Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence

Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a "picture" not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining approaches described in the literature produce such models. This is in stark contrast with the over 25 available commercial process mining tools that only discover informal process models that remain deliberately vague on the precise set of possible traces. There are two main reasons why vendors resort to such models: scalability and simplicity. In this paper, we propose to combine the best of both worlds: discovering hybrid process models that have formal and informal elements. As a proof of concept we present a discovery technique based on hybrid Petri nets. These models allow for formal reasoning, but also reveal information that cannot be captured in mainstream formal models. A novel discovery algorithm returning hybrid Petri nets has been implemented in ProM and has been applied to several real-life event logs. The results clearly demonstrate the advantages of remaining "vague" when there is not enough "evidence" in the data or standard modeling constructs do not "fit". Moreover, the approach is scalable enough to be incorporated in industrial-strength process mining tools.Comment: 25 pages, 12 figure

XES Software Communication Extension

During the execution of software, execution data can be recorded. With the development of process mining techniques on the one hand, and the growing availability of software execution data on the other hand, a new form of software analytics comes into reach. That is, applying process mining techniques to analyze software execution data. To enable process mining for software, event logs should be capable of capturing software-specific data.In the context of multi-process and distributed software, there are multiplesoftware applications interacting and communicating with each other. TheSoft-ware Communication extension supports recording IP-based communication for relating events across software applications.<br/

Optimized laser pulse profile for efficient radiation pressure acceleration of ions

The radiation pressure acceleration regime of laser ion acceleration requires high intensity laser pulses to function efficiently. Moreover the foil should be opaque for incident radiation during the interaction to ensure maximum momentum transfer from the pulse to the foil, which requires proper matching of the target to the laser pulse. However, in the ultrarelativistic regime, this leads to large acceleration distances, over which the high laser intensity for a Gaussian laser pulse must be maintained. It is shown that proper tailoring of the laser pulse profile can significantly reduce the acceleration distance, leading to a compact laser ion accelerator, requiring less energy to operate.Comment: 10 pages, 4 figure

Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)

This extended paper presents 1) a novel hierarchy and recursion extension to the process tree model; and 2) the first, recursion aware process model discovery technique that leverages hierarchical information in event logs, typically available for software systems. This technique allows us to analyze the operational processes of software systems under real-life conditions at multiple levels of granularity. The work can be positioned in-between reverse engineering and process mining. An implementation of the proposed approach is available as a ProM plugin. Experimental results based on real-life (software) event logs demonstrate the feasibility and usefulness of the approach and show the huge potential to speed up discovery by exploiting the available hierarchy.Comment: Extended version (14 pages total) of the paper Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis. This Technical Report version includes the guarantee proofs for the proposed discovery algorithm

Mapping the lifelines: how the design of infrastructure networks impacts on transformation in dispersed territories

Besides compact cities, Western Europe is characterised by low-density dispersion, resulting in a landscape with elements of both city and land. These dispersed territories offer an alternative to a traditional urban–rural dichotomy framework and have been put forward as twenty-first-century cities. However, these territories are currently facing urgent and complex socio-economic and ecological challenges. One such territory is the Eurometropolis Lille–Kortrijk–Tournai, a transnational region on the border of Belgium and France. The hypothesis is that the evolution of the Eurometropolis territory is closely intertwined with its infrastructure networks. The structure of this article is threefold. First, it describes the non-binary condition in which the Eurometropolis is situated. Second, it analyses the evolution of infrastructure networks in the Eurometropolis from the late eighteenth century to today through case studies. Third, it highlights the potential future role of infrastructure networks in providing answers to large-scale challenges. The research presented in this article demonstrates that transformation in dispersed territories is closely related to the evolution of their infrastructure networks. Moreover, infrastructure – such as waterways, railways and roads – has enabled an urban condition without urban form in the Eurometropolis dispersed territories. In the light of these findings, the article shows that the inherent nature of dispersed territories can be influenced by rethinking these infrastructures to proactively address the collective challenges at stake

Fast and accurate Slicewise OutLIer Detection (SOLID) with informed model estimation for diffusion MRI data

The accurate characterization of the diffusion process in tissue using diffusion MRI is greatly challenged by the presence of artefacts. Subject motion causes not only spatial misalignments between diffusion weighted images, but often also slicewise signal intensity errors. Voxelwise robust model estimation is commonly used to exclude intensity errors as outliers. Slicewise outliers, however, become distributed over multiple adjacent slices after image registration and transformation. This challenges outlier detection with voxelwise procedures due to partial volume effects. Detecting the outlier slices before any transformations are applied to diffusion weighted images is therefore required. In this work, we present i) an automated tool coined SOLID for slicewise outlier detection prior to geometrical image transformation, and ii) a framework to naturally interpret data uncertainty information from SOLID and include it as such in model estimators. SOLID uses a straightforward intensity metric, is independent of the choice of the diffusion MRI model, and can handle datasets with a few or irregularly distributed gradient directions. The SOLID-informed estimation framework prevents the need to completely reject diffusion weighted images or individual voxel measurements by downweighting measurements with their degree of uncertainty, thereby supporting convergence and well-conditioning of iterative estimation algorithms. In comprehensive simulation experiments, SOLID detects outliers with a high sensitivity and specificity, and can achieve higher or at least similar sensitivity and specificity compared to other tools that are based on more complex and time-consuming procedures for the scenarios investigated. SOLID was further validated on data from 54 neonatal subjects which were visually inspected for outlier slices with the interactive tool developed as part of this study, showing its potential to quickly highlight problematic volumes and slices in large population studies. The informed model estimation framework was evaluated both in simulations and in vivo human data.Peer reviewe