Prof. Dr.
Henrik Leopold

Associate Professor for Data Science and Business Intelligence

Prof. Dr.
Henrik Leopold

Associate Professor for Data Science and Business Intelligence

Henrik Leopold is Associate Professor for Data Science and Business Intelligence. Before joining KLU in February 2019, he held positions as Assistant Professor at Vrije Universiteit Amsterdam (2015 to 2019) and WU Vienna (2014 to 2015). In 2013, he obtained his PhD degree (Dr. rer. pol.) in Information Systems from the Humboldt University of Berlin. For his thesis he received the TARGION Dissertation Award 2014 for the best doctoral thesis in the field of Information Management and the runner-up of the McKinsey Business Technology Award 2013.

In his research, Henrik Leopold is mainly concerned with the interplay between information systems and business processes. Such business processes can range from manufacturing a car to delivering a medical service to a patient. He is particularly interested in how to leverage technology from the field of artificial intelligence (such as machine learning and natural language processing) to analyze and support the execution of business processes. Outcomes of his research range from techniques for the automated analysis of process model collections to novel process mining techniques.  The results of his research have been published, among others, in the journals IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Software Engineering, Decision Support Systems, and Information Systems.

For more details about Henrik Leopold, including downloads of research articles, you can also visit his personal website at www.henrikleopold.com.

Up Close & Personal

For me, the combination of being a top research institution while maintaining a friendly, family-like atmosphere is what really sets KLU apart.”
– Prof. Dr. Henrik Leopold

Selected Publications

DOI: https://doi.org/10.1016/j.is.2022.102035 

Abstract: Process mining techniques are valuable to gain insights into and help improve (work) processes. Many of these techniques focus on the sequential order in which activities are performed. Few of these techniques consider the statistical relations within processes. In particular, existing techniques do not allow insights into how responses to an event (action) result in desired or undesired outcomes (effects). We propose and formalize the ARE miner, a novel technique that allows us to analyze and understand these action-response-effect patterns. We take a statistical approach to uncover potential dependency relations in these patterns. The goal of this research is to generate processes that are: (1) appropriately represented, and (2) effectively filtered to show meaningful relations. We evaluate the ARE miner in two ways. First, we use an artificial data set to demonstrate the effectiveness of the ARE miner compared to two traditional process-oriented approaches. Second, we apply the ARE miner to a real-world data set from a Dutch healthcare institution. We show that the ARE miner generates comprehensible representations that lead to informative insights into statistical relations between actions, responses, and effects.

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Open reference in new window "From Action to Response to Effect: Mining Statistical Relations in Work Processes"

DOI: https://doi.org/10.1016/j.is.2021.101824 

Abstract: Anomaly detection in process mining aims to recognize outlying or unexpected behavior in event logs for purposes such as the removal of noise and identification of conformance violations. Existing techniques for this task are primarily frequency-based, arguing that behavior is anomalous because it is uncommon. However, such techniques ignore the semantics of recorded events and, therefore, do not take the meaning of potential anomalies into consideration. In this work, we overcome this caveat and focus on the detection of anomalies from a semantic perspective, arguing that anomalies can be recognized when process behavior does not make sense. To achieve this, we propose an approach that exploits the natural language associated with events. Our key idea is to detect anomalous process behavior by identifying semantically inconsistent execution patterns. To detect such patterns, we first automatically extract business objects and actions from the textual labels of events. We then compare these against a process-independent knowledge base. By populating this knowledge base with patterns from various kinds of resources, our approach can be used in a range of contexts and domains. We demonstrate the capability of our approach to successfully detect semantic execution anomalies through an evaluation based on a set of real-world and synthetic event logs and show the complementary nature of semantics-based anomaly detection to existing frequency-based techniques.

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Open reference in new window "Natural Language-based Detection of Semantic Execution Anomalies in Event Logs"

DOI: 10.1016/j.dss.2020.113347 

Abstract: 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.

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Open reference in new window "Partial Order Resolution of Event Logs for Process Conformance Checking"

DOI: 10.1109/TKDE.2019.2897557 

Abstract: Conformance checking enables organizations to automatically identify compliance violations based on the analysis of observed event data. A crucial requirement for conformance-checking techniques is that observed events can be mapped to normative process models used to specify allowed behavior. Without a mapping, it is not possible to determine if an observed event trace conforms to the specification or not. A considerable problem in this regard is that establishing a mapping between events and process model activities is an inherently uncertain task. Since the use of a particular mapping directly influences the conformance of an event trace to a specification, this uncertainty represents a major issue for conformance checking. To overcome this issue, we introduce a probabilistic conformance-checking technique that can deal with uncertain mappings. Our technique avoids the need to select a single mapping by taking the entire spectrum of possible mappings into account. A quantitative evaluation demonstrates that our technique can be applied on a considerable number of real-world processes where existing conformance-checking techniques fail.

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Open reference in new window "Efficient Process Conformance Checking on the Basis of Uncertain Event-to-Activity Mappings"

DOI: 10.1016/j.is.2019.02.005 

Abstract: Many process model analysis techniques rely on the accurate analysis of the natural language contents captured in the models’ activity labels. Since these labels are typically short and diverse in terms of their grammatical style, standard natural language processing tools are not suitable to analyze them. While a dedicated technique for the analysis of process model activity labels was proposed in the past, it suffers from considerable limitations. First of all, its performance varies greatly among data sets with different characteristics and it cannot handle uncommon grammatical styles. What is more, adapting the technique requires in-depth domain knowledge. We use this paper to propose a machine learning-based technique for activity label analysis that overcomes the issues associated with this rule-based state of the art. Our technique conceptualizes activity label analysis as a tagging task based on a Hidden Markov Model. By doing so, the analysis of activity labels no longer requires the manual specification of rules. An evaluation using a collection of 15,000 activity labels demonstrates that our machine learning-based technique outperforms the state of the art in all aspects.

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Open reference in new window "Using Hidden Markov Models for the Accurate Linguistic Analysis of Process Model Activity Labels"

Academic Positions

since 2020Associate Professor of Data Science and Business Intelligence, Kühne Logistics University, Hamburg, Germany
2018-2020Assistant Professor of Data Science and Business Intelligence, Kühne Logistics University, Hamburg, Germany
2015-2018Assistant Professor (Tenure Track) + Tenured Assistant Professor (2018) at Department of Computer Science, VU University Amsterdam, The Netherlands
2014-2015Visiting Researcher at University of Mannheim, Chair of Artificial Intelligence, Mannheim, Germany
2014-2015Assistant Professor at Institute for Information Business, Vienna University of Economics and Business, Austria
2012Research Visit at UNIRIO, Department of Applied Informatics, Rio de Janeiro, Brazil
2011-2014Research Assistant at Institute of Information Systems, Humboldt University of Berlin, Berlin, Germany

Education

2013Ph.D. Information Systems (Dr. rer. pol. (summa cum laude)), Humboldt University of Berlin, Germany
2010M.Sc. Information Systems, Humboldt University of Berlin, Germany
2008B.Sc. Information Systems, Berlin School of Economics and Law (Dual study program), Germany