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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]

With the advancement of cloud computing, many challenging scientific problems can be solved using scientific workflow technology which integrates geo-distributed instruments, applications, and big data effectively and efficiently. For workflow collaboration, the workflow protocols of all participants are needed. However, workflow protocols are not always available and are often outdated as the workflow evolve frequently. To address this problem, we propose a novel workflow discovery approach which can extract up-to-date scientific workflow protocols from public event logs in clouds, without the need to access the full-fledged event logs involving private events. Our approach leverages transitive precedence relations between events to achieve this. We implement our approach as a ProM plug-in, and evaluate it through extensive experiments on event logs of real-world scientific workflows. The experimental results demonstrate that our approach requires a weaker completeness notion of event logs than the state-of-the-art do, and our approach derives the same workflow protocol from the public event log as that discovered from the original event log, and thus the private events can be protected. [ABSTRACT FROM AUTHOR]

Current process management systems require a fully defined workflow design to implement a specific business process. Designing a business process is a complex and time-consuming task. Mostly workflow processes are different from those that are desired. Therefore, various algorithms have been developed for the discovery of actual processes rather than ideal processes. The first step for the discovery algorithms is to extract event logs that contain details about the process. In this study, Alpha algorithm which is used to generate a process model in event logs recorded in information technology (IT) systems is introduced. Process models are discovered using the basic activities performed in the recruitment processes. It has been shown that the alpha algorithm can successfully model a business process. [ABSTRACT FROM AUTHOR]

In industrial automation, there are numerous use cases for a model representing the equipment's sequence of operations. Not only can such a model be used as a debugging aid, allowing users to observe the current state of the machinery at a glance, but it also can serve as a foundation for process improvements. Annotated process models can help pinpoint bottlenecks within the system, and a combination of models obtained from identical machines can be used to create a best-case baseline to which the devices can be optimised. Typically, the models used for such activities have been created during the equipment's design phase, which means that many of the changes made during commissioning, start-up, and production are not reflected. The research domain of process mining suggests that an accurate model can be obtained from an activity log. Since most industrial processes are controlled by Programmable Logic Controllers (PLCs), which offer the capability of external access through Open Platform Communications (OPC), such a log can be created automatically. The theory is that process mining algorithms can then be used to discover the desired model from the data recorded. Unfortunately, it turns out that this whole procedure is a game of chance. The quality of the discovered models strongly depends on the logged data. Not only can the records be flawed, but also the information needed to discover a complete model might not have been observed at all. To better judge the quality of the discovered models, numerous quality metrics have been proposed, which in the authors' opinion are a good guess at best. The industrial automation domain demands complete models, which cannot be guaranteed by any process mining algorithm unless it is known that the log contains all the data needed. This paper shows that for industrial equipment, it is possible to reason through the evaluation of a single recorded case, which case traces are required to guarantee that the discovered model will be complete. In the next step, the authors then introduced the concept of 'trace induction', which takes advantage of the fact that a PLC controls the observed processes. A minor change within the PLC's logic is used to force the process to execute the desired traces on demand. The resulting, minimalistic log can then be analysed by the αLC-algorithm to create a guaranteed complete, model. [ABSTRACT FROM AUTHOR]

Techniques to discover Petri nets from event data assume precisely one case identifier per event. These case identifiers are used to correlate events, and the resulting discovered Petri net aims to describe the life-cycle of individual cases. In reality, there is not one possible case notion, but multiple intertwined case notions. For example, events may refer to mixtures of orders, items, packages, customers, and products. A package may refer to multiple items, multiple products, one order, and one customer. Therefore, we need to assume that each event refers to a collection of objects, each having a type (instead of a single case identifier). Such object-centric event logs are closer to data in real-life information systems. From an object-centric event log, we want to discover an object-centric Petri net with places that correspond to object types and transitions that may consume and produce collections of objects of different types. Object-centric Petri nets visualize the complex relationships among objects from different types. This paper discusses a novel process discovery approach implemented in PM4Py. As will be demonstrated, it is indeed feasible to discover holistic process models that can be used to drill-down into specific viewpoints if needed. [ABSTRACT FROM AUTHOR]

Data centres have become the backbone of large Cloud services and applications, providing virtually unlimited elastic and scalable computational and storage resources. The search for the efficiency and optimisation of resources is one of the current key aspects for large Cloud Service Providers and is becoming more and more challenging, since new computing paradigms such as Internet of Things, Cyber-Physical Systems and Edge Computing are spreading. One of the key aspects to achieve efficiency in data centres consists of the discovery and proper analysis of the data-centre behaviour. In this paper, we present a model to automatically retrieve execution workflows of existing data-centre logs by employing process mining techniques. The discovered processes are characterised and analysed according to the understandability and complexity in terms of execution efficiency of data-centre jobs. We finally validate and demonstrate the usability of the proposal by applying the model in a real scenario, that is, the Google Cluster traces. [ABSTRACT FROM AUTHOR]

Business processes are implemented in an organization. When a business process is run, it generates event log. One type of event log is double timestamp event log. Double timestamp has the start and complete time of each activity executed in the business process and has a close relationship with temporal pattern. In this paper, seven types of temporal pattern between activities were presented as extended version of relations used in the double timestamp event log. Since the event log was not always executed in sequential way, therefore using temporal pattern, event log was divided into several small groups to mine the business process both sequential and parallel. Both temporal pattern and Time-based a+ Miner algorithm were used to mine process model, determined sequential and parallel relations and then evaluated the process model using fitness value. This paper was focused on the advantages of temporal pattern implemented in Time-based a+ Miner algorithm to mine business process. The results also clearly stated that the proposed method could present better result rather than that of original a+ Miner algorithm. [ABSTRACT FROM AUTHOR]

Analysing performance of business processes is an important vehicle to improve their operation. Specifically, an accurate assessment of sojourn times and remaining times enables bottleneck analysis and resource planning. Recently, methods to create respective performance models from event logs have been proposed. These works have several limitations, though: They either consider control-flow and performance information separately, or rely on an ad-hoc selection of temporal relations between events. In this paper, we introduce the Temporal Network Representation (TNR) of a log. It is based on Allen's interval algebra, comprises the pairwise temporal relations for activity executions, and potentially incorporates the context in which these relations have been observed. We demonstrate the usefulness of the TNR for detecting (unrecorded) delays and for probabilistic mining of variants when modelling the performance of a process. In order to compare different models from the performance perspective, we further develop a framework for measuring performance fitness. Under this framework, TNR-based process discovery is guaranteed to dominate existing techniques in measuring performance characteristics of a process. In addition, we show how contextual information in terms of the congestion levels of the process can be mined in order to further improve capabilities for performance analysis. To illustrate the practical value of the proposed models, we evaluate our approaches with three real-life datasets. Our experiments show that the TNR yields an improvement in performance fitness over state-of-the-art algorithms, while congestion learning is able to accurately reconstruct congestion levels from event data. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to discover an anomalous-free business process model from event logs. The process discovery was conducted using a graph database, specifically using Neo4J tool involving trace clustering and data filtering processes. We also developed a control-flow pattern to address, AND relation between activities named parallel business process. The result showed that the proposed method improved the precision value of the generated business process model from 0.64 to 0.81 compared to the existing algorithm. The better outcome is constructed by applying trace clustering and data filtering to remove the anomaly on the event log as well as discovering parallel (AND) relation between activities. [ABSTRACT FROM AUTHOR]

Information systems, which are responsible for driving many processes in our lives (health care, the web, municipalities, commerce and business, among others), store information in the form of logs which is often left unused. Process mining, a discipline in between data mining and software engineering, proposes tailored algorithms to exploit the information stored in a log, in order to reason about the processes underlying an information system. A key challenge in process mining is discovery: Given a log, derive a formal process model that can be used afterward for a formal analysis. In this paper, we provide a general approach based on satisfiability modulo theories (SMT) as a solution for this challenging problem. By encoding the problem into the logical/arithmetic domains and using modern SMT engines, it is shown how two separate families of process models can be discovered. The theory of this paper is accompanied with a tool, and experimental results witness the significance of this novel view of the process discovery problem. [ABSTRACT FROM AUTHOR]

Local Process Models (LPM) describe structured fragments of process behavior occurring in the context of less structured business processes. Traditional LPM discovery aims to generate a collection of process models that describe highly frequent behavior, but these models do not always provide useful answers to questions posed by process analysts aiming at business process improvement. We propose a framework for goal-driven LPM discovery , based on utility functions and constraints. We describe four scopes on which these utility functions and constraints can be defined, and show that utility functions and constraints on different scopes can be combined to form composite utility functions/constraints. Finally, we demonstrate the applicability of our approach by presenting several actionable business insights discovered with LPM discovery on three real-life data sets. [ABSTRACT FROM AUTHOR]

Process mining techniques analyze processes based on events stored in event logs. Yet, low-level events recorded by information systems may not directly match high-level activities that make sense to process stakeholders. This results in discovered process models that cannot be easily understood. To prevent such situations from happening, low-level events need to be translated into high-level activities that are recognizable by stakeholders. This paper proposes the Guided Process Discovery method (GPD). Low-level events are grouped based on behavioral activity patterns, which capture domain knowledge on the relation between high-level activities and low-level events. Events in the resulting abstracted event log correspond to instantiations of high-level activities. We validate process models discovered on the abstracted event log by checking conformance between the low-level event log and an expanded model in which the high-level activities are replaced by activity patterns. The method was tested using two real-life event logs. We show that the process models discovered with the GPD method are more comprehensible and can be used to answer process questions, whereas process models discovered using standard process discovery techniques do not provide the insights needed. [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]

Process mining is concerned with the analysis, understanding and improvement of business processes. Process discovery, i.e. discovering a process model based on an event log, is considered the most challenging process mining task. State-of-the-art process discovery algorithms only discover local control flow patterns and are unable to discover complex, non-local patterns. Region theory based techniques, i.e. an established class of process discovery techniques, do allow for discovering such patterns. However, applying region theory directly results in complex, overfitting models, which is less desirable. Moreover, region theory does not cope with guarantees provided by state-of-the-art process discovery algorithms, both w.r.t. structural and behavioural properties of the discovered process models. In this paper we present an ILP-based process discovery approach, based on region theory, that guarantees to discover relaxed sound workflow nets. Moreover, we devise a filtering algorithm, based on the internal working of the ILP-formulation, that is able to cope with the presence of infrequent, exceptional behaviour. We have extensively evaluated the technique using different event logs with different levels of exceptional behaviour. Our experiments show that the presented approach allows us to leverage the inherent shortcomings of existing region-based approaches. The techniques presented are implemented and readily available in the HybridILPMiner package in the open-source process mining tool-kits ProM (http://promtools.org) and RapidProM (http://rapidprom.org). [ABSTRACT FROM AUTHOR]

The aim of process discovery is to build a process model from an event log without prior information about the process. The discovery of declarative process models is useful when a process works in an unpredictable and unstable environment since several allowed paths can be represented as a compact set of rules. One of the tools available in the literature for discovering declarative models from logs is the Declare Miner, a plug-in of the process mining tool ProM. Using this plug-in, the discovered models are represented using Declare , a declarative process modeling language based on ltl for finite traces. However, the high execution times of the Declare Miner when processing large sets of data hampers the applicability of the tool to real-life settings. Therefore, in this paper, we propose a new approach for the discovery of Declare models based on the combination of an Apriori algorithm and a group of algorithms for Sequence Analysis to enhance the time performance of the plug-in. The approach has been developed in a way that it is easy to be parallelized using two different partitioning methods: the search space partitioning, in which different groups of candidate constraints are processed in parallel, and the database partitioning, in which different chunks of the log are processed at the same time. The approach has been implemented in ProM in its sequential version and in two multi-threading implementations leveraging these two partitioning methods. All the new variants of the plug-in have been evaluated using a large set of synthetic and real-life event logs. [ABSTRACT FROM AUTHOR]

Event data are collected in logistics, manufacturing, finance, health care, customer relationship management, e‐learning, e‐government, and many other domains. The events found in these domains typically refer to activities executed by resources at particular times and for a particular case (i.e., process instances). Process mining techniques are able to exploit such data. In this article, we focus on process discovery. However, process mining also includes conformance checking, performance analysis, decision mining, organizational mining, predictions, recommendations, and so on. These techniques help to diagnose problems and improve processes. All process mining techniques involve both event data and process models. Therefore, a typical first step is to automatically learn a control‐flow model from the event data. This is very challenging, but in recent years, many powerful discovery techniques have been developed. It is not easy to compare these techniques since they use different representations and make different assumptions. Users often need to resort to trying different algorithms in an ad‐hoc manner. Developers of new techniques are often trying to solve specific instances of a more general problem. Therefore, we aim to unify existing approaches by focusing on log and model abstractions. These abstractions link observed and modeled behavior: Concrete behaviors recorded in event logs are related to possible behaviors represented by process models. Hence, such behavioral abstractions provide an “interface” between both of them. We discuss four discovery approaches involving three abstractions and different types of process models (Petri nets, block‐structured models, and declarative models). The goal is to provide a comprehensive understanding of process discovery and show how to develop new techniques. Examples illustrate the different approaches and pointers to software are given. The discussion on abstractions and process representations is also presented to reflect on the gap between process mining literature and commercial process mining tools. This facilitates users to select an appropriate process discovery technique. Moreover, structuring the role of internal abstractions and representations helps broaden the view and facilitates the creation of new discovery approaches. This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Application Areas > Business and Industry Technologies > Machine Learning Application Areas > Data Mining Software Tools [ABSTRACT FROM AUTHOR]

Nowadays, large-scale business processes is growing rapidly; in this regards process mining is required to discover and enhance business processes in different departments of an organization. A process mining algorithm can generally discover the process model of an organization without considering the detailed process models of the departments, and the relationship among departments. The exchange of messages among departments can produce asynchronous activities among department process models. The event logs from departments can be considered as multi-source logs, which cause difficulties in mining the process model. Discovering process models from multi-source logs is still in the state of the art, therefore this paper proposes a hierarchy high-to-low process mining approach to discover the process model from a complex multi-source and heterogeneous event logs collected from distributed departments. The proposed method involves three steps; i.e. firstly a high level process model is developed; secondly a separate low level process model is discovered from multi-source logs; finally the Petri net refinement operation is used to integrate the discovered process models. The refinement operation replaced the abctract transitions of a high level process model with the corresponding low level process models. Multisource event logs from several departments of a yarn manufacturing were used in the computational study, and the results showed that the proposed method combined with the modified time-based heuristics miner could discover a correct parallel process business model containing XOR, AND, and OR relations. [ABSTRACT FROM AUTHOR]

Evaluating the quality of discovered process models is an important task in many process mining analyses. Currently, several metrics measuring the fitness, precision and generalization of a discovered model are implemented. However, there is little empirical evidence how these metrics relate to each other, both within and across these different quality dimensions. In order to better understand these relationships, a large-scale comparative experiment was conducted. The statistical analysis of the results shows that, although fitness and precision metrics behave very similar within their dimension, some are more pessimistic while others are more optimistic. Furthermore, it was found that there is no agreement between generalization metrics. The results of the study can be used to inform decisions on which quality metrics to use in practice. Moreover, they highlight issues which give rise to new directions for future research in the area of quality measurement. [ABSTRACT FROM AUTHOR]

Process-aware information systems (PAIS) are systems relying on processes, which involve human and software resources to achieve concrete goals. There is a need to develop approaches for modeling, analysis, improvement and monitoring processes within PAIS. These approaches include process mining techniques used to discover process models from event logs, find log and model deviations, and analyze performance characteristics of processes. The representational bias (a way to model processes) plays an important role in process mining. The BPMN 2.0 (Business Process Model and Notation) standard is widely used and allows to build conventional and understandable process models. In addition to the flat control flow perspective, subprocesses, data flows, resources can be integrated within one BPMN diagram. This makes BPMN very attractive for both process miners and business users, since the control flow perspective can be integrated with data and resource perspectives discovered from event logs. In this paper, we describe and justify robust control flow conversion algorithms, which provide the basis for more advanced BPMN-based discovery and conformance checking algorithms. Thus, on the basis of these conversion algorithms low-level models (such as Petri nets, causal nets and process trees) discovered from event logs using existing approaches can be represented in terms of BPMN. Moreover, we establish behavioral relations between Petri nets and BPMN models and use them to adopt existing conformance checking and performance analysis techniques in order to visualize conformance and performance information within a BPMN diagram. We believe that the results presented in this paper can be used for a wide variety of BPMN mining and conformance checking algorithms. We also provide metrics for the processes discovered before and after the conversion to BPMN structures. Cases for which conversion algorithms produce more compact or more complicated BPMN models in comparison with the initial models are identified. [ABSTRACT FROM AUTHOR]

Many information systems record executed process instances in the event log , a very rich source of information for several process management tasks, like process mining and trace comparison. In this paper, we present a framework, able to convert activities in the event log into higher level concepts, at different levels of abstraction, on the basis of domain knowledge. Our abstraction mechanism manages non trivial situations, such as interleaved activities or delays between two activities that abstract to the same concept. Abstracted traces can then be provided as an input to an intelligent system, meant to implement a variety of process management tasks, significantly enhancing the quality and the usefulness of its output. In particular, in the paper we demonstrate how trace abstraction can impact on the quality of process discovery, showing that it is possible to obtain more readable and understandable process models. We also prove, through our experimental results, the impact of our approach on the capability of trace comparison and clustering (realized by means of a metric able to take into account abstraction phase penalties) to highlight (in)correct behaviors, abstracting from details. [ABSTRACT FROM AUTHOR]

Recently, researchers discovered that the major problems of mining event logs is to discover a simple, sound and complete process model. But since the mining techniques can only reproduce the behaviour recorded in the log, the fitness of the reproduced model is a function of the event log completeness. In this paper, a Fuzzy-Genetic Mining model based on Bayesian Scoring Functions (FGM-BSF) which we called probabilistic approach was developed to tackle problems which emanated from the incomplete event logs. The main motivation of using genetic mining for the process discovery is to benefit from the global search performed by the algorithm. The incompleteness in processes deals with uncertainty and is tackled by using the probabilistic nature of the scoring functions in Bayesian network based on a fuzzy logic value prediction. The global search performed by the genetic approach is panacea to dealing with the population that has both good and bad individuals. Hence, the proposed approach helps to enhance a robust fitness function for the genetic algorithm through highlift traces representing only good individuals not detected by mining model without an intelligent system . The implementation of our approach was carried out on java platform with MySQL for event log parsing and preprocessing while the actual discovery was done in ProM. The results showed that the proposed approach achieved 0.98% fitness when compared with existing schemes. [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 discovery aims at constructing a model from a set of observations given by execution traces (a log). Petri nets are a preferred target model in that they produce a compact description of the system by exhibiting its concurrency. This article presents a process discovery algorithm using Petri net synthesis, based on the notion of region introduced by A. Ehrenfeucht and G. Rozenberg and using techniques from linear algebra. The algorithm proceeds in three successive phases which make it possible to find a compromise between the ability to infer behaviours of the system from the set of observations while ensuring a parsimonious model, in terms of fitness, precision and simplicity. All used algorithms are incremental which means that one can modify the produced model when new observations are reported without reconstructing the model from scratch. [ABSTRACT FROM AUTHOR]

Predictive modeling approaches in business process management provide a way to streamline operational business processes. For instance, they can warn decision makers about undesirable events that are likely to happen in the future, giving the decision maker an opportunity to intervene. The topic is gaining momentum in process mining, a field of research that has traditionally developed tools to discover business process models from data sets of past process behavior. Predictive modeling techniques are built on top of process-discovery algorithms. As these algorithms describe business process behavior using models of formal languages (e.g., Petri nets), strong language biases are necessary in order to generate models with the limited amounts of data included in the data set. Naturally, corresponding predictive modeling techniques reflect these biases. Based on theory from grammatical inference, a field of research that is concerned with inducing language models, we design a new predictive modeling technique based on weaker biases. Fitting a probabilistic model to a data set of past behavior makes it possible to predict how currently running process instances will behave in the future. To clarify how this technique works and to facilitate its adoption, we also design a way to visualize the probabilistic models. We assess the effectiveness of the technique in an experimental evaluation with synthetic and real-world data. [ABSTRACT FROM AUTHOR]

Traditional process mining techniques offer limited possibilities to analyze business processes working in low-predictable and dynamic environments. Recently, to close this gap, declarative process models have been introduced to represent process mining results since they allow for describing complex behaviors as a compact set of business rules. However, in this context, activities of a business process are still considered as atomic/instantaneous events. This is a strong limitation for these approaches because often, in realistic environments, process activities are not instantaneous but executed across a time interval and pass through a sequence of states of a lifecycle. This paper investigates how the existing techniques for the discovery of declarative process models can be adapted when the business process under analysis contains non-atomic activities. In particular, we base our proposed approach on the use of discriminative rule mining to determine how the characteristics of the activity lifecycles in a business process influence the validity of a business rule in that process. The approach has been implemented as a plug-in of the process mining tool ProM and validated on synthetic logs and on a real-life log recorded by an incident and problem management system called VINST in use at Volvo IT Belgium. [ABSTRACT FROM AUTHOR]

A software company can define a software process line (SPrL) to deal with projects with different characteristics. This entails defining a base process and its variation points; the SPrL is then tailored to each project. This approach avoids the co-evolution problems but is expensive to set up. In companies that register project events, this information could be used to discover the SPrL. However, traditional discovery algorithms focus on extracting a single process, which can be overly complex and would not be useful for managing future projects. Filtering out less frequent behavior leads to the discovery of simpler models, but these may not include relevant behavior. To address these issues, we propose the v-algorithm, which discovers a SPrL from process logs. Two thresholds split the log into three clusters based on relation frequency. The first one is used to generate the base process, the second one is used to identify variable elements, and the last one is discarded. We used the v-algorithm to discover the SPrL of Mobius, a small Chilean software company. We also discuss how the values of the thresholds affect the process discovery quality dimensions, extending existing metrics to the SPrL case. Copyright © 2016 John Wiley & Sons, Ltd. [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]

Collaborative groups encounter many challenges in their learning. They need to recognize challenges that may hinder collaboration, and to develop appropriate strategies to strengthen collaboration. This study aims to explore how groups progress in their socially shared regulation of learning (SSRL) in the context of computer-supported collaborative learning (CSCL). Teacher education students ( N = 103) collaborated in groups of three to four students during a two-month multimedia course. The groups used the Virtual Collaborative Research Institute (VCRI) learning environment along with regulation tools that prompted them to recognize challenges that might hinder their collaboration and to develop SSRL strategies to overcome these challenges. In the data analysis, the groups reported challenges, and the SSRL strategies they employed were analyzed to specify the focus and function of the SSRL. Process discovery was used to explore how groups progressed in their SSRL. The results indicated that depending on the phase of the course, the SSRL focus and function shifted from regulating external challenges towards regulating the cognitive and motivational aspects of their collaboration. However, the high-performing groups progressed in their SSRL in terms of evidencing temporal variety in challenges and SSRL strategies across time, which was not the case with low performing groups. [ABSTRACT FROM AUTHOR]