Business process management employs process models and event logs to represent the behavior of the information systems under study. Traditional case-centric notions consider the order of activities and events in isolated process instances. The emerging field of object-centric processes challenges this assumption by putting objects in the center. Object-centric process mining and modeling approaches identify the structure of co-evolving data objects that influence the behavior of an information system to provide a comprehensive view of the system behavior. Object-centricity has been investigated independently in process modeling and in process mining, which resulted in the coexistence of seemingly contradictory assumptions and definitions. As a community effort, this research manifesto relates and aligns existing terminologies, definitions, and perspectives to provide a common ground for current and future research in object-centric business process management. Based on the current state of research, we propose a conceptualization that sets process models and event logs in relation to the information system’s behavior and the execution data it generates. The conceptualization aims at aligning different terminologies and, thus, providing a basis to model and analyze behavioral characteristics. Building on this common ground, we identify open research challenges along the most relevant research areas in object-centric process management. For each research area, its current status is investigated and an outline of the most relevant research challenges is presented.

Conformance checking techniques compare process models of organizational behavior with observed process executions to reveal their deviations. Traditional alignments concern individual activities and provide a single out of potentially infinitely many explanations for observed deviations. Skip alignments lift insights to subprocesses and provide all possible explanations. Though valuable for analysts and process mining tools, there exist no interpretations how likely these deviations are. In this paper, we introduce skip probabilities revealing how likely certain subprocesses deviate w.r.t. an event log of observed process executions. We show the formal derivation of this calculation and demonstrate the feasibility of its computation. By analyzing a realistic case, we empirically show that yet hidden process insights can be derived from skip probabilities and how they contribute to targeted process improvement.

Modern information systems track the behavior of business objects in digital execution records, called objectcentric event logs. An important step in the analysis of these logs is the discovery of a process model that describes the interacting control flows of all objects. In real-life processes, these interactions are often based on the object’s identities. In an order management process, for example, the same set of items that is ordered by a customer is subsequently also delivered. However, existing object-centric modeling formalisms and discovery techniques either ignore these identity relations, treat them as static, or capture them without guaranteeing important behavioral soundness properties. In this paper, we formalize three types of dynamic identity relations that can frequently be found in real-life processes. Then, we introduce a new object-centric modeling formalism that supports these identity relations while guaranteeing important soundness properties by construction. Additionally, we provide a discovery algorithm to construct corresponding process models from object-centric event logs. We prove that the inclusion of dynamic identity relations in our models preserves and improves important model quality criteria. Lastly, we evaluate our approach by applying it to a range of public logs. We observe the discovery of dynamic identity relations in feasible runtime in all investigated logs.

Process mining leverages event logs extracted from information systems to generate insights into the business processes of organizations. These insights are enhanced by explicitly accounting for the frequency of behavior captured in stochastic process models constructed from event logs. Causal nets are an elegant declarative process modeling formalism that relies on a small number of modeling constructs, yet is expressive. In this paper, we extend this formalism to the stochastic setting, that is, to allow the extended nets to capture the likelihoods of the observed process. We also propose a stochastic causal net discovery approach using Markovian abstraction. Our approach begins with a standard causal net model generated by a control flow discovery algorithm, and then employs optimization techniques to determine optimal binding weights. These weights enable the stochastic interpretation of the model to closely approximate the Markovian abstraction of the original event log. Our technique has been implemented and made publicly available. The evaluation based on this implementation demonstrates the feasibility of the technique. Compared to baseline models, the discovered models achieve noticeable improvements in the quality of stochastic conformance.

Process mining techniques are useful for analyzing and optimizing processes. However, processes often exist in many variants that can differ significantly in their execution. These differences within the data can negatively affect the quality of process mining results and, furthermore, indicate disparities within the process. While several approaches exist that characterize the differences between processes, oftentimes the mere existence of differences can be problematic. To this end, techniques to prove or disprove the existence of such differences are required and should do so in a statistically sound manner. However, the literature on process hypothesis testing is sparse and limited in the considered dimensions of difference. In this paper, we propose a hypothesis testing approach that uses the earth mover’s distance in combination with a permutation test to compare event logs in various dimensions. The evaluation shows that the proposed approach achieves better performance than the existing work in detecting control-flow differences and, moreover, detects differences in further dimensions, demonstrated on the time dimension.

Business processes may span multiple organizations. For instance, cattle may move through several organizations in an agricultural supply chain, or patients may be seen by multiple healthcare providers as part of a treatment process. Optimizing these cross-organizational processes is an aim of process mining, however, process mining efforts may be challenged by commercially sensitive data or privacy laws, which may prevent the involved organizations from sharing recorded process data with one another. Federated process mining aims to perform inter-organizational analyses without information being shared across organizational borders. In this paper, we propose a federated technique to discover directly-follows graphs (DFGs), using homomorphic encryption, while keeping timestamps and activities secret. We evaluate the feasibility of our technique using real-life event logs to discover their DFGs and discuss potential attacks.

Abstract Process mining aids organisations in improving their operational processes by providing visualisations and algorithms that turn event data into insights. How often behaviour occurs in a process—the stochastic perspective—is important for simulation, recommendation, enhancement and other types of analysis. Although the stochastic perspective is important, the focus is often on control flow. Stochastic conformance checking techniques assess the quality of stochastic process models and/or event logs with one another. In this paper, we address three limitations of existing stochastic conformance checking techniques: inability to handle uncertain event data (e.g. events having only a date), exponential blow-up in computation time due to the analysis of all interleavings of concurrent behaviour and the problem that loops that can be unfolded infinitely often. To address these challenges, we provide bounds for conformance measures and use partial orders to encode behaviour. An open-source implementation is provided, which we use to illustrate and evaluate the practical feasibility of the approach.

The research area of object-centric process mining provides techniques to model and analyze business processes with interacting object types, such as orders, items and packages. An important step in the analysis of such processes is the automated discovery of a process model that represents the control flow and interaction of all object types. However, existing object-centric discovery algorithms often produce process models that are hard to interpret due to their exccessive complexity. Additionally, they often do not provide formal guarantees on important model properties, such as liveness and identifier-soundness. These properties guarantee, upon executing the model, that all participating objects can properly reach the end of their life-cycle and that all parts of the model can eventually become active. In this paper, we propose a new object-centric discovery algorithm to automatically construct compact object-centric process models that are guaranteed to be live and identifier-sound. For this purpose, we introduce object-centric process trees as an abstract view on object-centric Petri nets that provide both guarantees by construction. We evaluate our approach by applying it to a range of synthetic and real-life logs and find it to be feasible in terms of runtime and unique with regards to its provided guarantees.

Conformance checking focuses on quantifying behavioral differences between desired and observed process behavior. Stochastic conformance checking considers not only the desired control flow of a process but also the relative frequency of each sequence. State-of-the-art stochastic conformance measures either cannot gracefully handle partially matching traces or are prohibitively expensive to compute. This paper bridges this gap by introducing the stochastic Markovian abstraction. The abstraction is defined as the relative occurrences of sub-traces in a stochastic language. Two stochastic languages can be compared via their Markovian abstractions using existing language comparison techniques. We show how to compute this abstraction for bounded, livelock-free stochastic labeled Petri nets. One of its derived measures is qualitatively and quantitatively evaluated on a series of artificial and real-world datasets. The experiments show that the abstraction can be efficiently computed and is successful in handling partially mismatching traces.

Process models describe the desired or observed behaviour of organisations. In stochastic process mining, computational analysis of trace data yields process models which describe process paths and their probability of execution. To understand the quality of these models, and to compare them, quantitative quality measures are used. This research investigates model comparison empirically, using stochastic process models built from real-life logs. The experimental design collects a large number of models generated randomly and using process discovery techniques. Twenty-five different metrics are taken on these models, using both existing process model metrics and new, exploratory ones. The results are analysed quantitatively, making particular use of principal component analysis. Based on this analysis, we suggest three stochastic process model dimensions: adhesion, relevance and simplicity. We also suggest possible metrics for these dimensions, and demonstrate their use on example models.

Capturing stochastic behaviour in business and work processes is essential to quantitatively understand how nondeterminism is resolved when taking decisions within the process. This is of special interest in process mining, where event data tracking the actual execution of the process are related to process models, and can then provide insights on frequencies and probabilities. Variants of stochastic Petri nets provide a natural formal basis to represent stochastic behaviour and support different data-driven and model-driven analysis tasks in this spectrum. However, when capturing business processes, such nets inherently need a labelling that maps between transitions and activities. In many state of the art process mining techniques, this labelling is not 1-on-1, leading to unlabelled transitions and activities represented by multiple transitions. At the same time, they have to be analysed in a finite-trace semantics, matching the fact that each process execution consists of finitely many steps. These two aspects impede the direct application of existing techniques for stochastic Petri nets, calling for a novel characterisation that incorporates labels and silent transitions in a finite-trace semantics. In this article, we provide such a characterisation starting from generalised stochastic Petri nets and obtaining the framework of labelled stochastic processes (LSPs). On top of this framework, we introduce different key analysis tasks on the traces of LSPs and their probabilities. We show that all such analysis tasks can be solved analytically, in particular reducing them to a single method that combines automata-based techniques to single out the behaviour of interest within an LSP, with techniques based on absorbing Markov chains to reason on their probabilities. Finally, we demonstrate the significance of how our approach in the context of stochastic conformance checking, illustrating practical feasibility through a proof-of-concept implementation and its application to different datasets.

In process mining, computational processing of sequential data allows the discovery and analysis of processes followed by organisations. These can be either explicitly understood processes, captured in documents or rules, or implicit process paths known in more informal or emergent ways. This paper examines a long-lived institution of historical interest, the Qing (1644-1911) Chinese civil service, using data assembled by historians on civil officials during the 19th century. Mapping the promotion process by following paths of officials through civil service postings helps illuminate the everyday operation of the institution and the society around it. Two distinctive features of this data set are that it records states, not events, and careers often include holding multiple concurrent roles. The combination is a poor match for existing process discovery techniques. We describe this structure as a state snapshot log, and present a new discovery technique, the State Snapshot miner, for constructing stochastic Petri net models from such logs. A case study shows its use in analysing promotion paths for elite graduates in the Qing civil service.

Process discovery algorithms extract process models from business process event logs. Existing discovery algorithms require upfront filtering, or specific parameter input, to produce models with balanced quality dimensions on real-life event logs. We propose the Approximate Inductive Miner (AIM) to fill this gap and offer an automated way to discover sound models in polynomial time complexity, without any pre-processing or mandatory parameter input. AIM uses the existing Inductive Miner framework and applies clustering techniques to recursively identify structures in the event log. It additionally performs an approximate parameter optimisation to dynamically suggest a suitable parameter. We compare AIM with existing discovery algorithms on synthetic and real-life event logs, and evaluate the quality of the integrated parameter suggestion. We find that AIM on its own produces sound models with low control flow complexity and high precision, even on complex event logs. Additionally, AIM is able to handle a vast range of event log properties, such as infrequent and incomplete behaviour, without requiring any human parameter input or upfront filtering.

Abstract 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 ], yielding a strict total order on the observed activity instances. However, for various practical use cases, e.g., the logging of activity executions with a nonzero duration and uncertainty on the correctness of the recorded timestamps of the activity executions, assuming a partial order on the observed activity instances is more appropriate. Using partial orders to represent process executions, i.e., based on recorded event data, allows for new classes of process mining algorithms, i.e., aware of parallelism and robust to uncertainty. Yet, interestingly, only a limited number of studies consider using intermediate data abstractions that explicitly assume a partial order over a collection of observed activity instances. Considering recent developments in process mining, e.g., the prevalence of high-quality event data and techniques for event data abstraction, the need for algorithms designed to handle partially ordered event data is expected to grow in the upcoming years. Therefore, this paper presents a survey of process mining techniques that explicitly use partial orders to represent recorded process behavior. We performed a keyword search, followed by a snowball sampling strategy, yielding 68 relevant articles in the field. We observe a recent uptake in works covering partial-order-based process mining, e.g., due to the current trend of process mining based on uncertain event data. Furthermore, we outline promising novel research directions for the use of partial orders in the context of process mining algorithms.

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. Despite the advances in this field, a solid statistical foundation is still lacking. Such a foundation would allow analysis outcomes to be found or judged using the notion of statistical significance, thus providing a more objective way to assess these outcomes. This paper contributes several statistical tests and association measures that treat process behaviour as a variable. The sensitivity of these tests to their parameters is evaluated and their applicability is illustrated through the use of real-life event logs. The presented tests and measures constitute a key contribution to a statistical foundation for process mining.

Stochastic process models are a type of model that explicitly include elements of probability in describing an organization, facilitating different modes of analysis and simulation. Having obtained models of an organizational process, say through process mining, using them well depends on understanding their quality, and being able to compare different models. There may not be a single optimal stochastic model for a process, but tradeoffs between models, decided by their intended use. Reasoning about trade-offs in a precise way requires quantitative measures, and an understanding of how these measures relate, including whether they capture independent underlying properties.This paper is an empirical investigation of measures for stochastic process models built from real-life logs. The experimental design assembles a large collection of models built both randomly and by discovery techniques. A wide spectrum of candidate measures, drawn from and inspired by the process mining literature, are applied using these models. Based on this analysis, three stochastic quality dimensions are proposed: adhesion, entropy and simplicity.

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 focusing on data-driven process analysis, do not take any data quality issues or the potential effects of data pre-processing into account explicitly. This can result in erroneous process mining results, leading to inaccurate, or misleading conclusions about the process under investigation. To address this gap, we propose data quality annotations for event logs, which can be used by process mining algorithms to generate quality-informed insights. Using a design science approach, requirements are formulated, which are leveraged to propose data quality annotations. Moreover, we present the “Quality-Informed visual Miner” plug-in to demonstrate the potential utility and impact of data quality annotations. Our experimental results, utilising both synthetic and real-life event logs, show how the use of data quality annotations by process mining techniques can assist in increasing the reliability of performance analysis results.

Process mining techniques aim to derive knowledge of the execution of processes, by means of automated analysis of behaviour recorded in event logs. A well-known challenge in process mining is to strike an adequate balance between the behavioural quality of a discovered model compared to the event log and the model's complexity as perceived by stakeholders. At the same time, events typically contain multiple attributes related to parts of the process at different levels of abstraction, which are often ignored by existing process mining techniques, resulting in either highly complex and/or incomprehensible process mining results. This paper addresses this problem by extending process mining to use event-level attributes readily available in event logs. We introduce (1) the concept of multi-level logs and generalise existing hierarchical process models, which support multiple modelling formalisms and notions of activities in a single model, (2) a framework, instantiation and implementation for process discovery of hierarchical models, and (3) a corresponding conformance checking technique. The resulting framework has been implemented as a plug-in of the open-source process mining framework ProM, and has been evaluated qualitatively and quantitatively using multiple real-life event logs.

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 process performance; hence, it is important to identify and understand them early on. As such, a number of techniques have been developed to detect process drifts , i.e., statistically significant changes in process behavior, from process event logs (offline) or event streams (online). However, detecting a drift without characterizing it, i.e., without providing explanations on its nature, is not enough to help analysts understand and rectify root causes for process performance issues. Existing approaches for drift characterization are limited to simple changes that affect individual activities. This article contributes an efficient, accurate, and noise-tolerant automated method for characterizing complex drifts affecting entire process fragments. The method, which works both offline and online, relies on two cornerstone techniques, one to automatically discover process trees from event streams (logs) and the other to transform process trees using a minimum number of change operations. The operations identified are then translated into natural language statements to explain the change behind a drift. The method has been extensively evaluated on artificial and real-life datasets, and against a state-of-the-art baseline method. The results from one of the real-life datasets have also been validated with a process stakeholder.

Many organisations now seek to analyse and improve their processes using event logs from various IT systems supporting their operations. Process mining aims to obtain insights from such process data, using process discovery, conformance checking and performance measures. While many commercial process mining tools feature user-friendly directly follows-based process maps, they typically do not offer a way to assess the quality of the model, leaving users with potentially unreliable insights, which could lead to the wrong conclusion being drawn from these insights. In contrast, academic tools typically provide verifiable results, but are often difficult to use and understand for stakeholders, sometimes overgeneralising behaviour to fit more extensive process model formalisms. In this paper, we bridge this well-known gap between commercial and academic tools by combining sound process discovery, conformance checking and performance capabilities with user-friendly directly follows-based process models. We implemented these techniques in a new process mining tool and applied them to analyse several business processes in a Queensland Government department. We discovered sound directly follows-based process models from their logs, compared them with prescribed models and analysed the performance of these processes. In particular, our conformance checking techniques allowed to pinpoint deviations between prescribed processes and actual recorded behaviour. The outcomes of this case study are now being used to document, review, improve and automate processes.