01879nas a2200181   4500008004100000245009200041210006900133520125300202653002301455653002501478653000901503653001901512653002001531100002701551700002201578700002101600856007601621       2018                        eng d00aAligning Partially-Ordered Process-Execution Traces and Models Using Automated Planning0 aAligning PartiallyOrdered ProcessExecution Traces and Models Usi3 aConformance 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' 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' 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'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.10aAutomated Planning10aConformance Checking10aPDDL10aProcess Mining10aTrace Alignment1 ade Leoni, Massimiliano1 aLanciano, Giacomo1 aMarrella, Andrea  uhttps://www.aaai.org/ocs/index.php/ICAPS/ICAPS18/paper/view/17739/1695101977nas a2200169   4500008004100000245011000041210006900151300001100220490000600231520139500237100002301632700002101655700001901676700002201695700002201717856006801739       2017                        eng d00aAssessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models0 aAssessing the use of mobile phone data to describe recurrent mob  a1609500 v43 aThe 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.1 aPanigutti, Cecilia1 aTizzoni, Michele1 aBajardi, Paolo1 aSmoreda, Zbigniew1 aColizza, Vittoria  uhttps://royalsocietypublishing.org/doi/full/10.1098/rsos.160950