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

Ontology-Driven Extraction of Event Logs from Relational Databases

\u3cp\u3eProcess mining is an emerging discipline whose aim is to discover, monitor and improve real processes by extracting knowledge from event logs representing actual process executions in a given organizational setting. In this light, it can be applied only if faithful event logs, adhering to accepted standards (such as XES), are available. In many real-world settings, though, such event logs are not explicitly given, but are instead implicitly represented inside legacy information systems of organizations, which are typically managed through relational technology. In this work, we devise a novel framework that supports domain experts in the extraction of XES event log information from legacy relational databases, and consequently enables the application of standard process mining tools on such data. Differently from previous work, the extraction is driven by a conceptual representation of the domain of interest in terms of an ontology. On the one hand, this ontology is linked to the underlying legacy data leveraging the well-established ontology-based data access (OBDA) paradigm. On the other hand, our framework allows one to enrich the ontology through user-oriented log extraction annotations, which can be flexibly used to provide different log-oriented views over the data. Different data access modes are then devised so as to view the legacy data through the lens of XES.\u3c/p\u3

Discovery of frequent episodes in event logs

Lion's share of process mining research focuses on the discovery of end-to-end process models describing the characteristic behavior of observed cases. The notion of a process instance (i.e., the case) plays an important role in process mining. Pattern mining techniques (such as frequent itemset mining, association rule learning, sequence mining, and traditional episode mining) do not consider process instances. An episode is a collection of partially ordered events. In this paper, we present a new technique (and corresponding implementation) that discovers frequently occurring episodes in event logs thereby exploiting the fact that events are associated with cases. Hence, the work can be positioned in-between process mining and pattern mining. Episode discovery has its applications in, amongst others, discovering local patterns in complex processes and conformance checking based on partial orders. We also discover episode rules to predict behavior and discover correlated behaviors in processes. We have developed a ProM plug-in that exploits efficient algorithms for the discovery of frequent episodes and episode rules. Experimental results based on real-life event logs demonstrate the feasibility and usefulness of the approach

Morphological alterations of exogenous surfactant inhibited by meconium can be prevented by dextran

BACKGROUND: Surfactant dysfunction due to inhibition is involved in the pathophysiology of meconium aspiration syndrome. Dextran addition has been shown to reverse exogenous surfactant inactivation by meconium, but the precise mechanisms and the morphological correlate of this effect are yet unknown. Morphological surfactant analysis by transmission electron microscopy (TEM) and stereology allows the differentiation of active (large aggregates = LA) and inactive (small aggregates = SA) subtypes. METHODS: To determine the in vitro effects of meconium and dextran addition on the morphology of a modified porcine natural surfactant (Curosurf), Curosurf samples were either incubated alone or together with meconium or with meconium and dextran, fixed and processed for TEM. Volume fractions of surfactant subtypes [lamellar body-like forms (LBL), multilamellar vesicles (MV), unilamellar vesicles (UV)] were determined stereologically. RESULTS: All preparations contained LBL and MV (corresponding to LA) as well as UV (corresponding to SA). The volume fraction of UV increased with addition of meconium and decreased with further addition of dextran. Correspondingly, the UV/(LBL+MV) ratio (resembling the SA/LA ratio) increased when meconium was added and decreased when dextran was added to the surfactant-meconium mixture. CONCLUSION: Meconium causes alterations in the ultrastructural composition of Curosurf that can be visualized and analyzed by TEM and stereology. These alterations resemble an increase in the SA/LA ratio and are paralleled by an increase in minimum surface tension. Dextran prevents these effects and may therefore be a useful additive to exogenous surfactant preparations to preserve their structural and functional integrity, thereby improving their resistance to inactivation

f(R) theories

Over the past decade, f(R) theories have been extensively studied as one of the simplest modifications to General Relativity. In this article we review various applications of f(R) theories to cosmology and gravity - such as inflation, dark energy, local gravity constraints, cosmological perturbations, and spherically symmetric solutions in weak and strong gravitational backgrounds. We present a number of ways to distinguish those theories from General Relativity observationally and experimentally. We also discuss the extension to other modified gravity theories such as Brans-Dicke theory and Gauss-Bonnet gravity, and address models that can satisfy both cosmological and local gravity constraints.Comment: 156 pages, 14 figures, Invited review article in Living Reviews in Relativity, Published version, Comments are welcom

Irradiation-Induced Up-Regulation of HLA-E on Macrovascular Endothelial Cells Confers Protection against Killing by Activated Natural Killer Cells

BACKGROUND: Apart from the platelet/endothelial cell adhesion molecule 1 (PECAM-1, CD31), endoglin (CD105) and a positive factor VIII-related antigen staining, human primary and immortalized macro- and microvascular endothelial cells (ECs) differ in their cell surface expression of activating and inhibitory ligands for natural killer (NK) cells. Here we comparatively study the effects of irradiation on the phenotype of ECs and their interaction with resting and activated NK cells. METHODOLOGY/PRINCIPAL FINDINGS: Primary macrovascular human umbilical vein endothelial cells (HUVECs) only express UL16 binding protein 2 (ULBP2) and the major histocompatibility complex (MHC) class I chain-related protein MIC-A (MIC-A) as activating signals for NK cells, whereas the corresponding immortalized EA.hy926 EC cell line additionally present ULBP3, membrane heat shock protein 70 (Hsp70), intercellular adhesion molecule ICAM-1 (CD54) and HLA-E. Apart from MIC-B, the immortalized human microvascular endothelial cell line HMEC, resembles the phenotype of EA.hy926. Surprisingly, primary HUVECs are more sensitive to Hsp70 peptide (TKD) plus IL-2 (TKD/IL-2)-activated NK cells than their immortalized EC counterpatrs. This finding is most likely due to the absence of the inhibitory ligand HLA-E, since the activating ligands are shared among the ECs. The co-culture of HUVECs with activated NK cells induces ICAM-1 (CD54) and HLA-E expression on the former which drops to the initial low levels (below 5%) when NK cells are removed. Sublethal irradiation of HUVECs induces similar but less pronounced effects on HUVECs. Along with these findings, irradiation also induces HLA-E expression on macrovascular ECs and this correlates with an increased resistance to killing by activated NK cells. Irradiation had no effect on HLA-E expression on microvascular ECs and the sensitivity of these cells to NK cells remained unaffected. CONCLUSION/SIGNIFICANCE: These data emphasize that an irradiation-induced, transient up-regulation of HLA-E on macrovascular ECs might confer protection against NK cell-mediated vascular injury

Axonal Transmission in the Retina Introduces a Small Dispersion of Relative Timing in the Ganglion Cell Population Response

Background: Visual stimuli elicit action potentials in tens of different retinal ganglion cells. Each ganglion cell type responds with a different latency to a given stimulus, thus transforming the high-dimensional input into a temporal neural code. The timing of the first spikes between different retinal projection neurons cells may further change along axonal transmission. The purpose of this study is to investigate if intraretinal conduction velocity leads to a synchronization or dispersion of the population signal leaving the eye. Methodology/Principal Findings: We 'imaged' the initiation and transmission of light-evoked action potentials along individual axons in the rabbit retina at micron-scale resolution using a high-density multi-transistor array. We measured unimodal conduction velocity distributions (1.3 +/- 0.3 m/sec, mean +/- SD) for axonal populations at all retinal eccentricities with the exception of the central part that contains myelinated axons. The velocity variance within each piece of retina is caused by ganglion cell types that show narrower and slightly different average velocity tuning. Ganglion cells of the same type respond with similar latency to spatially homogenous stimuli and conduct with similar velocity. For ganglion cells of different type intraretinal conduction velocity and response latency to flashed stimuli are negatively correlated, indicating that differences in first spike timing increase (up to 10 msec). Similarly, the analysis of pair-wise correlated activity in response to white-noise stimuli reveals that conduction velocity and response latency are negatively correlated. Conclusion/Significance: Intraretinal conduction does not change the relative spike timing between ganglion cells of the same type but increases spike timing differences among ganglion cells of different type. The fastest retinal ganglion cells therefore act as indicators of new stimuli for postsynaptic neurons. The intraretinal dispersion of the population activity will not be compensated by variability in extraretinal conduction times, estimated from data in the literature

Mining conditional partial order graphs from event logs

Process mining techniques rely on event logs: the extraction of a process model (discovery) takes an event log as the input, the adequacy of a process model (conformance) is checked against an event log, and the enhancement of a process model is performed by using available data in the log. Several notations and formalisms for event log representation have been proposed in the recent years to enable efficient algorithms for the aforementioned process mining problems. In this paper we show how Conditional Partial Order Graphs (CPOGs), a recently introduced formalism for compact representation of families of partial orders, can be used in the process mining field, in particular for addressing the problem of compact and easy-to-comprehend representation of event logs with data. We present algorithms for extracting both the control flow as well as the relevant data parameters from a given event log and show how CPOGs can be used for efficient and effective visualisation of the obtained results. We demonstrate that the resulting representation can be used to reveal the hidden interplay between the control and data flows of a process, thereby opening way for new process mining techniques capable of exploiting this interplay. Finally, we present open-source software support and discuss current limitations of the proposed approach.Peer ReviewedPostprint (author's final draft

Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

Process discovery methods automatically infer process models from event logs. Often, event logs contain so-called noise, e.g., infrequent outliers or recording errors, which obscure the main behavior of the process. Existing methods filter this noise based on the frequency of event labels: infrequent paths and activities are excluded. However, infrequent behavior may reveal important insights into the process. Thus, not all infrequent behavior should be considered as noise. This paper proposes the Data-aware Heuristic Miner (DHM), a process discovery method that uses the data attributes to distinguish infrequent paths from random noise by using classification techniques. Data- and control-flow of the process are discovered together. We show that the DHM is, to some degree, robust against random noise and reveals data-driven decisions, which are filtered by other discovery methods. The DHM has been successfully tested on several real-life event logs, two of which we present in this paper