Wil van der Aalst | RWTH Aachen University (original) (raw)
Prof.dr.ir. Wil van der Aalst is a full professor at RWTH Aachen University, leading the Process and Data Science (PADS) group. He is also the Chief Scientist at Celonis, part-time affiliated with the Fraunhofer-Institut für Angewandte Informationstechnik (FIT), and a member of the Board of Governors of Tilburg University. He also has unpaid professorship positions at Queensland University of Technology (since 2003) and the Technische Universiteit Eindhoven (TU/e). Until December 2017, he was the scientific director of the Data Science Center Eindhoven (DSC/e) and led the Architecture of Information Systems group at TU/e. Currently, he is also a distinguished fellow of Fondazione Bruno Kessler (FBK) in Trento, deputy CEO of the Internet of Production (IoP) Cluster of Excellence, co-director of the RWTH Center for Artificial Intelligence. His research interests include process mining, Petri nets, business process management, workflow automation, simulation, process modeling, and model-based analysis. Wil van der Aalst has published more than 265 journal papers, 22 books (as author or editor), 580 refereed conference/workshop publications, and 85 book chapters. Many of his papers are highly cited (he is one of the most-cited computer scientists in the world and has an H-index of 168 according to Google Scholar with over 127,000 citations), and his ideas have influenced researchers, software developers, and standardization committees working on process support. He has been a co-chair of many conferences, including the Business Process Management conference, the International Conference on Cooperative Information Systems, the International Conference on the Application and Theory of Petri Nets, the International Conference on Process Mining, and the IEEE International Conference on Services Computing. He is also editor/member of the editorial board of several journals, including Business & Information Systems Engineering, Computing, Distributed and Parallel Databases, Software and Systems Modeling, Computer Supported Cooperative Work, the International Journal of Business Process Integration and Management, the International Journal on Enterprise Modelling and Information Systems Architectures, Computers in Industry, IEEE Transactions on Services Computing, Lecture Notes in Business Information Processing, and Transactions on Petri Nets and Other Models of Concurrency. He has chaired the steering committee of IEEE Task Force on Process Mining from 2009 until 2021 and chaired the steering committee of the International Conference Series on Business Process Management from 2003 until 2017. He previously served on the advisory boards of several organizations, including Fluxicon, Celonis, ProcessGold/UiPath, and aiConomix. In 2012, he received the degree of doctor honoris causa from Hasselt University in Belgium. He also served as scientific director of the International Laboratory of Process-Aware Information Systems of the National Research University, Higher School of Economics in Moscow. In 2013, he was appointed as Distinguished University Professor of TU/e and was awarded an honorary guest professorship at Tsinghua University. In 2015, he was appointed as an honorary professor at the National Research University, Higher School of Economics in Moscow. He is also an IFIP Fellow, IEEE Fellow, ACM Fellow, and elected member of the Royal Netherlands Academy of Arts and Sciences (Koninklijke Nederlandse Akademie van Wetenschappen), Royal Holland Society of Sciences and Humanities (Koninklijke Hollandsche Maatschappij der Wetenschappen), the Academy of Europe (Academia Europaea), and the North Rhine-Westphalian Academy of Sciences, Humanities and the Arts (Nordrhein-Westfälische Akademie der Wissenschaften und der Künste). In 2018 he was awarded an Alexander-von-Humboldt Professorship, Germany’s most valuable research award (five million euros).
For more information about his work visit www.wvdaalst.com, www.processmining.org, or www.pads.rwth-aachen.de.
Address: Prof.dr.ir. Wil van der Aalst
Lehrstuhl für Informatik 9 / Process and Data Science
Ahornstr. 55, Gebäude E2, Raum 6321
RWTH Aachen University
D-52074 Aachen, Germany
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Papers by Wil van der Aalst
Digital transformation often entails small-scale changes to information systems supporting the ex... more Digital transformation often entails small-scale changes to information systems supporting the execution of business processes. These changes may increase the operational frictions in process execution, which decreases the process performance. The contributions in the literature providing support to the tracking and impact analysis of small-scale changes are limited in scope and functionality. In this paper, we use the recently developed Digital Twins of Organizations (DTOs) to assess the impact of (process-aware) information systems updates. More in detail, we model the updates using the configuration of DTOs and quantitatively assess different types of impacts of information system updates (structural, operational, and performance-related). We implemented a prototype of the proposed approach. Moreover, we discuss a case study involving a standard ERP procure-to-pay business process.
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Performance analysis in process mining aims to provide insights on the performance of a business ... more Performance analysis in process mining aims to provide insights on the performance of a business process by using a process model as a formal representation of the process. Existing techniques for performance analysis assume that a single case notion exists in a business process (e.g., a patient in healthcare process). However, in reality, different objects might interact (e.g., order, delivery, and invoice in an O2C process). In such a setting, traditional techniques may yield misleading or even incorrect insights on performance metrics such as waiting time. More importantly, by considering the interaction between objects, we can define object-centric performance metrics such as synchronization time, pooling time, and lagging time. In this work, we propose a novel approach to performance analysis considering multiple case notions by using object-centric Petri nets as formal representations of business processes. The proposed approach correctly computes existing performance metrics, while supporting the derivation of newly-introduced object-centric performance metrics. We have implemented the approach as a web application and conducted a case study based on a real-life loan application process.
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Traditional process mining techniques take event data as input where each event is associated wit... more Traditional process mining techniques take event data as input where each event is associated with exactly one object. An object represents the instantiation of a process. Object-centric event data contain events associated with multiple objects expressing the interaction of multiple processes. As traditional process mining techniques assume events associated with exactly one object, these techniques cannot be applied to object-centric event data. To use traditional process mining techniques, object-centric event data are flattened by removing all object references but one. The flattening process is lossy, leading to inaccurate features extracted from flattened data. Furthermore, the graph-like structure of object-centric event data is lost when flattening. In this paper, we introduce a general framework for extracting and encoding features from object-centric event data. We calculate features natively on the object-centric event data, leading to accurate measures. Furthermore, we provide three encodings for these features: tabular, sequential, and graph-based. While tabular and sequential encodings have been heavily used in process mining, the graph-based encoding is a new technique preserving the structure of the object-centric event data. We provide six use cases: a visualization and a prediction use case for each of the three encodings. We use explainable AI in the prediction use cases to show the utility of both the object-centric features and the structure of the sequential and graph-based encoding for a predictive model.
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Process mining relies on the ability to discover high-quality process models from event data desc... more Process mining relies on the ability to discover high-quality process models from event data describing only example behavior. Process discovery is challenging because event data only provide positive examples and process models may serve different purposes (performance analysis, compliance checking, predictive analytics, etc.). This paper focuses on the discovery of accepting Petri nets under the assumption that both the event log and process model are directlyfollows complete. Based on novel insights, two new variants (α 1.1 and α 2.0) of the well-known Alpha algorithm (α 1.0) are proposed. These variants overcome some of the limitations of the classical algorithm (e.g., dealing with short-loops and non-unique start and ending activities) and shed light on the boundaries of the "directly-follows completeness" assumption. These insights can be leveraged to create new process discovery algorithms or improve existing ones.
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Process mining employs event data extracted from different types of information systems to discov... more Process mining employs event data extracted from different types of information systems to discover and analyze actual processes. Event data often contain highly sensitive information about the people who carry out activities or the people for whom activities are performed. Therefore, privacy concerns in process mining are receiving increasing attention. To alleviate privacy-related risks, several privacy preservation techniques have been proposed. Differential privacy is one of these techniques which provides strong privacy guarantees. However, the proposed techniques presume that event data are released in only one shot, whereas business processes are continuously executed. Hence, event data are published repeatedly, resulting in additional risks. In this paper, we demonstrate that continuously released event data are not independent, and the correlation among different releases can result in privacy degradation when the same differential privacy mechanism is applied to each release. We quantify such privacy degradation in the form of temporal privacy leakages. We apply continuous event data publishing scenarios to real-life event logs to demonstrate privacy leakages.
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Process mining techniques provide insight into operational processes by systematically analyzing ... more Process mining techniques provide insight into operational processes by systematically analyzing event data generated during process execution. These insights are used to improve processes, for instance, in terms of runtime, conformity, or resource allocation. Time-based performance analysis of processes is a key use case of process mining. This paper presents the performance analysis functionality in the process mining software tool Cortado. We present novel performance analyses for block-structured process models, i.e., hierarchical structured Petri nets. By assuming block-structured models, detailed performance indicators can be calculated for each block that makes up the model. This detailed temporal information provides valuable insight into the process under study and facilitates analysts to identify optimization potential.
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A deviation detection aims to detect deviating process instances, e.g., patients in the healthcar... more A deviation detection aims to detect deviating process instances, e.g., patients in the healthcare process and products in the manufacturing process. A business process of an organization is executed in various contextual situations, e.g., a COVID-19 pandemic in the case of hospitals and a lack of semiconductor chip shortage in the case of automobile companies. Thus, context-aware deviation detection is essential to provide relevant insights. However, existing work 1) does not provide a systematic way of incorporating various contexts, 2) is tailored to a specific approach without using an extensive pool of existing deviation detection techniques, and 3) does not distinguish positive and negative contexts that justify and refute deviation, respectively. In this work, we provide a framework to bridge the aforementioned gaps. We have implemented the proposed framework as a web service that can be extended to various contexts and deviation detection methods. We have evaluated the effectiveness of the proposed framework by conducting experiments using 255 different contextual scenarios.
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In process discovery, the goal is to find, for a given event log, the model describing the underl... more In process discovery, the goal is to find, for a given event log, the model describing the underlying process. While process models can be represented in a variety of ways, Petri nets form a theoretically well-explored description language. In this paper, we present an extension of the eST-Miner process discovery algorithm. This approach computes a set of places which are considered to be fitting with respect to a user-definable fraction of the behavior described by the given event log, by evaluating all possible candidate places using token-based replay. The set of replayable traces is determined for each place in isolation, i.e., they do not need to be consistent. When combining these places into a Petri net by connecting them to the corresponding transitions, which are uniquely labeled for each activity in the event log, the resulting net can replay exactly those traces that can be replayed by each of the inserted places. Thus, inserting places without further checks may results in deadlocks and thus low fitness of the Petri net. In this paper, we explore a variant of the eST-Miner, that aims to select a subset of the discovered places such that the resulting Petri net guarantees a definable minimal fitness while maintaining high precision with respect to the input event log. Various place selection strategies are proposed and their impact on the returned Petri net is evaluated by experiments using both real and artificial event logs.
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Executing operational processes generates valuable event data in organizations' information syste... more Executing operational processes generates valuable event data in organizations' information systems. Process discovery describes the learning of process models from such event data. Incremental process discovery algorithms allow learning a process model from event data gradually. In this context, process behavior recorded in event data is incrementally fed into the discovery algorithm that integrates the added behavior to a process model under construction. In this paper, we investigate the open research question of the impact of the ordering of incrementally selected process behavior on the quality, i.e., recall and precision, of the learned process models. We propose a framework for defining ordering strategies for traces, i.e., observed process behavior, for incremental process discovery. Further, we provide concrete instantiations of this framework. We evaluate different trace-ordering strategies on real-life event data. The results show that trace-ordering strategies can significantly improve the quality of the learned process models.
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Friction in shopfloor-level manufacturing processes often occurs at the intersection of different... more Friction in shopfloor-level manufacturing processes often occurs at the intersection of different subprocesses (e. g., joining sub-parts). Therefore, considering the Digital Shadows (DSs) of individual materials/sub-parts is not sufficient when analyzing the processes. To this end, holistic views on shopfloor-level processes that integrate multiple DSs are needed. In this work, we discuss how material-centric DSs supported by discrete assembly events can be integrated using techniques from process mining. In particular, we propose to utilize DSs that contain additional structural information to overcome the main challenges of concurrency and the presence of many different objects.
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Event logs, as viewed in process mining, contain event data describing the execution of operation... more Event logs, as viewed in process mining, contain event data describing the execution of operational processes. Most process mining techniques take an event log as input and generate insights about the underlying process by analyzing the data provided. Consequently, handling large volumes of event data is essential to apply process mining successfully. Traditionally, individual process executions are considered sequentially ordered process activities. However, process executions are increasingly viewed as partially ordered activities to more accurately reflect process behavior observed in reality, such as simultaneous execution of activities. Process executions comprising partially ordered activities may contain more complex activity patterns than sequence-based process executions. This paper presents a novel query language to call up process executions from event logs containing partially ordered activities. The query language allows users to specify complex ordering relations over activities, i.e., control flow constraints. Evaluating a query for a given log returns process executions satisfying the specified constraints. We demonstrate the implementation of the query language in a process mining tool and evaluate its performance on real-life event logs.
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SAP ERP is one of the most popular information systems supporting various organizational processe... more SAP ERP is one of the most popular information systems supporting various organizational processes, e.g., O2C and P2P. However, the amount of processes and data contained in SAP ERP is enormous. Thus, the identification of the processes that are contained in a specific SAP instance, and the creation of a list of related tables is a significant challenge. Eventually, one needs to extract an event log for process mining purposes from SAP ERP. This demo paper shows the tool Interactive SAP Explorer that tackles the process identification and selection problem by encoding the relational structure of SAP ERP in a labeled property graph. Our approach allows asking complex process-related queries along with advanced representations of the relational structure.
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Process mining enables the discovery of actionable insights from event data of organizations. Pro... more Process mining enables the discovery of actionable insights from event data of organizations. Process analysis techniques typically focus on process executions at detailed, i.e., fine-grained levels, which might lead to missed insights. For instance, the relation between the waiting time of process instances and the current states of the process including resources workload is hidden at fine-grained level analysis. We propose an approach for coarse-grained diagnostics of processes while decreasing user dependency and ad hoc decisions compared to the current approaches. Our approach begins with the analysis of processes at fine-grained levels focusing on performance and compliance and proceeds with an automated translation of processes to the time series format, i.e., coarse-grained process logs. We exploit time series analysis techniques to uncover the underlying patterns and potential causes and effects in processes. The evaluation using real and synthetic event logs indicates the efficiency of our approach to discover overlooked insights at fine-grained levels.
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Conformance checking deals with collating modeled process behavior with observed process behavior... more Conformance checking deals with collating modeled process behavior with observed process behavior recorded in event data. Alignments are a state-of-the-art technique to detect, localize, and quantify deviations in process executions, i.e., traces, compared to reference process models. Alignments, however, assume complete process executions covering the entire process from start to finish or prefixes of process executions. This paper defines infix/postfix alignments, proposes approaches to their computation, and evaluates them using real-life event data.
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Among the many sources of event data available today, a prominent one is user interaction data. U... more Among the many sources of event data available today, a prominent one is user interaction data. User activity may be recorded during the use of an application or website, resulting in a type of user interaction data often called click data. An obstacle to the analysis of click data using process mining is the lack of a case identifier in the data. In this paper, we show a case and user study for event-case correlation on click data, in the context of user interaction events from a mobility sharing company. To reconstruct the case notion of the process, we apply a novel method to aggregate user interaction data in separate user sessions-interpreted as cases-based on neural networks. To validate our findings, we qualitatively discuss the impact of process mining analyses on the resulting well-formed event log through interviews with process experts.
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Process discovery aims to discover models that can explain the behaviors of event logs extracted ... more Process discovery aims to discover models that can explain the behaviors of event logs extracted from information systems. While various approaches have been proposed, only a few guarantee desirable properties such as soundness and free-choice. State-of-the-art approaches that exploit the representational bias of process trees to provide the guarantees are constrained to be block-structured. Such constructs limit the expressive power of the discovered models, i.e., only a subset of sound free-choice workow nets can be discovered. To support a more exible structural representation, we aim to discover process models that provide the same guarantees but also allow for non-block structures. Inspired by existing works that utilize synthesis rules from the free-choice nets theory, we propose an automatic approach that incrementally adds activities to an existing process model with predened patterns. Playing by the rules ensures that the resulting models are always sound and free-choice. Furthermore, the discovered models are not restricted to block structures and are thus more exible. The approach has been implemented in Python and tested using various real-life event logs. The experiments show that our approach can indeed discover models with competitive quality and more exible structures compared to the existing approach.
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Standard process discovery algorithms find a single process model that describes all traces in th... more Standard process discovery algorithms find a single process model that describes all traces in the event log from start to end as best as possible. However, when the event log contains highly diverse behavior, they fail to find a suitable model, i.e., a so-called "flower" or "spaghetti" model is returned. In these cases, discovering local process models can provide valuable information about the event log by returning multiple small process models that explain local behavior. In addition to explainability, local process models have also been used for event abstraction, trace clustering, outcome prediction, etc. Existing approaches that discover local process models do not scale well on event logs with many events or activities. Hence, in this paper, we propose a novel approach for discovering local process models composed of so-called place nets, i.e., Petri net places with the corresponding transitions. The place nets may correspond to state-or language-based regions, but do not need to. The goal however is to build multiple models, each explaining parts of the overall behavior. We also introduce different heuristics that measure the model's frequency, simplicity, and precision. The algorithm is scalable on large event logs since it needs only one pass through the event log. We implemented our approach as a ProM plugin and evaluated it on several data sets.
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Process mining enables extracting insights into human resources working in business processes and... more Process mining enables extracting insights into human resources working in business processes and supports employee management and process improvement. Often, resources from the same organizational group exhibit similar characteristics in process execution, e.g., executing the same set of process activities or participating in the same types of cases. This is a natural consequence of division of labor in organizations. These characteristics can be organized along various process dimensions, e.g., case, activity, and time, which ideally are all considered in the application of resource-oriented process mining, especially analytics of resource groups and their behavior. In this paper, we use the concept of execution context to classify cases, activities, and times to enable a precise characterization of resource groups. We propose an approach to automatically learning execution contexts from process execution data recorded in event logs, incorporating domain knowledge and discriminative information embedded in data. Evaluation using real-life event log data demonstrates the usefulness of our approach.
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Process mining uses event sequences recorded in information systems to discover and analyze the p... more Process mining uses event sequences recorded in information systems to discover and analyze the process models that generated them. Traditional process mining techniques make two assumptions that often do not find correspondence in real-life event data: First, each event sequence is assumed to be of the same type, i.e., all sequences describe an instantiation of the same process. Second, events are assumed to exclusively belong to one sequence, i.e., not being shared between different sequences. In reality, these assumptions often do not hold. Events may be shared between multiple event sequences identified by objects, and these objects may be of different types describing different subprocesses. Assuming "unshared" events and homogeneously typed objects leads to misleading insights and neglects the opportunity of discovering insights about the interplay between different objects and object types. Objectcentric process mining is the term for techniques addressing this more general problem setting of deriving process insights for event data with multiple objects. In this paper, we introduce the tool OCπ. OCπ aims to make the process behind object-centric event data transparent to the user. It does so in two ways: First, we show frequent process executions, defined and visualized as a set of event sequences of different types that share events. The frequency is determined with respect to the activity attribute, i.e., these are object-centric variants. Second, we allow the user to filter infrequent executions and activities, discovering a mainstream process model in the form of an object-centric Petri net. Our tool is freely available for download 1 .
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Process mining methods often analyze processes in terms of the individual end-to-end process runs... more Process mining methods often analyze processes in terms of the individual end-to-end process runs. Process behavior, however, may materialize as a general state of many involved process components, which can not be captured by looking at the individual process instances. A more holistic state of the process can be determined by looking at the events that occur close in time and share common process capacities. In this work, we conceptualize such behavior using high-level events and propose a new framework for detecting and logging such high-level events. The output of our method is a new high-level event log, which collects all generated high-level events together with the newly assigned event attributes: activity, case, and timestamp. Existing process mining techniques can then be applied on the produced high-level event log to obtain further insights. Experiments on both simulated and real-life event data show that our method is able to automatically discover how system-level patterns such as high traffic and workload emerge, propagate and dissolve throughout the process.
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Digital transformation often entails small-scale changes to information systems supporting the ex... more Digital transformation often entails small-scale changes to information systems supporting the execution of business processes. These changes may increase the operational frictions in process execution, which decreases the process performance. The contributions in the literature providing support to the tracking and impact analysis of small-scale changes are limited in scope and functionality. In this paper, we use the recently developed Digital Twins of Organizations (DTOs) to assess the impact of (process-aware) information systems updates. More in detail, we model the updates using the configuration of DTOs and quantitatively assess different types of impacts of information system updates (structural, operational, and performance-related). We implemented a prototype of the proposed approach. Moreover, we discuss a case study involving a standard ERP procure-to-pay business process.
Bookmarks Related papers MentionsView impact
Performance analysis in process mining aims to provide insights on the performance of a business ... more Performance analysis in process mining aims to provide insights on the performance of a business process by using a process model as a formal representation of the process. Existing techniques for performance analysis assume that a single case notion exists in a business process (e.g., a patient in healthcare process). However, in reality, different objects might interact (e.g., order, delivery, and invoice in an O2C process). In such a setting, traditional techniques may yield misleading or even incorrect insights on performance metrics such as waiting time. More importantly, by considering the interaction between objects, we can define object-centric performance metrics such as synchronization time, pooling time, and lagging time. In this work, we propose a novel approach to performance analysis considering multiple case notions by using object-centric Petri nets as formal representations of business processes. The proposed approach correctly computes existing performance metrics, while supporting the derivation of newly-introduced object-centric performance metrics. We have implemented the approach as a web application and conducted a case study based on a real-life loan application process.
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Traditional process mining techniques take event data as input where each event is associated wit... more Traditional process mining techniques take event data as input where each event is associated with exactly one object. An object represents the instantiation of a process. Object-centric event data contain events associated with multiple objects expressing the interaction of multiple processes. As traditional process mining techniques assume events associated with exactly one object, these techniques cannot be applied to object-centric event data. To use traditional process mining techniques, object-centric event data are flattened by removing all object references but one. The flattening process is lossy, leading to inaccurate features extracted from flattened data. Furthermore, the graph-like structure of object-centric event data is lost when flattening. In this paper, we introduce a general framework for extracting and encoding features from object-centric event data. We calculate features natively on the object-centric event data, leading to accurate measures. Furthermore, we provide three encodings for these features: tabular, sequential, and graph-based. While tabular and sequential encodings have been heavily used in process mining, the graph-based encoding is a new technique preserving the structure of the object-centric event data. We provide six use cases: a visualization and a prediction use case for each of the three encodings. We use explainable AI in the prediction use cases to show the utility of both the object-centric features and the structure of the sequential and graph-based encoding for a predictive model.
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Process mining relies on the ability to discover high-quality process models from event data desc... more Process mining relies on the ability to discover high-quality process models from event data describing only example behavior. Process discovery is challenging because event data only provide positive examples and process models may serve different purposes (performance analysis, compliance checking, predictive analytics, etc.). This paper focuses on the discovery of accepting Petri nets under the assumption that both the event log and process model are directlyfollows complete. Based on novel insights, two new variants (α 1.1 and α 2.0) of the well-known Alpha algorithm (α 1.0) are proposed. These variants overcome some of the limitations of the classical algorithm (e.g., dealing with short-loops and non-unique start and ending activities) and shed light on the boundaries of the "directly-follows completeness" assumption. These insights can be leveraged to create new process discovery algorithms or improve existing ones.
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Process mining employs event data extracted from different types of information systems to discov... more Process mining employs event data extracted from different types of information systems to discover and analyze actual processes. Event data often contain highly sensitive information about the people who carry out activities or the people for whom activities are performed. Therefore, privacy concerns in process mining are receiving increasing attention. To alleviate privacy-related risks, several privacy preservation techniques have been proposed. Differential privacy is one of these techniques which provides strong privacy guarantees. However, the proposed techniques presume that event data are released in only one shot, whereas business processes are continuously executed. Hence, event data are published repeatedly, resulting in additional risks. In this paper, we demonstrate that continuously released event data are not independent, and the correlation among different releases can result in privacy degradation when the same differential privacy mechanism is applied to each release. We quantify such privacy degradation in the form of temporal privacy leakages. We apply continuous event data publishing scenarios to real-life event logs to demonstrate privacy leakages.
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Process mining techniques provide insight into operational processes by systematically analyzing ... more Process mining techniques provide insight into operational processes by systematically analyzing event data generated during process execution. These insights are used to improve processes, for instance, in terms of runtime, conformity, or resource allocation. Time-based performance analysis of processes is a key use case of process mining. This paper presents the performance analysis functionality in the process mining software tool Cortado. We present novel performance analyses for block-structured process models, i.e., hierarchical structured Petri nets. By assuming block-structured models, detailed performance indicators can be calculated for each block that makes up the model. This detailed temporal information provides valuable insight into the process under study and facilitates analysts to identify optimization potential.
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A deviation detection aims to detect deviating process instances, e.g., patients in the healthcar... more A deviation detection aims to detect deviating process instances, e.g., patients in the healthcare process and products in the manufacturing process. A business process of an organization is executed in various contextual situations, e.g., a COVID-19 pandemic in the case of hospitals and a lack of semiconductor chip shortage in the case of automobile companies. Thus, context-aware deviation detection is essential to provide relevant insights. However, existing work 1) does not provide a systematic way of incorporating various contexts, 2) is tailored to a specific approach without using an extensive pool of existing deviation detection techniques, and 3) does not distinguish positive and negative contexts that justify and refute deviation, respectively. In this work, we provide a framework to bridge the aforementioned gaps. We have implemented the proposed framework as a web service that can be extended to various contexts and deviation detection methods. We have evaluated the effectiveness of the proposed framework by conducting experiments using 255 different contextual scenarios.
Bookmarks Related papers MentionsView impact
In process discovery, the goal is to find, for a given event log, the model describing the underl... more In process discovery, the goal is to find, for a given event log, the model describing the underlying process. While process models can be represented in a variety of ways, Petri nets form a theoretically well-explored description language. In this paper, we present an extension of the eST-Miner process discovery algorithm. This approach computes a set of places which are considered to be fitting with respect to a user-definable fraction of the behavior described by the given event log, by evaluating all possible candidate places using token-based replay. The set of replayable traces is determined for each place in isolation, i.e., they do not need to be consistent. When combining these places into a Petri net by connecting them to the corresponding transitions, which are uniquely labeled for each activity in the event log, the resulting net can replay exactly those traces that can be replayed by each of the inserted places. Thus, inserting places without further checks may results in deadlocks and thus low fitness of the Petri net. In this paper, we explore a variant of the eST-Miner, that aims to select a subset of the discovered places such that the resulting Petri net guarantees a definable minimal fitness while maintaining high precision with respect to the input event log. Various place selection strategies are proposed and their impact on the returned Petri net is evaluated by experiments using both real and artificial event logs.
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Executing operational processes generates valuable event data in organizations' information syste... more Executing operational processes generates valuable event data in organizations' information systems. Process discovery describes the learning of process models from such event data. Incremental process discovery algorithms allow learning a process model from event data gradually. In this context, process behavior recorded in event data is incrementally fed into the discovery algorithm that integrates the added behavior to a process model under construction. In this paper, we investigate the open research question of the impact of the ordering of incrementally selected process behavior on the quality, i.e., recall and precision, of the learned process models. We propose a framework for defining ordering strategies for traces, i.e., observed process behavior, for incremental process discovery. Further, we provide concrete instantiations of this framework. We evaluate different trace-ordering strategies on real-life event data. The results show that trace-ordering strategies can significantly improve the quality of the learned process models.
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Friction in shopfloor-level manufacturing processes often occurs at the intersection of different... more Friction in shopfloor-level manufacturing processes often occurs at the intersection of different subprocesses (e. g., joining sub-parts). Therefore, considering the Digital Shadows (DSs) of individual materials/sub-parts is not sufficient when analyzing the processes. To this end, holistic views on shopfloor-level processes that integrate multiple DSs are needed. In this work, we discuss how material-centric DSs supported by discrete assembly events can be integrated using techniques from process mining. In particular, we propose to utilize DSs that contain additional structural information to overcome the main challenges of concurrency and the presence of many different objects.
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Event logs, as viewed in process mining, contain event data describing the execution of operation... more Event logs, as viewed in process mining, contain event data describing the execution of operational processes. Most process mining techniques take an event log as input and generate insights about the underlying process by analyzing the data provided. Consequently, handling large volumes of event data is essential to apply process mining successfully. Traditionally, individual process executions are considered sequentially ordered process activities. However, process executions are increasingly viewed as partially ordered activities to more accurately reflect process behavior observed in reality, such as simultaneous execution of activities. Process executions comprising partially ordered activities may contain more complex activity patterns than sequence-based process executions. This paper presents a novel query language to call up process executions from event logs containing partially ordered activities. The query language allows users to specify complex ordering relations over activities, i.e., control flow constraints. Evaluating a query for a given log returns process executions satisfying the specified constraints. We demonstrate the implementation of the query language in a process mining tool and evaluate its performance on real-life event logs.
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SAP ERP is one of the most popular information systems supporting various organizational processe... more SAP ERP is one of the most popular information systems supporting various organizational processes, e.g., O2C and P2P. However, the amount of processes and data contained in SAP ERP is enormous. Thus, the identification of the processes that are contained in a specific SAP instance, and the creation of a list of related tables is a significant challenge. Eventually, one needs to extract an event log for process mining purposes from SAP ERP. This demo paper shows the tool Interactive SAP Explorer that tackles the process identification and selection problem by encoding the relational structure of SAP ERP in a labeled property graph. Our approach allows asking complex process-related queries along with advanced representations of the relational structure.
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Process mining enables the discovery of actionable insights from event data of organizations. Pro... more Process mining enables the discovery of actionable insights from event data of organizations. Process analysis techniques typically focus on process executions at detailed, i.e., fine-grained levels, which might lead to missed insights. For instance, the relation between the waiting time of process instances and the current states of the process including resources workload is hidden at fine-grained level analysis. We propose an approach for coarse-grained diagnostics of processes while decreasing user dependency and ad hoc decisions compared to the current approaches. Our approach begins with the analysis of processes at fine-grained levels focusing on performance and compliance and proceeds with an automated translation of processes to the time series format, i.e., coarse-grained process logs. We exploit time series analysis techniques to uncover the underlying patterns and potential causes and effects in processes. The evaluation using real and synthetic event logs indicates the efficiency of our approach to discover overlooked insights at fine-grained levels.
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Conformance checking deals with collating modeled process behavior with observed process behavior... more Conformance checking deals with collating modeled process behavior with observed process behavior recorded in event data. Alignments are a state-of-the-art technique to detect, localize, and quantify deviations in process executions, i.e., traces, compared to reference process models. Alignments, however, assume complete process executions covering the entire process from start to finish or prefixes of process executions. This paper defines infix/postfix alignments, proposes approaches to their computation, and evaluates them using real-life event data.
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Among the many sources of event data available today, a prominent one is user interaction data. U... more Among the many sources of event data available today, a prominent one is user interaction data. User activity may be recorded during the use of an application or website, resulting in a type of user interaction data often called click data. An obstacle to the analysis of click data using process mining is the lack of a case identifier in the data. In this paper, we show a case and user study for event-case correlation on click data, in the context of user interaction events from a mobility sharing company. To reconstruct the case notion of the process, we apply a novel method to aggregate user interaction data in separate user sessions-interpreted as cases-based on neural networks. To validate our findings, we qualitatively discuss the impact of process mining analyses on the resulting well-formed event log through interviews with process experts.
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Process discovery aims to discover models that can explain the behaviors of event logs extracted ... more Process discovery aims to discover models that can explain the behaviors of event logs extracted from information systems. While various approaches have been proposed, only a few guarantee desirable properties such as soundness and free-choice. State-of-the-art approaches that exploit the representational bias of process trees to provide the guarantees are constrained to be block-structured. Such constructs limit the expressive power of the discovered models, i.e., only a subset of sound free-choice workow nets can be discovered. To support a more exible structural representation, we aim to discover process models that provide the same guarantees but also allow for non-block structures. Inspired by existing works that utilize synthesis rules from the free-choice nets theory, we propose an automatic approach that incrementally adds activities to an existing process model with predened patterns. Playing by the rules ensures that the resulting models are always sound and free-choice. Furthermore, the discovered models are not restricted to block structures and are thus more exible. The approach has been implemented in Python and tested using various real-life event logs. The experiments show that our approach can indeed discover models with competitive quality and more exible structures compared to the existing approach.
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Standard process discovery algorithms find a single process model that describes all traces in th... more Standard process discovery algorithms find a single process model that describes all traces in the event log from start to end as best as possible. However, when the event log contains highly diverse behavior, they fail to find a suitable model, i.e., a so-called "flower" or "spaghetti" model is returned. In these cases, discovering local process models can provide valuable information about the event log by returning multiple small process models that explain local behavior. In addition to explainability, local process models have also been used for event abstraction, trace clustering, outcome prediction, etc. Existing approaches that discover local process models do not scale well on event logs with many events or activities. Hence, in this paper, we propose a novel approach for discovering local process models composed of so-called place nets, i.e., Petri net places with the corresponding transitions. The place nets may correspond to state-or language-based regions, but do not need to. The goal however is to build multiple models, each explaining parts of the overall behavior. We also introduce different heuristics that measure the model's frequency, simplicity, and precision. The algorithm is scalable on large event logs since it needs only one pass through the event log. We implemented our approach as a ProM plugin and evaluated it on several data sets.
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Process mining enables extracting insights into human resources working in business processes and... more Process mining enables extracting insights into human resources working in business processes and supports employee management and process improvement. Often, resources from the same organizational group exhibit similar characteristics in process execution, e.g., executing the same set of process activities or participating in the same types of cases. This is a natural consequence of division of labor in organizations. These characteristics can be organized along various process dimensions, e.g., case, activity, and time, which ideally are all considered in the application of resource-oriented process mining, especially analytics of resource groups and their behavior. In this paper, we use the concept of execution context to classify cases, activities, and times to enable a precise characterization of resource groups. We propose an approach to automatically learning execution contexts from process execution data recorded in event logs, incorporating domain knowledge and discriminative information embedded in data. Evaluation using real-life event log data demonstrates the usefulness of our approach.
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Process mining uses event sequences recorded in information systems to discover and analyze the p... more Process mining uses event sequences recorded in information systems to discover and analyze the process models that generated them. Traditional process mining techniques make two assumptions that often do not find correspondence in real-life event data: First, each event sequence is assumed to be of the same type, i.e., all sequences describe an instantiation of the same process. Second, events are assumed to exclusively belong to one sequence, i.e., not being shared between different sequences. In reality, these assumptions often do not hold. Events may be shared between multiple event sequences identified by objects, and these objects may be of different types describing different subprocesses. Assuming "unshared" events and homogeneously typed objects leads to misleading insights and neglects the opportunity of discovering insights about the interplay between different objects and object types. Objectcentric process mining is the term for techniques addressing this more general problem setting of deriving process insights for event data with multiple objects. In this paper, we introduce the tool OCπ. OCπ aims to make the process behind object-centric event data transparent to the user. It does so in two ways: First, we show frequent process executions, defined and visualized as a set of event sequences of different types that share events. The frequency is determined with respect to the activity attribute, i.e., these are object-centric variants. Second, we allow the user to filter infrequent executions and activities, discovering a mainstream process model in the form of an object-centric Petri net. Our tool is freely available for download 1 .
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Process mining methods often analyze processes in terms of the individual end-to-end process runs... more Process mining methods often analyze processes in terms of the individual end-to-end process runs. Process behavior, however, may materialize as a general state of many involved process components, which can not be captured by looking at the individual process instances. A more holistic state of the process can be determined by looking at the events that occur close in time and share common process capacities. In this work, we conceptualize such behavior using high-level events and propose a new framework for detecting and logging such high-level events. The output of our method is a new high-level event log, which collects all generated high-level events together with the newly assigned event attributes: activity, case, and timestamp. Existing process mining techniques can then be applied on the produced high-level event log to obtain further insights. Experiments on both simulated and real-life event data show that our method is able to automatically discover how system-level patterns such as high traffic and workload emerge, propagate and dissolve throughout the process.
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