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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 heuristics are derived from assumptions about how a given behavioral description - an event log - translates into a process model and were not learned from actual models which introduce biases in the solutions. In this paper, we explore the problem of supervised learning of a process discovery technique D. We introduce a technique for training an ML-based model D using graph convolutional neural networks; D translates a given input event log into a sound Petri net. We show that training D on synthetically generated pairs of input logs and output models allows D to translate previously unseen synthetic and several real-life event logs into sound, arbitrarily structured models of comparable accuracy and simplicity as existing state of the art techniques for discovering imperative process models. We analyze the limitations of the proposed technique and outline alleys for future work., Comment: accepted at IEEE International Conference on Process Mining (ICPM) 2021, submitted version

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 processing of work in batches, where multiple actors and/or cases meet for a number of steps. Established process mining and modeling techniques lack concepts for dealing with these more complex manifestations of work. We leverage event graphs as a data structure to model behavior along the actor and the case perspective in an integrated model, revealing a variety of fundamentally different types of task executions. We contribute a novel taxonomy and interpretation of these task execution patterns as well as techniques for detecting these in event graphs, complementing recent research in identifying patterns of work and their changes in routine dynamics. Our evaluation on two real-life event logs shows that these non-classical task execution patterns not only exist, but make up for the larger share of events in a process and reveal changes in how actors do their work., Extended report underlying the paper "Classifying and Detecting Task Executions and Routines in Processes using Event Graphs" accepted at the Forum of the 19th International Conference on Business Process Management. 2021

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 quality guarantees. We would like such techniques to handle billions of events or thousands of activities, to produce sound models (without deadlocks and other anomalies), and to guarantee that the underlying process can be rediscovered when sufficient information is available. In this paper, we introduce a framework for process discovery that ensures these properties while passing over the log only once and introduce three algorithms using the framework. To measure the quality of discovered models for such large logs, we introduce a model–model and model–log comparison framework that applies a divide-and-conquer strategy to measure recall, fitness, and precision. We experimentally show that these discovery and measuring techniques sacrifice little compared to other algorithms, while gaining the ability to cope with event logs of 100,000,000 traces and processes of 10,000 activities on a standard computer.

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 modeling approaches are taken as a relevant and timely stream of process design approaches to test our argument. First, we provide a review of existing data-centric process approaches, culminating in a theoretical classification framework. Next, we empirically evaluate three specific approaches with regard to the claims they make. We had participants representative of actual users try out these approaches on realistic scenarios via a series of workshops. Participants assessed to what extent quality claims from the literature could be recognized within the workshop sessions. The results of this evaluation substantiate a number of claims behind the approaches, but also identify opportunities to further improve them. Most prominently, we found that the usability aspects of all considered approaches are a source of concern. This leads us to the insight that usability aspects of process design approaches are crucial and, in the perception of groups representative of actual users, leave much to be desired. In that sense, our research can be seen as a wake-up call for process modeling notation designers to consider the usability side—and as such, the interest of the human modeler—more than is currently the case.

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 novel prediction approach that uses the performance spectrum for feature selection and extraction to pose machine learning problems used for performance prediction in non-isolated cases. Although the approach is general, we focus on material handling systems as a primary example. We report on a feasibility study conducted for the material handling systems of a major European airport. The results show that the use of the performance spectrum enables much better predictions than baseline approaches.

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 processes stored in linear event logs to discover a process model that reveals unusual executions. Existing approaches to obtain linear event logs from ERP data require a single case identifier to which all behavior can be related. However, in ERP systems processes such as Order to Cash operate on multiple interrelated business objects, each having their own case identifier, their own behavior, and interact with each other. Forcing these into a single case creates ambiguous dependencies caused by data convergence and divergence which obscures unusual flows in the resulting process model. In this paper, we present a new semi-automatic, end-to-end approach for analyzing event data in a plain database of an ERP system for unusual executions. More precisely, we identify an artifact-centric process model describing the business objects, their life-cycles, and how the various objects interact along their life-cycles. This way, we prevent data divergence and convergence. We report on two case studies where our approach allowed to successfully analyze processes of ERP systems and reliably revealed unusual flows later confirmed by domain experts.

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 entirely model-driven approach for BPMN introducing a few concepts that suffice to model data retrieval, data transformation, message exchange, and correlation – four aspects of data exchange. For automation, this work utilizes a recent concept to enact data dependencies in internal processes. We present a modeling guideline to derive local process models from a given choreography; their operational semantics allows to correctly enact the entire choreography from the derived models only including the exchange of data. Targeting on successful interactions, we discuss means to ensure correct process choreography modeling. Finally, we implemented our approach by extending the camunda BPM platform with our approach and show its feasibility by realizing all service interaction patterns using only model-based concepts. Keywords: Process modeling; Data modeling; Process choreographies; Data exchange; BPMN; SQL

We report on a case study on control-flow analysis of business process models. We checked 735 industrial business process models from financial services, telecommunications, and other domains. We investigated these models for soundness (absence of deadlock and lack of synchronization) using three different approaches: the business process verification tool Woflan, the Petri net model checker LoLA, and a recently developed technique based on SESE decomposition. We evaluate the various techniques used by these approaches in terms of their ability of accelerating the check. Our results show that industrial business process models can be checked in a few milliseconds, which enables tight integration of modeling with control-flow analysis. We also briefly compare the diagnostic information delivered by the different approaches and report some first insights from industrial applications. Keywords : Business Process Modeling; Quality assurance; Soundness; Verification; Control-flow analysis

Artifact-centric modeling is an approach for capturing business processes in terms of so-called business artifacts — key entities driving a company's operations and whose lifecycles and interactions define an overall business process. This approach has been shown to be especially suitable in the context of processes where one-to-many or many-to-many relations exist between the entities involved in the process. As a contribution towards building up a body of methods to support artifact-centric modeling, this article presents a method for automated discovery of artifact-centric process models starting from logs consisting of flat collections of event records. We decompose the problem in such a way that a wide range of existing (non-artifact-centric) automated process discovery methods can be reused in a flexible manner. The presented methods are implemented as a package for ProM, a generic open-source framework for process mining. The methods have been applied to reverse-engineer an artifact-centric process model starting from logs of a real-life business process.

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 to the error state. These paths can grow unmanageably and make the understanding and fixing of errors very time consuming. This paper addresses this issue and proposes a novel explanation of errors: Instead of listing the actions on the path to the error, only the decisions that lead to it are reported and highlighted in the original model. Furthermore, we exploit concurrency to create a compact artifact to explain errors.

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 Charts, using a statistical data-mining algorithm. We show the power of branching scenarios to reveal alternative scenario-based behaviors, which could not be mined by previous approaches. The work contrasts and complements previous works on mining linear-time scenarios. An implementation and evaluation over execution trace sets recorded from several real-world applications shows the unique contribution of mining branching-time scenarios to the state-of-the-art in specification mining.

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. Deviations may be costly and expose the organization to severe risks. Compliance checking is of growing importance for the business process management and auditing communities. This paper presents a comprehensive compliance checking approach based on Petri-net patterns and alignments. 55 control flow oriented compliance rules, distributed over 15 categories, have been formalized in terms of Petri-net patterns describing the compliant behavior. To check compliance with respect to a rule, the event log describing the observed behavior is aligned with the corresponding pattern. The approach is flexible (easy to add new patterns), robust (the selected alignment between log and pattern is guaranteed to be optimal), and allows for both a quantification of compliance and intuitive diagnostics explaining deviations at the level of alignments. The approach can also handle resource-based and data-based compliance rules and is supported by ProM plug-ins. The applicability of the approach has been evaluated using various real-life event logs.

In the immediate aftermath of a disaster, routine processes, even if specifically designed for such a situation, are not enacted blindly. Actions and processes rather adapt their behavior based on observations and available information. Attempts to support these processes by technology rely on process models that faithfully capture process execution and adaptation. Based on experiences from actual disaster response settings, we propose to specify an adaptive process as a set of scenarios using a Petri net syntax. Our operational model provides an adaptation operator that synthesizes and adapts the system behavior at run-time based on the given scenarios. An example illustrates our approach.

The paradigm of Service-Oriented Computing (SOC) provides a framework for interorganizational business processes and for the emerging programming-in-the-large. The basic idea of SOC, the interaction of services, rises a lot of issues such as proper termination of interacting services or substitution of a service by another one. Such issues can be addressed by means of models of services. We show how services can intelligibly be modeled, and we present algorithms and tools to analyze properties of service models. In order to emphasize that our models properly reflect real world issues of services, we also show that services represented in established languages such as WS-BPEL can be transformed into our formal method.