Sander J. J. Leemans's research while affiliated with RWTH Aachen University and other places

Publications (58)

Article
Full-text available
The field of process mining focuses on distilling knowledge of the (historical) execution of a process based on the operational event data generated and stored during its execution. Most existing process mining techniques assume that the event data describe activity executions as degenerate time intervals, i.e., intervals of the form [t, t], yieldi...
Conference Paper
Full-text available
The continuous optimization of business processes remains a critical success factor for companies. The assisted business process redesign (aBPR) concept guides users in improving business processes based on redesign patterns. Depending on the process data at hand, it generates four types of recommendations that differ in their level of automation....
Conference Paper
Full-text available
Process mining – a suite of techniques for extracting insights from event logs of Information Systems (IS) – is increasingly being used by a wide range of organisations to improve operational efficiency. However, despite extensive studies of Critical Success Factors (CSF) in related domains, CSF studies of process mining are limited. Moreover, thes...
Chapter
One of the goals of process discovery is to construct, from a given event log, a process model which correctly represents the underlying system. As with any abstraction, one does not necessarily want to represent all possible behavior, but only the significant behavior. While various discovery algorithms support this use case of discovering the sig...
Chapter
Interest in stochastic models for business processes has been revived in a recent series of studies on uncertainty in process models and event logs, with corresponding process mining techniques. In this context, variants of stochastic labelled Petri nets, that is with duplicate labels and silent transitions, have been employed as a reference model....
Article
Process mining is a well-established discipline with applications in many industry sectors, including healthcare. To date, few publications have considered the context in which processes execute. Little consideration has been given as to how contextual data (exogenous data) can be practically included for process mining analysis, beyond including c...
Article
Full-text available
Process Mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of Process Mining in organisations has been far more limited. In particular, there is limited understanding of the...
Preprint
Full-text available
Process models are used by human analysts to model and analyse behaviour, and by machines to verify properties such as soundness, liveness or other reachability properties, and to compare their expressed behaviour with recorded behaviour within business processes of organisations. For both human and machine use, small models are preferable over lar...
Chapter
Full-text available
Process mining facilitates analysis of business processes using event logs derived from historical records of process executions stored in organisations’ information systems. Most existing process mining techniques only consider data directly related to process execution (endogenous data). Data not directly representable as attributes of either eve...
Article
Real-life event logs, reflecting the actual executions of complex business processes, are faced with numerous data quality issues. Extensive data sanity checks and pre-processing are usually needed before historical data can be used as input to obtain reliable data-driven insights. However, most of the existing algorithms in process mining, a field...
Article
For many organizations, the continuous optimization of their business processes has become a critical success factor. Several related methods exist that enable the step-by-step redesign of business processes. However, these methods are mainly performed manually and require both creativity and business process expertise, which is often hard to combi...
Chapter
In this chapter, we introduce a conformance checking framework focused on speed. The framework can be applied to any combination of logs and models. The framework projects the behaviour exhaustively on all k-sized subsets of activities, after which the results can be shown as a measure, being projected on a model, or inspected in tabular format. We...
Chapter
In the previous chapters, we introduced the input and outputs of process mining techniques, and described challenges of process discovery techniques. Due to the trade offs identified in these challenges, we argued that different algorithms might be necessary in different use cases. In this chapter, we introduce a framework for process discovery in...
Chapter
In this chapter, we introduce some basic concepts that will be used extensively in the remaining chapters. Introduced concepts include multisets, regular expressions, process automata, Petri nets, workflow nets, soundness, free-choice Petri nets, YAWL, BPMN, process trees, event logs and the directly follows language abstraction.
Chapter
Process mining aims to extract information from event logs, which are recorded from running business processes. Process mining projects may go through multiple phases in which different process mining techniques are used: process discovery, conformance checking and model enhancement, to all of which we contributed concepts and techniques. In this c...
Chapter
In Chapter 1, we introduced three challenges of process mining: process discovery, conformance checking and model enhancement. In this chapter, we elaborate on these challenges, discuss related work and gather requirements for process mining techniques. We first discuss several use cases, and how these might need to be addressed using different pro...
Chapter
In this section, we discuss enhancement strategies using a end-user focused process mining tool that provides process discovery, conformance checking and enhancement: the Inductive visual Miner. We describe its architecture, introduce its user-focused process tree and highlight some of its features. The Inductive visual Miner supports several enhan...
Article
Through the application of process mining, organisations can improve their business processes by leveraging data recorded as a result of the performance of these processes. Over the past two decades, the field of process mining evolved considerably, offering a rich collection of analysis techniques with different objectives and characteristics. Des...
Chapter
In Chapter 4, we introduced the IM framework, which recursively discovers process trees from event logs. In Chapter 5, we identified footprints of behaviour in abstractions and corresponding classes of process trees that can be uniquely identified using these footprints. In this chapter, we introduce actual process discovery algorithms by defining...
Chapter
In this chapter, we evaluate the introduced discovery techniques and conformance checking framework. We perform 5 experiments to study the following questions: (1) how scalable are the discovery algorithms? (2) how do the discovery algorithms compare to existing techniques in terms of log quality? (3) what are the boundaries of rediscoverability of...
Chapter
In this chapter, we perform an in-depth study of several language abstractions. To this end, we first introduce a set of reduction rules for process trees, and show that this set is confluent. Second, we show for several abstractions that any two trees from a certain class of process trees that have the same language, have the same canonical form....
Conference Paper
The COVID-19 pandemic brought unexpected disruptions to educational practices, forcing universities to deliver lectures, tutorials, exams, and other assessments online. Academics and program managers reacted swiftly to adapt their education programs, managing a crisis that could have harmed Australia’s education system. Academic staff rapidly addre...
Article
Full-text available
Process mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of process mining in organizations has been far more limited. In particular, there is limited understanding of the...
Chapter
Responding to recent and repeated calls in literature, we sought to understand the effective use of business intelligence systems, specifically process mining. The intersection between effective use and business intelligence is pertinent to practice, as these systems do not automatically result in improved organizational outcomes, rather they must...
Article
Full-text available
The problem of automatically discovering business process models from event logs has been intensely investigated in the past two decades, leading to a wide range of approaches that strike various trade-offs between accuracy, model complexity, and execution time. A few studies have suggested that the accuracy of automated process discovery approache...
Conference Paper
In process mining, extensive data about an organizational process is summarized by a formal mathematical model with well-grounded semantics. In recent years a number of successful algorithms have been developed that output Petri nets, and other related formalisms, from input event logs, as a way of describing process control flows. Such formalisms...
Article
Initially, process mining focused on discovering process models from event data, but in recent years the use and importance of conformance checking has increased. Conformance checking aims to uncover differences between a process model and an event log. Many conformance checking techniques and measures have been proposed. Typically, these take into...
Article
Full-text available
Since their introduction, process trees have been frequently used as a process modeling formalism in many process mining algorithms. A process tree is a (mathematical) tree-based model of a process, in which internal vertices represent behavioral control-flow relations and leaves represent process activities. Translation of a process tree into a so...
Conference Paper
Full-text available
Many algorithms now exist for discovering process models from event logs. These models usually describe a control flow and are intended for use by people in analysing and improving real-world organizational processes. The relative likelihood of choices made while following a process (i.e., its stochastic behaviour) is highly relevant information wh...
Chapter
Robotic Process Automation (RPA) is an emerging technology for automating tasks using bots that can mimic human actions on computer systems. Most existing research focuses on the earlier phases of RPA implementations, e.g. the discovery of tasks that are suitable for automation. To detect exceptions and explore opportunities for bot and process red...
Chapter
Process mining aims to obtain insights from event logs to improve business processes. In complex environments with large variances in process behaviour, analysing and making sense of such complex processes becomes challenging. Insights in such processes can be obtained by identifying sub-groups of traces (cohorts) and studying their differences. In...
Preprint
Since their introduction, process trees have been frequently used as a process modeling formalism in many process mining algorithms. A process tree is a (mathematical) tree-based model of a process, in which internal vertices represent behavioral control-flow relations and leaves represent process activities. Translation of a process tree into a so...
Chapter
Process mining is proffered to bring substantial benefits to adopting organisations. Nevertheless, the uptake of process mining in organisations has not been as extensive as predicted. In-depth analysis of how organisations can successfully adopt process mining is seldom explored, yet much needed. We report our findings on an exploratory case study...
Preprint
Full-text available
This paper presents a command-line tool, called Entropia, that implements a family of conformance checking measures for process mining founded on the notion of entropy from information theory. The measures allow quantifying classical non-deterministic and stochastic precision and recall quality criteria for process models automatically discovered f...
Chapter
Business process management (BPM) aims to support changes and innovations in organizations’ processes. Process mining complements BPM with methods, techniques, and tools that provide insights based on observed executions of business processes recorded in event logs of information systems. State-of-the-art discovery and conformance techniques comple...
Conference Paper
Full-text available
Learning Analytics Dashboards (LADs) make use of rich and complex data about students and their learning activities to assist educators in understanding and making informed decisions about student learning and the design and improvement of learning processes. With the increase in the volume, velocity, variety and veracity of data on students, manua...
Conference Paper
Full-text available
Business process management (BPM) aims to support changes and innovations in organizations' processes. Process mining complements BPM with methods, techniques, and tools that provide insights based on observed executions of business processes recorded in event logs of information systems. State-of-the-art discovery and conformance techniques comple...
Article
Full-text available
Process workers may vary the normal execution of a business process to adjust to changes in their operational environment, e.g., changes in workload, season, or regulations. Changes may be simple, such as skipping an individual activity, or complex, such as replacing an entire procedure with another. Over time, these changes may negatively affect p...
Article
Full-text available
Process mining aims at obtaining information about processes by analysing their past executions in event logs, event streams, or databases. Discovering a process model from a finite amount of event data thereby has to correctly infer infinitely many unseen behaviours. Thereby, many process discovery techniques leverage abstractions on the finite ev...
Article
Through the application of Robotic Process Automation (RPA) organisations aim to increase their operational efficiency. In RPA, robots, or ‘bots’ for short, represent software agents capable of interacting with software systems by mimicking user actions, thus alleviating the workload of the human workforce. RPA has already seen significant uptake i...
Chapter
Full-text available
Process Mining aims to support Business Process Management (BPM) by extracting information about processes from real-life process executions recorded in event logs. In particular, conformance checking aims to measure the quality of a process model by quantifying differences between the model and an event log or another model. Even though event logs...
Chapter
In this work, we explore an approach to process discovery that is based on combining several existing process discovery algorithms. We focus on algorithms that generate process models in the process tree notation, which are sound by design. The main components of our proposed process discovery approach are the Inductive Miner, the Evolutionary Tree...
Article
Mining operations record a large amount of data from multiple sources (such as block model and online processing data) which is neither effectively nor systematically used to understand and improve operational performance. This paper proposes a generic semi-automatable data analytics method, the Integrated Analysis Method (IAM), that addresses the...
Article
Full-text available
Considerable amounts of data, including process events, are collected and stored by organisations nowadays. Discovering a process model from such event data and verification of the quality of discovered models are important steps in process mining. Many discovery techniques have been proposed, but none of them combines scalability with strong quali...
Conference Paper
Detecting and measuring resource queues is central to business process optimization. Queue mining techniques allow for the identification of bottlenecks and other process inefficiencies, based on event data. This work focuses on the discovery of resource queues. In particular, we investigate the impact of available information in an event log on th...
Conference Paper
Understanding the performance of business processes is an important part of any business process intelligence project. From historical information recorded in event logs, performance can be measured and visualized on a discovered process model. Thereby the accuracy of the measured performance, e.g., waiting time, greatly depends on (1) the availabi...
Conference Paper
Considerable amounts of data, including process event data, are collected and stored by organisations nowadays. Discovering a process model from recorded process event data is the aim of process discovery algorithms. Many techniques have been proposed, but none combines scalability with quality guarantees, e.g. can handle billions of events or thou...
Conference Paper
Full-text available
Process mining aims to transform event data recorded in information systems into knowledge of an organisation’s business processes. The results of process mining analysis can be used to improve process performance or compliance to rules and regulations. However, applying process mining in practice is not trivial. In this paper we introduce PM\(^2\)...
Conference Paper
Full-text available
In process mining, one of the main challenges is to discover a process model, while balancing several quality criteria. This often requires repeatedly setting parameters, discovering a map and evaluating it, which we refer to asprocess exploration. Commercial process mining tools like Disco, Perceptive and Celonis are easy to use and have many feat...
Conference Paper
Process mining, and in particular process discovery, have gained traction as a technique for analysing actual process executions from event data recorded in event logs. Process discovery aims to automatically derive a model of the process. Current process discovery techniques either do not provide executable semantics, do not guarantee to return mo...
Conference Paper
Full-text available
One of the main challenges in process mining is to discover a process model describing observed behaviour in the best possible manner. Since event logs only contain example behaviour and one cannot assume to have seen all possible process executions, process discovery techniques need to be able to handle incompleteness. In this paper, we study the...
Conference Paper
Full-text available
Given an event log describing observed behaviour, process discovery aims to find a process model that ‘best’ describes this behaviour. A large variety of process discovery algorithms has been proposed. However, no existing algorithm returns a sound model in all cases (free of deadlocks and other anomalies), handles infrequent behaviour well and fin...
Conference Paper
Full-text available
Process mining aims to extract information from recorded process data, which can be used to gain insights into the process. This requires applying a discovery algorithm and settings its parameters, after which the discovered process model should be evaluated. Both steps may need to be repeated several times until a satisfying model is found; we ref...
Conference Paper
Full-text available
Process discovery is the problem of, given a log of observed behaviour, finding a process model that ‘best’ describes this behaviour. A large variety of process discovery algorithms has been proposed. However, no existing algorithm guarantees to return a fitting model (i.e., able to reproduce all observed behaviour) that is sound (free of deadlocks...

Citations

... We performed a hybrid (inductive and deductive) qualitative analysis of 62 process mining case reports written from the user, tool vendor and practitioner perspectives outlining the success stories, tangible benefits and lessons learnt from over 50 organisations. Since process mining cases focus on applying PM tools within a given context, they are noted for providing detailed insights into PM use and outcomes [20]. Qualitatively analysing the insights from these cases provides a detailed understanding of PM success factors from a multi-case perspective. ...
... The process indicates that 234 patients do not have any preoperative imaging diag- nostics, while 214 patients enter the imaging diagnostics branch. Please refer to Leeman's manual on Inductive Visual Miner for details on the model notations[32]. ...
... First, for each of the nine datasets, one Petri net model is discovered using the inductive miner algorithm [48] implemented in PM4Py [49]. The Petri net is discovered based on the complete event log before training-evaluation-test splitting as the underlying process model should be as good as possible to generalize the behavior of the underlying process [22]. ...
... The presented aBPR tool integrates this four-step procedure. The literature shows that individual steps and combinations of these steps can be (partially) automated, leaving the finalization of the redesign options with the human user [6]. This is by no means a weakness of related work in this area, but often because the approaches work with assumptions for which data may be missing in the execution. ...
... While research in this field has been mainly concerned with technical matters, several recent works called for research around managerial and organizational aspects of process mining [e.g., 10], in order to leverage the full potential of process mining [11]. Understanding the organizational perspective involved in process mining is crucial to capitalize on the possible benefits of the technology [4,7]. ...
... Although we approached it from a process perspective, identifying the process activities and their execution over time, we have not discussed nor analysed the process behaviour, i.e., how such activities follow one another, and what their execution leads to. To analyse the process behaviour, process mining methodologies and tools often rely on directlyfollows relations [47] (see Definition 4), especially, for automated discovery of process models [48], and for process variant analysis [25,49,26]. ...
... The first deals with methodologies and techniques to improve the quality of event data, thus handling uncertainty in the data preparation phase [20]. The second instead aims at incorporating the management of uncertainty within the process mining tasks themselves, leading to a new generation of process mining techniques where process models [12,17,4,1] and/or event logs [16,8] explicitly address different kinds of uncertainty. ...
... Other papers [16,17,18,[19][20][21]22] applied abstraction mechanisms to log traces, and not to the process model. Only a few of them [16,20,22] explicitly adopted domain knowledge for realizing abstraction. ...
... In [31], EMSC is used to identify the trace attributes (for instance, amount of a loan application, gender, mode of study, etc.) has the highest influence on the process that is being followed, and to quantify this influence. That is, this technique automatically recommends trace-level filters that maximise the differences between groups of students (measured using EMSC) having and not having a particular attribute and value. ...
... For behavioral monitoring, there are several industrial solutions for keylogging 1 that capture the interaction of a human interacting with a system. In addition, other approaches have been proposed in academia, taking a further step in how to automate certain stages of robotization [3,11,20,24,25]. It should be noted that there are different formats proposed for capturing events, although the most representative for this work is the UI Log from [15] which defines it as an extension of the XES format-standard for event logs in Process Mining-which incorporate attributes like the app name (i.e., the name of the app), event type (i.e., mouse click or keystroke), click type (i.e., left, right, or middle), click coords (i.e., position of the mouse on the screen), the keystroke (i.e., the keys that are typed), and the screenshot (i.e., the screen capture associated to this event path). ...