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Process performance indicators (PPIs) are metrics to quantify the degree with which organizational goals defined based on business processes are fulfilled. They exploit the event logs recorded by information systems during the execution of business processes, thereby providing a basis for process monitoring and subsequent optimization. However, PPIs are often evaluated on processes that involve individuals, which implies an inevitable risk of privacy intrusion. In this paper, we address the demand for privacy protection in the computation of PPIs. We first present a framework that enforces control over the data exploited for process monitoring. We then show how PPIs defined based on the established PPINOT meta-model are instantiated in this framework through a set of data release mechanisms. These mechanisms are designed to provide provable guarantees in terms of differential privacy. We evaluate our framework and the release mechanisms in a series of controlled experiments. We further use a public event log to compare our framework with approaches based on privatization of event logs. The results demonstrate feasibility and shed light on the trade-offs between data utility and privacy guarantees in the computation of PPIs.

Event logs recorded during the execution of business processes constitute a valuable source of information. Applying process mining techniques to them, event logs may reveal the actual process execution and enable reasoning on quantitative or qualitative process properties. However, event logs often contain sensitive information that could be related to individual process stakeholders through background information and cross-correlation. We therefore argue that, when publishing event logs, the risk of such re-identification attacks must be considered. In this paper, we show how to quantify the re-identification risk with measures for the individual uniqueness in event logs. We also report on a large-scale study that explored the individual uniqueness in a collection of publicly available event logs. Our results suggest that potentially up to all of the cases in an event log may be re-identified, which highlights the importance of privacy-preserving techniques in process mining., Comment: Accepted to CAiSE-2020

Die Adipositas ist ein weltweit rasant zunehmendes Krankheitsbild. Die bariatrische Chirurgie ermöglicht es, Körpergewicht und Masse des weißen Fettgewebes nachhaltig zu reduzieren und das gesundheitliche Risiko Adipositas-assoziierter Komorbiditäten zu senken. Einhergehend mit dem Gewichtsverlust der Patienten nach bariatrischer Operation, verändern sich außerdem die Konzentrationen der vom Fettgewebe freigesetzten Adipokine. Diese spiegeln die durch den Eingriff angestoßenen metabolischen Veränderungen wider und sind Ausdruck von Wachstum und Reduktion des Fettgewebes. Zwei wichtige Adipokine sind VEGF-A und Apelin-12. VEGF-A ist ein Angiogenese-stimulierendes Adipokin, welches die Gefäßneubildung im wachsenden Fettgewebe fördert. Adipositas scheint stark mit erhöhten Serumkonzentrationen dieses Peptidhormons verbunden zu sein. Zudem ist VEGF-A an der Angiogenese wachsender Tumore beteiligt. Apelin-12 ist ein Peptidhormon, welches eine inhibierende Wirkung auf die Adipogense besitzt. Es könnte ein Gegenspieler des VEGF sein. Ziel dieser Arbeit ist es zu untersuchen, ob die bariatrische Chirurgie direkten Einfluss auf die Serumkonzentrationen von VEGF und Apelin-12 ausübt. Darüber hinaus wird analysiert, ob die Änderung der Serum-Adipokin-Konzentrationen mit der des Patienten-BMI korreliert. Auf Grundlage der in-vitro-Adipogenese von LiSa-2-Zellen wird ermittelt, welchen Einfluss in-vitro Adipokine auf die Adipogenese haben. Abschließend soll beurteilt werden, ob die Adipokine VEGF-A und Apelin-12 im Serum adipöser Patienten geeignete Parameter sind, um den Erfolg der bariatrischen Chirurgie zu evaluieren. 30 morbid adipöse Patienten, welche bariatrisch operiert wurden, sind in diese Studie eingeschlossen. Sie erhielten entweder eine Magenbypass-Operation oder eine Schlauchmagen-Operation. Die Blutserumproben wurden präoperativ sowie 6, 12 und 24 Monate nach Operation akquiriert. Die Konzentrationen von VEGF-A und Apelin-12 wurden mithilfe eines spezifischen ELISAs ermittelt. Die Wirkung des VEGF-A, eines VEGF-Inhibitors sowie des Apelin-12 auf die Adipozytendifferenzierung wurde durch in-vitro-Adipogenese von Liposarkomzellen (LiSa-2) anhand von Western Blots und Oil-Red-O-Adipozyten-Färbungen analysiert. Nach bariatrischer Operation sinkt die VEGF-A-Konzentration im Serum der Patienten signifikant, während die Serumkonzentration des Apelin-12 signifikant ansteigt. Der Body Mass Index (BMI) nimmt postoperativ signifikant ab. Es besteht ein signifikant positiver Zusammenhang zwischen VEGF-A und dem BMI, während Apelin-12 keinen signifikanten Zusammenhang mit dem BMI aufweist. Es zeigte sich sowohl in Western Blots, als auch Oil-Red-O-Färbungen ein stimulierender Einfluss von VEGF-A auf die in-vitro-Adipogenese. Apelin-12 zeigt ebenso wie ein VEGF-A-Inhibitor einen hemmenden Einfluss auf die Adipogenese. Die aktuelle Studienlage deutet an, dass die Zunahme des Serum-Apelin-12 mit einem Rückgang von Insulinresistenz und einer Konsolidierung des Diabetes mellitus Typ 2 bei Adipositas assoziiert ist. In Zusammenschau scheint es sinnvoll, Serum-VEGF-A in die Evaluierung des Erfolgs der bariatrischen Chirurgie mit einzubeziehen., Obesity rapidly becomes more prevalent worldwide. Bariatric surgery sustainably reduces bodyweight and mass of white fat tissue and decreases risk of obesity-associated comorbidities. The patient’s weight loss after bariatric surgery is accompanied by dramatic changes in the concentrations of adipokines secreted by fat tissue, reflecting metabolic changes including the growth and reduction of fat tissue. Two important adipokines are VEGF-A and apelin-12. VEGF-A stimulates angiogenesis, supporting the formation of blood vessels in expanding fat tissue. Obesity seems to be strongly correlated with elevated serum levels of VEGF-A. Moreover, VEGF-A is participating in the blood vessel growth in tumours. Apelin-12 is an adipokine that has an inhibiting effect on adipogenesis. It could possibly be an opponent of VEGF-A. Aim of this study is to analyse, if bariatric surgery directly influences serum concentrations of VEGF-A and apelin-12 in obese patients. Additionally, it is analysed if serum concentrations of VEGF-A and apelin-12 correlate with the change in patient’s post-surgical BMI. Based on in-vitro-adipogenesis of LiSa-2 cells, we look at the influence of these peptide hormones on the adipocyte differentiation. Finally, it is assessed if the adipokines VEGF-A and apelin-12 in the serum of obese patients are suitable parameters to evaluate the success of bariatric surgery. 30 morbidly obese patients who received bariatric surgery were examined. They either underwent gastric bypass surgery or a sleeve gastrectomy. Blood samples were obtained preoperatively and 6, 12 and 24 months after surgery. Concentrations of VEGF-A and apelin-12 were measured with a specific ELISA for each hormone respectively. The effect of VEGF-A, a VEGF-Inhibitor and apelin-12 on adipocyte differentiation was analysed by in-vitro-adipogenesis of liposarcoma cells (LiSa-2) based on western blots and oil-red-o staining. After bariatric surgery VEGF-A concentrations decreased significantly. Serum concentrations of apelin-12 significantly increased. Post-surgery, the body mass index (BMI) dropped significantly. A significant and positive correlation between BMI and VEGF-A was found whereas apelin-12 and BMI show no significant correlation. In western blots as well as oil-red-o staining a stimulating effect of VEGF-A on in-vitro-adipogenesis became evident. Apelin-12, as much as a VEGF-A inhibitor show an inhibiting influence on adipogenesis. An increase of serum apelin-12 is associated with a decline in insulin resistance and a consolidation of Diabetes mellitus Type 2 in obesity but further research is necessary. Considering the risks associated with elevated serum VEGF-A it seems reasonable to include this peptide hormone into the success evaluation of bariatric surgery.

While supporting the execution of business processes, information systems record event logs. Conformance checking relies on these logs to analyze whether the recorded behavior of a process conforms to the behavior of a normative specification. A key assumption of existing conformance checking techniques, however, is that all events are associated with timestamps that allow to infer a total order of events per process instance. Unfortunately, this assumption is often violated in practice. Due to synchronization issues, manual event recordings, or data corruption, events are only partially ordered. In this paper, we put forward the problem of partial order resolution of event logs to close this gap. It refers to the construction of a probability distribution over all possible total orders of events of an instance. To cope with the order uncertainty in real-world data, we present several estimators for this task, incorporating different notions of behavioral abstraction. Moreover, to reduce the runtime of conformance checking based on partial order resolution, we introduce an approximation method that comes with a bounded error in terms of accuracy. Our experiments with real-world and synthetic data reveal that our approach improves accuracy over the state-of-the-art considerably., Comment: Accepted for publication in Decision Support Systems

Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to large graphs. We therefore propose a framework for parallel computation of a graph embedding using a cluster of compute nodes with resource constraints. We show how to distribute any existing embedding technique by first splitting a graph for any given set of constrained compute nodes and then reconciling the embedding spaces derived for these subgraphs. We also propose a new way to evaluate the quality of graph embeddings that is independent of a specific inference task. Based thereon, we give a formal bound on the difference between the embeddings derived by centralised and parallel computation. Experimental results illustrate that our approach for parallel computation scales well, while largely maintaining the embedding quality.

A lot of recent literature on outcome-oriented predictive process monitoring focuses on using models from machine and deep learning. In this literature, it is assumed the outcome labels of the historical cases are all known. However, in some cases, the labelling of cases is incomplete or inaccurate. For instance, you might only observe negative customer feedback, fraudulent cases might remain unnoticed. These cases are typically present in the so-called positive and unlabelled (PU) setting, where your data set consists of a couple of positively labelled examples and examples which do not have a positive label, but might still be examples of a positive outcome. In this work, we show, using a selection of event logs from the literature, the negative impact of mislabelling cases as negative, more specifically when using XGBoost and LSTM neural networks. Furthermore, we show promising results on real-life datasets mitigating this effect, by changing the loss function used by a set of models during training to those of unbiased Positive-Unlabelled (uPU) or non-negative Positive-Unlabelled (nnPU) learning. ispartof: pages:255-268 ispartof: Process Mining Workshops vol:468 pages:255-268 ispartof: 4th International Conference on Process Mining (ICPM) location:ITALY, Free Univ Bozen Bolzano, Bolzano date:23 Oct - 28 Oct 2022 status: published

In today’s expansion of new technologies, innovation is found necessary for organizations to be up to date with the latest management trends. Although organizations are increasingly using new technologies, opportunities still exist to achieve the nowadays essential omnichannel management strategy. More precisely, social media are opening a path for benefiting more from an organization’s process orientation. However, social media strategies are still an under-investigated field, especially when it comes to the research of social media use for the management and improvement of business processes or the internal way of working in organizations. By classifying a variety of articles, this study explores the evolution of social media implementation within the BPM discipline. We also provide avenues for future research and strategic implications for practitioners to use social media more comprehensively.

Business processes are prone to continuous and unexpected changes. Process workers may start executing a process differently in order to adjust to changes in workload, season, guidelines or regulations for example. Early detection of business process changes based on their event logs --- also known as business process drift detection --- enables analysts to identify and act upon changes that may otherwise affect process performance. Previous methods for business process drift detection are based on an exploration of a potentially large feature space and in some cases they require users to manually identify the specific features that characterize the drift. Depending on the explored feature set, these methods may miss certain types of changes. This paper proposes a fully automated and statistically grounded method for detecting process drift. The core idea is to perform statistical tests over the distributions of runs observed in two consecutive time windows. By adaptively sizing the window, the method strikes a trade-off between classification accuracy and drift detection delay. A validation on synthetic and real-life logs shows that the method accurately detects typical change patterns and scales up to the extent that it works for online drift detection.