	url = {https://royalsocietypublishing.org/doi/full/10.1098/rsos.160950},
	author = {Cecilia Panigutti and Tizzoni, Michele and Bajardi, Paolo and Smoreda, Zbigniew and Colizza, Vittoria}
}
