BINet: Multi-perspective Business Process Anomaly Classification

In this paper, we introduce BINet, a neural network architecture for real-time multi-perspective anomaly detection in business process event logs. BINet is designed to handle both the control flow and the data perspective of a business process. Additionally, we propose a set of heuristics for setting the threshold of an anomaly detection algorithm automatically. We demonstrate that BINet can be used to detect anomalies in event logs not only on a case level but also on event attribute level. Finally, we demonstrate that a simple set of rules can be used to utilize the output of BINet for anomaly classification. We compare BINet to eight other state-of-the-art anomaly detection algorithms and evaluate their performance on an elaborate data corpus of 29 synthetic and 15 real-life event logs. BINet outperforms all other methods both on the synthetic as well as on the real-life datasets

Analyzing Business Process Anomalies Using Autoencoders

Businesses are naturally interested in detecting anomalies in their internal processes, because these can be indicators for fraud and inefficiencies. Within the domain of business intelligence, classic anomaly detection is not very frequently researched. In this paper, we propose a method, using autoencoders, for detecting and analyzing anomalies occurring in the execution of a business process. Our method does not rely on any prior knowledge about the process and can be trained on a noisy dataset already containing the anomalies. We demonstrate its effectiveness by evaluating it on 700 different datasets and testing its performance against three state-of-the-art anomaly detection methods. This paper is an extension of our previous work from 2016 [30]. Compared to the original publication we have further refined the approach in terms of performance and conducted an elaborate evaluation on more sophisticated datasets including real-life event logs from the Business Process Intelligence Challenges of 2012 and 2017. In our experiments our approach reached an F1 score of 0.87, whereas the best unaltered state-of-the-art approach reached an F1 score of 0.72. Furthermore, our approach can be used to analyze the detected anomalies in terms of which event within one execution of the process causes the anomaly.Comment: 20 pages, 5 figure

Analyzing Business Process Anomalies Using Autoencoders

Businesses are naturally interested in detecting anomalies in their internal processes, because these can be indicators for fraud and inefficiencies. Within the domain of business intelligence, classic anomaly detection is not very frequently researched. In this paper, we propose a method, using autoencoders, for detecting and analyzing anomalies occurring in the execution of a business process. Our method does not rely on any prior knowledge about the process and can be trained on a noisy dataset already containing the anomalies. We demonstrate its effectiveness by evaluating it on 700 different datasets and testing its performance against three state-of-the-art anomaly detection methods. This paper is an extension of our previous work from 2016 [30]. Compared to the original publication we have further refined the approach in terms of performance and conducted an elaborate evaluation on more sophisticated datasets including real-life event logs from the Business Process Intelligence Challenges of 2012 and 2017. In our experiments our approach reached an F1 score of 0.87, whereas the best unaltered state-of-the-art approach reached an F1 score of 0.72. Furthermore, our approach can be used to analyze the detected anomalies in terms of which event within one execution of the process causes the anomaly.Comment: 20 pages, 5 figure

BASIS: A workshop for psycho-social basis competences for the teaching profession

An verschiedenen Universitäten werden seit einigen Jahren Diagnose- und Beratungsverfahren entwickelt, um bereits zu frühen Zeitpunkten in der Lehramtsausbildung zu prüfen, ob die angehenden Lehrer die notwendigen persönlichen Voraussetzungen mitbringen. Der vorliegende Artikel stellt das Kasseler Modell "BASIS: Psychosoziale Basiskompetenzen für den Lehrerberuf" dar, welches seit 2008 als verpflichtendes Kompaktseminar in Kleingruppen durchgeführt wird, das von allen Lehramtsstudierenden an der Universität Kassel im ersten Studienjahr belegt wird. BASIS bietet ein Erfahrungsfeld, welches Studierenden die realitätsnahe Selbsteinschätzung ihrer Kompetenzen, die Reflexion ihrer motivationalen Grundlagen und die Formulierung von Lernzielen ermöglicht. Nach einer theoretischen Begründung des Konzepts werden praktische Erfahrungen und Ergebnisse aus der empirischen Begleitstudie berichtet. Es finden sich Hinweise dafür, dass für die Beurteilung der Passung einer Person zum Lehrerberuf nicht nur bestimmte - bereits vorhandene - Kompetenzausprägungen relevant sind, sondern auch die Ausprägung einer als Lernorientierung bezeichneten Haltung gegenüber Lernsituationen, wie sie z. B. in dem Kasseler Modell BASIS realisiert werden. (DIPF/Orig.)In the last years different universities have developed specific diagnosis and counseling tools for teacher students in order to examine at a very early stage of the students\u27 academic qualification if they meet the necessary personal requirements. This article describes the Kassel Model "BASIS: Psycho-social basis competences for the teaching profession". The topic is dealt with in small groups in compulsory workshops for all teacher students in their first year at the University of Kassel. BASIS provides a setting which enables the students to estimate their competences on a realistic basis, to reflect their motivational basis and to clarify their learning goals. After theoretically explaining the concept, practical experience and results from an evaluation study are discussed. The results point to relevance of a parameter called learning orientation which is important for the appraisal of the development of the person-profession fit besides and above the already achieved competence level. (DIPF/Orig.

Process Learning for Autonomous Process Anomaly Correction

The automatic detection of divergences from a desired process behavior is a common research topic in the business process management community. An established technique to analyze processes is called conformance checking. Given a definition of a process in form of a process model, conformance checking can be used to test whether the executions of a process contained in a so-called event log data structure are conforming with the process as it was defined. The result is a comparison of the execution traces and their respective correct execution, according to the process model. This technique provides insights into where the divergence has occurred and how the execution must be altered to conform to the process model. However, a problem is that it requires a process model to be available. Process models in the correct format are not always available. Contrary to conformance checking, process anomaly detection aims to find anomalous executions without relying on a predefined process model. A process anomaly detection algorithm derives the process logic from the event log itself and exploits the patterns found within the event log to distinguish normal from anomalous process executions. Though process anomaly detection provides the benefit of not relying on a process model, it typically does not provide the level of detail that conformance checking does. A process execution can either be normal or it can be anomalous. This dissertation proposes process anomaly correction, a novel approach that combines the benefits of conformance checking and process anomaly detection. Given only an event log, process anomaly correction detects anomalous executions, clearly indicates where the anomaly has occurred during the execution and suggests possible corrective measures. The solution presented in this work is based on a new concept to the field of process anomaly detection: Process learning. In process learning, the task of understanding the process based on the example data is transformed into a learning problem in which a neural network is trained to predict the very next activity in a running process execution. The resulting machine learning model thus represents an approximation of the real process that created the data. This cumulative dissertation consists of five contributions to the field of business process management that demonstrate how, starting from a process anomaly detection, process anomaly correction is achieved in a series of four steps. (1) Process learning is employed to generate an approximated model of the process logic. (2) The limitation of only distinguishing between normal and anomalous process executions is overcome by employing the process learning model which processes the process executions on a finer level of detail than existing approaches. (3) The necessity of providing manual threshold settings, as it is typical for process anomaly detection algorithms, is replaced by an automatic parameterization utilizing the process learning model. (4) The predictive capabilities of the process learning model are exploited to generate possible corrections of detected anomalies. The resulting process anomaly correction approach can be employed in scenarios where classic conformance checking would be infeasible, due to the restriction of relying on a process model. Furthermore, it can be employed alongside classical conformance checking, for it incorporates more information coming from the event log than classical conformance checking (such as employees executing a process step, in which country the process is executed, etc.), and thus provides a new perspective for the process analyst

BINet: Multivariate Business Process Anomaly Detection Using Deep Learning

In this paper, we propose BINet, a neural network architecture for real-time multivariate anomaly detection in business process event logs. BINet has been designed to handle both the control flow and the data perspective of a business process. Additionally, we propose a heuristic for setting the threshold of an anomaly detection algorithm automatically. We demonstrate that BINet can be used to detect anomalies in event logs not only on a case level, but also on event attribute level. We compare BINet to 6 other state-of-the-art anomaly detection algorithms and evaluate their performance on an elaborate data corpus of 60 synthetic and 21 real life event logs using artificial anomalies. BINet reached an average F₁ score over all detection levels of 0.83, whereas the next best approach, a denoising autoencoder, reached only 0.74. This F₁ score is calculated over two different levels of detection, namely case and attribute level. BINet reached 0.84 on case and 0.82 on attribute level, whereas the next best approach reached 0.78 and 0.71 respectively

Unsupervised Anomaly Detection in Noisy Business Process Event Logs Using Denoising Autoencoders

Business processes are prone to subtle changes over time, as unwanted behavior slowly manifests in the execution flow. This problem is related to anomaly detection, as these subtle changes start of as anomalies at first, and thus it is important to detect them early. However, the necessary process documentation is often outdated, and thus not usable. Moreover, the only way of analyzing a process in execution is the use of event logs coming from process-aware information systems, but these event logs already contain anomalous behavior and other sorts of noise. Classic process anomaly detection algorithms require a dataset that is free of anomalies; thus, they are unable to process the noisy event logs. Within this paper we propose a system, relying on neural network technology, that is able to deal with the noise in the event log and learn a representation of the underlying model, and thus detect anomalous behavior based on this representation. We evaluate our approach on five different event logs, coming from process models with different complexities, and demonstrate that our approach yields remarkable results of 97.2 percent F1-score in detecting anomalous traces in the event log, and 95.6 percent accuracy in detecting the respective anomalous activities within the traces

ProcessExplorer: An Interactive Visual Recommendation System for Process Mining

More and more business process data is collected by organizations to analyze and optimize their process performance. As a consequence it is particularly challenging to locate possible process issues or potential optimizations using process mining. Process mining aims at analyzing the actual usage of information systems by reconstructing a process model from recorded event log. However, such large amount of data often leads to spaghetti-like visualizations which are in-comprehensive and inaccurate. This paper addresses this issue by introducing an unsupervised visual recommender system for process analysis. The system provides suggestions during the interactive visual inspection of the discovered process model by recommending points of interests (e.g., long duration times or exceptional process behavior) ranked by severity. For calculated interest points we characterize the deviation from the average behavior as well as compute the effect the observed conspicuousness has. Our approach has been implemented as a ProM plugin. We evaluate our approach by presenting a case study using a real life event log

Detecting Concept Drift in Processes using Graph Metrics on Process Graphs

Work in organisations is often structured into business processes, implemented using process-aware information systems (PAISs). These systems aim to enforce employees to perform work in a certain way, executing tasks in a specified order. However, the execution strategy may change over time, leading to expected and unexpected changes in the overall process. Especially the unexpected changes may manifest without notice, which can have a big impact on the performance, costs, and compliance. Thus it is important to detect these hidden changes early in order to prevent monetary consequences. Traditional process mining techniques are unable to identify these execution changes because they usually generalise without considering time as an extra dimension, and assume stable processes. Most algorithms only produce a single process model, reflecting the behaviour of the complete analysis scope. Small changes cannot be identified as they only occur in a small part of the event log. This paper proposes a method to detect process drifts by performing statistical tests on graph metrics calculated from discovered process models. Using process models allows to additionally gather details about the structure of the drift to answer the question which changes were made to the process