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The applications of process mining, which is a business process management technique, in the field of healthcare are increasing day by day. In process mining, it is possible to analyze the process for three main purposes: discovery of the process, compliance control and process improvement, based on event logs recorded in information systems. Transforming patient-based healthcare processes data into process and event-based event logs is the first step of a process mining project in order to apply process mining techniques to human-centered, distributed, complex and multidisciplinary healthcare processes and to improve the quality of health care services. The process model discovered in multi-perspective process mining is expanded from different perspectives such as control flow, organizational, data, time and function, and the discovered process becomes more understandable. In this study, and in order to apply multi-perspective process mining, a method is proposed to convert the healthcare processes data recorded in hospital information systems into event logs. Data transformation method consists of six steps: data collection and data privacy, data integration, data conversion, data preprocessing, feature selection and extraction, and multi-perspective process mining analysis. The proposed method was validated by a case study by transforming the surgery process data of a university hospital in Turkey into an event log. The process discovery algorithm was applied to the surgery process data of the case study and the actual process was discovered, and the applicability of the data transformation method was shown on the real data. With the guiding feature of the method for healthcare professionals, it is expected to contribute to the applications of multi-perspective process mining in the field of healthcare in Turkey. [ABSTRACT FROM AUTHOR]

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 and are therefore often used. In this paper, we extend the eST-Miner process discovery algorithm. The eST-Miner computes a set of Petri net places which are considered to be fitting with respect to a certain fraction of the behavior described by the given event log as indicated by a given noise threshold. It evaluates all possible candidate places using token-based replay. The set of replayable traces is determined for each place in isolation, i.e., these sets do not need to be consistent. This allows the algorithm to abstract from infrequent behavioral patterns occurring only in some traces. However, when combining places into a Petri net by connecting them to the corresponding uniquely labeled transitions, the resulting net can replay exactly those traces from the event log that are allowed by the combination of all inserted places. Thus, inserting places one-by-one without considering their combined effect may result in deadlocks and low fitness of the Petri net. In this paper, we explore adaptions of the eST-Miner, that aim to select a subset of places such that the resulting Petri net guarantees a definable minimal fitness while maintaining high precision with respect to the input event log. Furthermore, current place evaluation techniques tend to block the execution of infrequent activity labels. Thus, a refined place fitness metric is introduced and thoroughly investigated. In our experiments we use real and artificial event logs to evaluate and compare the impact of the various place selection strategies and place fitness evaluation metrics on the returned Petri net. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of automated modelling of discrete-event processes from event traces that capture the process behaviour. A novel method for building Petri nets (PN) that include silent transitions is presented. The method discovers workflow nets (WFN), a class of PN, from logs of event traces λ. The proposed approach is based on a classification of traces such that λ is partitioned into four classes: normal traces (λN), which do not involve the firing of silent transitions; skip traces (λS), redo traces (λR), and switch traces (λSW), which involve the firing of skip, redo, and switch type silent transitions, respectively. Afterwards, traces in λN are used to build a base WFN N, which may include silent transitions of type initialise and finalise. Then, N is refined by adding the silent transitions corresponding to the classes oftraces λS, λR, and λSW. The algorithms of the method have polynomial-time complexity; implementation and tests using artificial event logs are presented. [ABSTRACT FROM AUTHOR]

Traditional approaches for process modeling and process analysis tend to focus on one type of object (also referred to as cases or instances), and each event refers to precisely one such object. This simplifies modeling and analysis, e.g., a process model merely describes the lifecycle of one object (e.g., a production order or an insurance claim) in terms of its activities (i.e., event types). However, in reality, there are often multiple objects of different types involved in an event. Think about filling out an electronic form referring to one order, one customer, ten items, three shipments, and one invoice. Object-centric process mining (OCPM) takes a more holistic and more comprehensive approach to process analysis and improvement by considering multiple object types and events that involve any number of objects. This paper introduces object-centric event data (OCED) and shows how these can be used to discover, analyze, and improve the fabric of real-life, highly intertwined processes. This tutorial-style paper presents the basic concepts, object-centric process-mining techniques, examples, and formalizes OCED. Fully embracing object centricity provides organizations with a "three-dimensional" view of their processes, showing how they interact with each other, and where the root causes of performance and compliance problems lie. [ABSTRACT FROM AUTHOR]

Process model resulting from small event log datasets can be easily done because currently available applications are relatively able to do so. However, when faced with event logs from big data, modelling will force the existing applications to work hard. So far, the steps used are sampling of event logs resulting from the system. The problem arising is that the sampling process must be done several times because it has to check the desired fitness value on the sample taken. If the fitness value has not been got, then the sample size is added and the fitness value at a certain iteration is calculated until the required fitness value is met. There are many steps to do with this mechanism. Thus, this paper proposes an alternative way to reduce the steps by using an appropriate sampling technique. The mechanism used is statistical-based sampling simulation in the event log datasets to determine which sampling method is stable in the process modelling. The simulation results show that the sampling method using Cluster Random Sampling with the error rate or Alpha of 1 % has a relatively stable process model and can represent the process model resulting from the event log population. [ABSTRACT FROM AUTHOR]

Background: Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease whose spreading and progression mechanisms are still unclear. The ability to predict ALS prognosis would improve the patients' quality of life and support clinicians in planning treatments. In this paper, we investigate ALS evolution trajectories using Process Mining (PM) techniques enriched to both easily mine processes and automatically reveal how the pathways differentiate according to patients' characteristics. Methods: We consider data collected in two distinct data sources, namely the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) dataset and a real-world clinical register (ALS–BS) including data of patients followed up in two tertiary clinical centers of Brescia (Italy). With a focus on the functional abilities progressively impaired as the disease progresses, we use two Process Discovery methods, namely the Directly-Follows Graph and the CareFlow Miner, to mine the population disease trajectories on the PRO-ACT dataset. We characterize the impairment trajectories in terms of patterns, timing, and probabilities, and investigate the effect of some patients' characteristics at onset on the followed paths. Finally, we perform a comparative study of the impairment trajectories mined in PRO-ACT versus ALS–BS. Results: We delineate the progression pathways on PRO-ACT, identifying the predominant disabilities at different stages of the disease: for instance, 85% of patients enter the trials without disabilities, and 48% of them experience the impairment of Walking/Self-care abilities first. We then test how a spinal onset increases the risk of experiencing the loss of Walking/Self-care ability as first impairment (52% vs. 27% of patients develop it as the first impairment in the spinal vs. the bulbar cohorts, respectively), as well as how an older age at onset corresponds to a more rapid progression to death. When compared, the PRO-ACT and the ALS–BS patient populations present some similarities in terms of natural progression of the disease, as well as some differences in terms of observed trajectories plausibly due to the trial scheduling and recruitment criteria. Conclusions: We exploited PM to provide an overview of the evolution scenarios of an ALS trial population and to preliminary compare it to the progression observed in a clinical cohort. Future work will focus on further improving the understanding of the disease progression mechanisms, by including additional real-world subjects as well as by extending the set of events considered in the impairment trajectories. [ABSTRACT FROM AUTHOR]

A process model discovered from an event log of a multi-agent system often does not fully cover certain viewpoints of its architecture. We consider those concerned with the structure of a model explicitly reflecting agent behavior and interactions. The direct discovery from an event log of a multi-agent system may result in an unclear model structure and over-generalizations of agent behavior. We suggest applying a compositional approach that yields architecture-aware process models of multi-agent systems. An event log of a multi-agent system is filtered by the behavior of individual agents. Then, a multi-agent system model is a composition of agent models discovered from filtered logs. We use an intermediate model, called an interface pattern, specifying agent interactions and representing the architecture of a multi-agent system. We design a collection of specific interface patterns modeling typical agent interactions. An interface pattern provides an abstract specification of interactions and has a part corresponding to the behavior of each agent. We use structural transformations to map agent models discovered from filtered logs on the respective parts in an interface pattern. If such a mapping exists, we guarantee that a composition of agent models preserves their soundness. We conduct a series of experiments to evaluate the compositional approach. Experimental results confirm the improvement in the structure of process models discovered using the compositional approach compared to those discovered directly from event logs. [ABSTRACT FROM AUTHOR]

Within process mining, discovery techniques make it possible to construct business process models automatically from event logs. However, results often do not achieve a balance between model complexity and fitting accuracy, establishing a need for manual model adjusting. This paper presents an approach to process mining that provides semi-automatic support to model optimization based on the combined assessment of model complexity and fitness. To balance complexity and fitness, a model simplification approach is proposed, which abstracts the raw model at the desired granularity. Additionally, we introduce a concept of meta-states, a cycle collapsing in the model, which can potentially simplify the model and interpret it. We aim to demonstrate the capabilities of our technological solution using three datasets from different applications in the healthcare domain. These are remote monitoring processes for patients with arterial hypertension and workflows of healthcare workers during the COVID-19 pandemic. A case study also investigates the use of various complexity measures and different ways of solution application, providing insights on better practices in improving interpretability and complexity/fitness balance in process models. [ABSTRACT FROM AUTHOR]

Process discovery usually analyses frequent behaviour in event logs to gain an intuitive understanding of processes. However, there are some effective infrequent behaviours that help to improve business processes in real life. Most existing studies either ignore them or treat them as harmful behaviours. To distinguish effective infrequent sequences from noisy activities, this paper proposes an algorithm to analyse the distribution states of activities and the strong transfer relationships between behaviours based on maximum probability paths. The algorithm divides episodic traces into two categories: harmful and useful episodes, namely noisy activities and effective sequences. First, using conditional probability entropy, the infrequent logs are pre-processed to remove individual noisy activities that are extremely irregularly distributed in the traces. Effective sequences are then extracted from the logs based on the state transfer information of the activities. The algorithm is based on a PM4Py implementation and is validated using synthetic and real logs. From the results, the algorithm not only preserves the key structure of the model and reduces noise activity, but also improves the quality of the model. [ABSTRACT FROM AUTHOR]

Event logs that are recorded by information systems provide a valuable starting point for the analysis of processes in various domains, reaching from healthcare, through logistics, to e-commerce. Specifically, behavioral patterns discovered from an event log enable operational insights, even in scenarios where process execution is rather unstructured and shows a large degree of variability. While such behavioral patterns capture frequently recurring episodes of a process’ behavior, they are not limited to sequential behavior but include notions of concurrency and exclusive choices. Existing algorithms to discover behavioral patterns are context-agnostic, though. They neglect the context in which patterns are observed, which severely limits the granularity at which behavioral regularities are identified. In this paper, we therefore present an approach to discover contextual behavioral patterns. Contextual patterns may be frequent solely in a certain partition of the event log, which enables fine-granular insights into the aspects that influence the conduct of a process. Moreover, we show how to analyze the discovered contextual behavioral patterns in terms of causal relations between context information and the patterns, as well as correlations between the patterns themselves. A complete analysis methodology leveraging all the tools presented in the paper and supplemented by interpretations guidelines is also provided. Finally, experiments with real-world event logs demonstrate the effectiveness of our techniques in obtaining fine-granular process insights. [ABSTRACT FROM AUTHOR]

Dependency graph discovery is a substantial step in heuristic mining algorithms which are among the most prevalent process discovery methods in the healthcare domain due to their ability to deal with noisy event logs derived from unstructured and highly variable healthcare processes. However, in many healthcare applications, the current dependency graph discovery methods are still likely to create complex (spaghetti-like) and low-quality results. Hence, this paper improves the heuristic mining methods for healthcare applications by introducing a novel integer linear programming model for selecting the optimum set of dependency graph activities and arcs. The proposed method can lead to the extraction of dependency graphs that are simultaneously high-quality and non-complex. According to the assessments, when dealing with artificial and real-life healthcare event logs, the proposed method results in outputs with significantly less complexity and higher quality than the most prominent heuristic mining methods. [ABSTRACT FROM AUTHOR]

Process discovery is an important area in the field of process mining. To help advance this area, a process discovery contest (PDC) has been set up, which allows us to compare different approaches. At the moment of writing, there have been three instances of the PDC: in 2016, in 2017, and in 2019. This paper introduces the winning contribution to the PDC 2019, called the Log Skeleton Visualizer. This visualizer uses a novel type of process models called log skeletons. In contrast with many workflow net-based discovery techniques, these log skeletons do not rely on the directly follows relation. As a result, log skeletons offer circumstantial information on the event log at hand rather than only sequential information. Using this visualizer, we were able to classify 898 out of 900 traces correctly for the PDC 2019 and to win this contest. [ABSTRACT FROM AUTHOR]

Declarative process modeling formalisms—which capture high-level process constraints—have seen growing interest, especially for modeling flexible processes. This paper presents DisCoveR, an efficient and accurate declarative miner for learning Dynamic Condition Response (DCR) Graphs from event logs. We present a precise formalization of the algorithm, describe a highly efficient bit vector implementation and present a preliminary evaluation against five other miners, representing the state-of-the-art in declarative and imperative mining. DisCoveR performs competitively with each of these w.r.t. a fully automated binary classification task, achieving an average accuracy of 96.1% in the Process Discovery Contest 2019 (Results are available at https://icpmconference.org/2019/process-discovery-contest). We appeal to computational learning theory to gain insight into its performance as a classifier. Due to its linear time complexity, DisCoveR also achieves much faster run times than other declarative miners. Finally, we show how the miner has been integrated in a state-of-the-art declarative process modeling framework as a model recommendation tool and discuss how discovery can play an integral part of the modeling task and report on how the integration has improved the modeling experience of end-users. [ABSTRACT FROM AUTHOR]

Process discovery usually analyses frequent behaviour in event logs to gain an intuitive understanding of processes. However, there are some effective infrequent behaviours that help to improve business processes in real life. Most existing studies either ignore them or treat them as harmful behaviours. To distinguish effective infrequent sequences from noisy activities, this paper proposes an algorithm to analyse the distribution states of activities and the strong transfer relationships between behaviours based on maximum probability paths. The algorithm divides episodic traces into two categories: harmful and useful episodes, namely noisy activities and effective sequences. First, using conditional probability entropy, the infrequent logs are pre-processed to remove individual noisy activities that are extremely irregularly distributed in the traces. Effective sequences are then extracted from the logs based on the state transfer information of the activities. The algorithm is based on a PM4Py implementation and is validated using synthetic and real logs. From the results, the algorithm not only preserves the key structure of the model and reduces noise activity, but also improves the quality of the model. [ABSTRACT FROM AUTHOR]

Process mining (PM) is a well‐known research area that includes techniques, methodologies, and tools for analyzing processes in a variety of application domains. In the case of healthcare, processes are characterized by high variability in terms of activities, duration, and involved resources (e.g., physicians, nurses, administrators, machineries, etc.). Besides, the multitude of diseases that the patients housed in healthcare facilities suffer from makes medical contexts highly heterogeneous. As a result, understanding and analyzing healthcare processes are certainly not trivial tasks, and administrators and doctors look for tools and methods that can concretely support them in improving the healthcare services they are involved in. In this context, PM has been increasingly used for a wide range of applications as reported in some recent reviews. However, these reviews mainly focus on discussion on applications related to the clinical pathways, while a systematic review of all possible applications is absent. In this article, we selected 172 papers published in the last 10 years, that present applications of PM in the healthcare domain. The objective of this study is to help and guide researchers interested in the medical field to understand the main PM applications in the healthcare, but also to suggest new ways to develop promising and not yet fully investigated applications. Moreover, our study could be of interest for practitioners who are considering applications of PM, who can identify and choose PM algorithms, techniques, tools, methodologies, and approaches, toward what have been the experiences of success. This article is categorized under:Application Areas > Health CareFundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining [ABSTRACT FROM AUTHOR]

Return material authorization (RMA) is a process in which a company decides to repair or replace customer’s defect product during the warranty period. To execute RMA, both company and customer obliged to follow standard operating procedure (SOP) which usually consists of many business processes of a company well. As the business process could cause inefficiencies, a company should improve their business process regularly. The best way is using process discovery. This research proposes a new improved fuzzy miner algorithm to represent binary correlation between activities. This new algorithm utilizes binary significance and binary correlation equally to acquire fuzzy model. While the original fuzzy miner algorithm uses various binary correlation metrics, the improved fuzzy miner algorithm uses only one metric and could capture the fuzzy model, accurately based on the event logs to capture more accurate business process model. In this research, ProM fuzzy miner is used as a comparison to the proposed improved time-based fuzzy miner. The results showed that the improved algorithm has higher value on conformance checking and able to capture business process model based on time interval, by using only time-interval significance as a binary correlation metrics. [ABSTRACT FROM AUTHOR]

Tremendous amounts of execution data are collected during software execution. These data provide rich information for software runtime behavior comprehension. The unstructured execution data may be too complex, involving multiple interleaved components and so on. Applying existing process discovery techniques results in spaghetti-like models with no clear structure and no valuable information that can be easily understood by end users. In this article, we start with the observation that a software system is composed of a group of components, and we use this information to decompose the problem into smaller independent ones by discovering a behavioral model per component. To this end, we first distill a software event log for each component from the raw software execution data. Then, we construct the hierarchical software event log by recursively applying caller-and-callee relation detection. Next, component behavioral models, represented as hierarchical Petri nets, are discovered by recursively applying existing process discovery techniques. To measure the quality of discovered models against the execution data, we transform hierarchical Petri nets to flat ones, and the quality metrics, e.g., fitness, precision, and complexity, are applied. All proposed approaches have been implemented in the open-source process mining toolkit ProM. Through the experimental evaluation using both synthetic software systems and open-source software systems, we illustrate that the proposed approach facilitates the discovery of more understandable and high-quality software behavioral models. Note to Practitioners—Software execution data record rich information on the runtime behavior of software systems. Discovering an overall behavioral model for the whole software system typically results in an extremely complicated model that hinders further comprehension and usage. With the observation that a software system is composed of a set of interacting components, this article considers the problem of discovering a behavioral model per component. The proposed techniques are implemented in the open-source process mining tool ProM, and experimental evaluations using both synthetic and real-life software systems have indicated their applicability. The proposed approaches are readily applicable to industrial-size software behavior comprehension. [ABSTRACT FROM AUTHOR]

With Process discovery algorithms, we discover process models based on event data, captured during the execution of business processes. The process discovery algorithms tend to use the whole event data. When dealing with large event data, it is no longer feasible to use standard hardware in a limited time. A straightforward approach to overcome this problem is to down-size the data utilizing a random sampling method. However, little research has been conducted on selecting the right sample, given the available time and characteristics of event data. This paper systematically evaluates various biased sampling methods and evaluates their performance on different datasets using four different discovery techniques. Our experiments show that it is possible to considerably speed up discovery techniques using biased sampling without losing the resulting process model quality. Furthermore, due to the implicit filtering (removing outliers) obtained by applying the sampling technique, the model quality may even be improved. [ABSTRACT FROM AUTHOR]

Process mining (PM) aims to construct, from event logs, process maps that can help discover, automate, improve, and monitor organizational processes. Robotic process automation (RPA) uses software robots to perform some tasks usually executed by humans. It is usually difficult to determine what processes and steps to automate, especially with RPA. PM is seen as one way to address such difficulty. This paper aims to assess the applicability of process mining in accelerating and improving the implementation of RPA, along with the challenges encountered throughout project lifecycle. A systematic literature review was conducted to examine the approaches where PM techniques were used to understand the as-is processes that can be automated with software robots. Seven databases were used to identify papers on this topic. A total of 32 papers, all published since 2018, were selected from 605 unique candidate papers and then analyzed. There is a steady increase in the number of publications in this domain, especially during the year 2022, which suggests a raising interest in the combined use of PM with RPA. The literature mainly focuses on the methods to record the events that occur at the level of user interactions with the application, and on the preprocessing methods that are needed to discover routines with the steps that can be automated. Important challenges are faced with preprocessing such event logs, and many lifecycle steps of automation projects are weakly supported by existing approaches suggesting corresponding research areas in need of further attention. • Combining process mining (PM) and RPA offers unique process management opportunities. • PM techniques must better support discovering processes based on UI logs for RPA. • Tools need to better integrate PM and RPA features together, in a synergetic way. • Challenges common to PM and RPA remain, e.g., with data gathering and preprocessing. [ABSTRACT FROM AUTHOR]

Discovering process models from event data is a highly relevant, but also a notoriously difficult, problem. Therefore, it is unsurprising that the biggest share of process mining research is devoted to process discovery. While techniques reported in scientific literature tend to produce process models that are formal, i.e., which mathematically describe the possible behaviors, commercial process mining tools return informal models (merely a "picture" not allowing for any form of formal reasoning). Hybrid process models aim at combining the best of both worlds: they capture behavior that is strongly supported by data and that can be used for formal reasoning, as well as behavior that cannot be represented in clear-cut process constructs or that does not have enough evidence in the data. This paper presents an approach for discovering hybrid Petri nets , which, unlike existing techniques, produces models that have both formal and semi-formal constructs so that even if the behavior in the data is noisy and irregular or it does not fit predefined constructs, causal relationships are still captured. Our evaluation demonstrates the advantages of combining such "deliberate vagueness" with formal guarantees. The ideas presented here are fairly general, and can serve as a foundation for other, new hybrid discovery techniques. [ABSTRACT FROM AUTHOR]

Many medical solutions exist in the vast medical logs, and how to use them to make precise recommendations for medical pathways has become a current research hotspot. This paper proposed an algorithmic framework that combines trace clustering, process discovery, and neural network techniques to discover models of medical pathways considering complications from medical logs. First, trace clustering divided the treatment pathways set in medical logs into multiple subsets with similar behaviours. Then multiple streamlined process models were mined by process discovery techniques. The reasonable medical pathways were extracted from each process model. A cluster of trained neural network models was used to determine the case characteristic labels and obtain the attention of events in the medical pathways. The output was finally integrated to generate a model of medical pathways considering complications. The results of the experiments using the Sepsis Cases dataset showed that the average simplicity of the generated process models was 0.695, the average accuracy of the neural network models was 93.3%, and the medical pathways model score was about 0.879. [ABSTRACT FROM AUTHOR]

A process discovery algorithm aims to construct a process model that represents the real-world process stored in event data well; it is precise, generalizes the data correctly, and is simple. At the same time, it is reasonable to expect that better quality input event data should lead to constructed process models of better quality. However, existing process discovery algorithms omit the discussion of this relationship between the inputs and outputs and, as it turns out, often do not guarantee it. We demonstrate the latter claim using several quality measures for event data and discovered process models. Consequently, this paper requests for more rigor in the design of process discovery algorithms, including properties that relate the qualities of the inputs and outputs of these algorithms. We present four incremental maturity stages for process discovery algorithms, along with concrete guidelines for formulating relevant properties and experimental validation. We then use these stages to review several state of the art process discovery algorithms to confirm the need to reflect on how we perform algorithmic process discovery. • We propose measures for the quality of event logs using random sampling techniques; • We show the effect random sampling techniques have on the quality of event logs; • Empirical evidence that quality of event logs is not correlated with quality of models; • We propose four maturity stages for process discovery algorithms. [ABSTRACT FROM AUTHOR]

Process mining techniques are valuable to gain insights into and help improve (work) processes. Many of these techniques focus on the sequential order in which activities are performed. Few of these techniques consider the statistical relations within processes. In particular, existing techniques do not allow insights into how responses to an event (action) result in desired or undesired outcomes (effects). We propose and formalize the ARE miner, a novel technique that allows us to analyze and understand these action-response-effect patterns. We take a statistical approach to uncover potential dependency relations in these patterns. The goal of this research is to generate processes that are: (1) appropriately represented, and (2) effectively filtered to show meaningful relations. We evaluate the ARE miner in two ways. First, we use an artificial data set to demonstrate the effectiveness of the ARE miner compared to two traditional process-oriented approaches. Second, we apply the ARE miner to a real-world data set from a Dutch healthcare institution. We show that the ARE miner generates comprehensible representations that lead to informative insights into statistical relations between actions, responses, and effects. • We present the ARE miner as a novel process discovery technique. • The ARE miner provides insights into action–response–effect patterns. • We build on the notion of statistical significance to accomplish this. • The results are visualized in a dedicated graphical representation. • The representations allow to effectively analyze (un)desired effects. [ABSTRACT FROM AUTHOR]

Discovery plays a key role in data-driven analysis of business processes. The vast majority of contemporary discovery algorithms aims at the identification of control-flow constructs. The increase in data richness, however, enables discovery that incorporates the context of process execution beyond the control-flow perspective. A "control-flow first" approach, where context data serves for refinement and annotation, is limited and fails to detect fundamental changes in the control-flow that depend on context data. In this work, we thus propose a novel approach for combining the control-flow and data perspectives under a single roof by extending inductive process discovery. Our approach provides criteria under which context data, handled through unsupervised learning, take priority over control-flow in guiding process discovery. The resulting model is a process tree, in which some operators carry data semantics instead of control-flow semantics. We show that the proposed approach produces trees that are context consistent, deterministic, complete, and can be explainable without a major quality reduction. We evaluate the approach using synthetic and real-world datasets, showing that the resulting models are superior to state-of-the-art discovery methods in terms of measures based on multi-perspective alignments. • Context-aware process trees (CaT) are defined. • An inductive context-aware discovery algorithm (CaDi) is proposed. • CaDi produces trees that are context consistent, deterministic, and complete. • CaDi discovers models of higher quality than the state-of-the-art. • CaDi can be tuned to provide CaTs that are more explainable. [ABSTRACT FROM AUTHOR]