Dirk Fahland

Eindhoven University of Technology | TUE · Department of Mathematics and Computer Science

Event data is the basis for all process mining analysis. Most process mining techniques assume their input to be an event log. However, event data is rarely recorded in an event log format, but has to be extracted from raw data. Event log extraction itself is an act of modeling as the analyst has to consciously choose which features of the raw data...

Processes are complex phenomena that emerge from the interplay of human actors, materials, data, and machines. Process science develops effective methods and techniques for studying and improving processes. The BPM field has developed mature methods and techniques for studying and improving process executions from the control-flow perspective, and...

Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems that draws upon trustworthy AI technology. An ABPMS enhances the execution of business processes with the aim of making these processes more adaptable, proactive, explainable, and context-sensitive. This manifesto presents a vision for...

To identify the causes of performance problems or to predict process behavior, it is essential to have correct and complete event data. This is particularly important for distributed systems with shared resources, e.g., one case can block another case competing for the same machine, leading to inter-case dependencies in performance. However, due to...

Current process discovery techniques are unable to produce meaningful models for semi-structured processes, as they are either too inaccurate or too complex. In this paper we use the idea of local process models (LPMs) to model fragments of a semi-structured process and explore the potential of sets of LPMs. Automatic LPM discovery finds many small...

Automatically discovering a process model from an event log is the prime problem in process mining. This task is so far approached as an unsupervised learning problem through graph synthesis algorithms. Algorithmic design decisions and heuristics allow for efficiently finding models in a reduced search space. However, design decisions and heuristic...

Numerous process discovery techniques exist for generating process models that describe recorded executions of business processes. The models are meant to generalize executions into human-understandable modeling patterns, notably parallelism, and enable rigorous analysis of process deviations. However, well-defined models with parallelism returned...

Business process management organizes work into several interrelated “units of work”, fundamentally conceptualized as a task. The classical concept of a task as a single step executed by a single actor in a single case fails to capture more complex aspects of work that occur in real-life processes. For instance, actors working together or the proce...

Process mining offers a set of techniques for gaining data-based insights into business processes from event logs. The literature acknowledges the potential benefits of using process mining techniques in Six Sigma-based process improvement initiatives. However, a guideline that is explicitly dedicated on how process mining can be systematically use...

Process event data is usually stored either in a sequential process event log or in a relational database. While the sequential, single-dimensional nature of event logs aids querying for (sub)sequences of events based on temporal relations such as “directly/eventually-follows,” it does not support querying multi-dimensional event data of multiple r...

To identify the causes of performance problems or to predict process behavior, it is essential to have correct and complete event data. This is particularly important for distributed systems with shared resources, e.g., one case can block another case competing for the same machine, leading to inter-case dependencies in performance. However, due to...

In process mining, basic descriptive statistics over observed events of event logs or streams, projected onto a process model, are typically used for performance analysis. The so-called performance spectrum is used for the fine-grained description of process performance over time, additionally revealing phenomena related to the behavior of multiple...

Process mining techniques can be used to discover process models from event data and project performance and conformance related diagnostics on such models. For example, it is possible to automatically discover Petri nets showing the bottlenecks in production, administration, transport, and financial processes. Also basic statistics (frequencies, a...

To identify the causes of performance problems or to predict process behavior, it is essential to have correct and complete event data. This is particularly important for distributed systems with shared resources, e.g., one case can block another case competing for the same machine, leading to inter-case dependencies in performance. However, due to...

To identify the causes of performance problems or to predict process behavior, it is essential to have correct and complete event data. This is particularly important for distributed systems with shared resources, e.g., one case can block another case competing for the same machine, leading to inter-case dependencies in performance. However, due to...

Process event data is usually stored either in a sequential process event log or in a relational database. While the sequential, single-dimensional nature of event logs aids querying for (sub)sequences of events based on temporal relations such as "directly/eventually-follows", it does not support querying multi-dimensional event data of multiple r...

Given a model of the expected behavior of a business process and given an event log recording its observed behavior, the problem of business process conformance checking is that of identifying and describing the differences between the process model and the event log. A desirable feature of a conformance checking technique is that it should identif...

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...

This book constitutes the proceedings of the BPM Forum of the 18th International Conference on Business Process Management, BPM 2020, which was planned to take place in Seville, Spain, in September 2020. Due to the COVID-19 pandemic the conference took place virtually. The BPM Forum hosts innovative research which has a high potential of stimulatin...

This book constitutes the proceedings of the 18th International Conference on Business Process Management, BPM 2020, held in Seville, Spain, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 27 full papers included in this volume were carefully reviewed and selected from 125 submissions. Two full keynote papers...

Given a model of the expected behavior of a business process and an event log recording its observed behavior, the problem of business process conformance checking is that of identifying and describing the differences between the model and the log. A desirable feature of a conformance checking technique is to identify a minimal yet complete set of...

The author offers a personal account of how Carl Adam Petri explained nets, tokens, and dynamics in the universe.

Predictive performance analysis is crucial for supporting operational processes. Prediction is challenging when cases are not isolated but influence each other by competing for resources (spaces, machines, operators). The so-called performance spectrum maps a variety of performance-related measures within and across cases over time. We propose a no...

Processes are a key application area for formal models of concurrency. The core concepts of Petri nets have been adopted in research and industrial practice to describe and analyze the behavior of processes where each instance is executed in isolation. Unaddressed challenges arise when instances of processes may interact with each other in a one-to...

Process event data is usually stored either in a sequential process event log or in a relational database. While the sequential, single-dimensional nature of event logs aids querying for (sub)sequences of events based on temporal relations such as “directly/eventually-follows”, it does not support querying multi-dimensional event data of multiple r...

Performance analysis from process event logs is a central element of business process management and improvement. Established performance analysis techniques aggregate time-stamped event data to identify bottlenecks or to visualize process performance indicators over time. These aggregation-based techniques are not able to detect and quantify the p...

The BPI Challenge 2018 is focused on analysis of a process that covers the handling of applications for EU direct payments for German farmers from the European Agricultural Guarantee Fund. This work is focused on performance analysis of this process. Our goal is to demonstrate and explain a novel process mining technique and tool, which we recently...

Performance is central to processes management and event data provides the most objective source for analyzing and improving performance. Current process mining techniques give only limited insights into performance by aggregating all event data for each process step. In this paper, we investigate process performance of all process behaviors withou...

We present the Performance Spectrum Miner, a ProM plugin, which implements a new technique for fine-grained performance analysis of processes. The technique uses the performance spectrum as a simple model, that maps all observed flows between two process steps together regarding their performance over time, and can be applied for event logs of any...

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...

We propose and study dynamic versions of the classical flexibility constructs ‘skip’ and ‘block’ for workflows and motivate and define a formal semantics for them. We show that our semantics is a generalization of dead-path-elimination and solves the open problem to define dead-path-elimination for cyclic workflows. This in turn gives rise to a sim...

The detection of data breaches has become a major challenge for most organizations. The problem lies in that fact that organizations often lack proper mechanisms to control and monitor users' activities and their data usage. Although several auditing approaches have been proposed to assess the compliance of actual executed behavior, existing approa...

Process mining offers a variety of techniques for analyzing process execution event logs. Although process discovery algorithms construct end-to-end process models, they often have difficulties dealing with the complexity of real-life event logs. Discovered models may contain either complex or over-generalized fragments, the interpretation of which...

The Business Process Management field addresses design, improvement, management, support, and execution of business processes. In doing so, we argue that it focuses more on developing modeling notations and process design approaches than on the needs and preferences of the individual who is modeling (i.e., the user). New data-centric process modeli...

In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log. Although several measures have been proposed throughout the years, no research has been done to validate whether these measures achieve the intended aim of quantifying over-approximation in a consistent way for all...

We propose and study dynamic versions of the classical flexibility constructs skip and block and motivate and define a formal semantics for them. We show that our semantics for dynamic blocking is a generalization of classical dead-path-elimination and solves the long-standing open problem to define dead-path elimination for cyclic workflows. This...

Processes may require to execute the same activity in different stages of the process. A human modeler can express this by creating two different task nodes labeled with the same activity name (thus duplicating the task). However, as events in an event log often are labeled with the activity name, discovery algorithms that derive tasks based on lab...

Deviation detection is a set of techniques that identify deviations from normative processes in real process executions. These diagnostics are used to derive recommendations for improving business processes. Existing detection techniques identify deviations either only on the process instance level or rely on a normative process model to locate dev...

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...

Enterprise Resource Planning (ERP) systems are widely used to manage business documents along a business processes and allow very detailed recording of event data of past process executions and involved documents. This recorded event data is the basis for auditing and detecting unusual flows. Process mining techniques can analyze event data of proc...

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...

Communication between organizations is formalized as process choreographies in daily business. While the correct ordering of exchanged messages can be modeled and enacted with current choreography techniques, no approach exists to describe and automate the exchange of data between processes in a choreography using messages. This paper describes an...

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...

Conformance checking is becoming more important for the analysis of business processes. While the diagnosed results of conformance checking techniques are used in diverse context such as enabling auditing and performance analysis, the quality and reliability of the conformance checking techniques themselves have not been analyzed rigorously. As the...

Compliance checking is gaining importance as today's organizations need to show that their business practices are in accordance with predefined (legal) require-ments. Current compliance checking techniques are mostly focused on checking the control-flow perspective of business processes. This paper presents an approach for checking the compliance o...

Business process modeling is still a challenging task — especially since more and more aspects are added to the models, such as data lifecycles, security constraints, or compliance rules. At the same time, formal methods allow for a detection of errors in the early modeling phase. Detected errors are usually explained with a path from the initial t...

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...

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...

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...

Process discovery is a technique for deriving a conceptual high-level process model from the execution logs of a running implementation. The technique is particularly useful when no high-level model is available or in case of significant gaps between process documentation and implementation. The discovered model makes the implementation accessible...

This paper contributes to the solution of the problem of transforming a process model with an arbitrary topology into an equivalent structured process model. In particular, this paper addresses the subclass of process models that have no equivalent well-structured representation but which, nevertheless, can be partially structured into their maxima...

Workflow graphs represent the main control-flow constructs of industrial process modeling languages such as BPMN, EPC and UML Activity diagrams, whereas free-choice workflow nets is a well understood class of Petri nets that possesses many efficient analysis techniques. In this paper, we provide new results on the translation between workflow graph...

Specification mining extracts candidate specification from existing systems, to be used for downstream tasks such as testing and verification. Specifically, we are interested in the extraction of behavior models from execution traces. In this paper we introduce mining of branching-time scenarios in the form of existential, conditional Live Sequence...

Specification mining extracts candidate specification from existing systems, to be used for downstream tasks such as testing and verification. Specifically, we are interested in the extraction of behavior models from execution traces. In this paper we introduce mining of branching-time scenarios in the form of existential, conditional Live Sequence...

Enacting business processes in process engines requires the coverage of control flow, resource assignments, and process data. While the first two aspects are well supported in current process engines, data dependencies need to be added and maintained manually by a process engineer. Thus, this task is error-prone and time-consuming. In this paper, w...

Compliance specifications concisely describe selected aspects of what a business operation should adhere to. To enable automated techniques for compli-ance checking, it is important that these requirements are specified correctly and precisely, describing exactly the behavior intended. Although there are rigorous mathematical formalisms for represe...

Enterprise Integration Patterns allow us to design a middleware system conceptually before actually implementing it. So far, the in-depth analysis of such a design was not feasible, as these patterns are only described informally. We introduce a translation of each of these patterns into a Coloured Petri Net, which allows to investigate and improve...

Compliance checking is gaining importance as today’s organizations need to show that operational processes are executed in a controlled manner while satisfying predefined (legal) requirements or service level agreements. Deviations may be costly and expose an organization to severe risks. Compliance checking is of growing importance for the busines...

Business process models are an important means to design, analyze, implement, and control business processes. As with every type of conceptual model, a business process model has to meet certain syntactic, semantic, and pragmatic quality requirements to be of value. For many years, such quality aspects were investigated by centering on the properti...

Artifact-centric modeling is a promising approach for modeling business processes based on the so-called business artifacts - key entities driving the company's operations and whose lifecycles define the overall business process. While artifact-centric modeling shows significant advantages, the overwhelming majority of existing process mining metho...

Live sequence charts (LSC) is a visual, executable, language for the modeling of reactive systems. Each chart depicts an inter-object scenario arising in the modeled system, partitioned into two: a monitored prechart, and a main chart. Despite the intuitive use of the language, complications arise when one wants to implement an LSC specification wi...

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...

Process mining techniques relate observed behavior (i.e., event logs) to modeled behavior (e.g., a BPMN model or a Petri net). Process models can be discovered from event logs and conformance checking techniques can be used to detect and diagnose differences between observed and modeled behavior. Existing process mining techniques can only uncover...

Grading dozens of Petri net models manually is a tedious and error-prone task. In this paper, we present Grade/CPN, a tool supporting the grading of Colored Petri nets modeled in CPN Tools. The tool is extensible, configurable, and can check static and dynamic properties. It automatically handles tedious tasks like checking that good modeling pract...

Execution of process models requires a process engine to handle control flow and data dependencies. While control flow is well supported in available activity-oriented process engines, data dependencies have to be specified manually in an error-prone and time-consuming work. In this paper, we present an extension to the process engine Activiti allo...