In an increasingly digital world, collecting, processing, and exchanging personal data are critical drivers for enacting enterprise business processes. However, the long-term retention and access of personal data expose organizations to data breaches, in which sensitive and protected data are disclosed and exploited unauthorizedly. To mitigate the damage that data breaches can cause, in the European Union (EU), the right to data privacy is enforced through the General Data Protection Regulation (GDPR), which defines how organizations must store and manage EU citizens’ data. GDPR is highly influencing how organizations approach data privacy, forcing them to rethink and upgrade their business processes to become GDPR compliant, which can be daunting. In this paper, in line with the privacy-by-design principles of GDPR, we propose a methodology that shows how to capture the main privacy GDPR constraints in the form of design patterns and integrate them into business process models specified in BPMN (Business Process Model and Notation). This allows us to achieve full transparency of privacy constraints in business processes, making it possible to ensure their compliance with GDPR at design-time. We adopt a design science research approach to present our methodology and make design decisions explicit. We also introduce GDPR-Pilot, a BPMN editor that assists process designers and Data Controllers in integrating GDPR patterns into existing models. The methodology is evaluated through real-world use cases against structural, usage, and environmental requirements.

Declarative process models allow us to capture the behavior of a business process through temporal constraints on the evolution of process activities. In process mining, declarative process discovery focuses on deriving these constraints from event logs. Although the semantic aspects of declarative processes have been extensively investigated, there has been less focus on designing declarative visual notations that enhance model understanding and support analysts in solving process mining tasks. To improve the human understandability of declarative process models, in this paper, we present easyDeclare , a novel visual notation to specify declarative process models using the Declare language. easyDeclare was developed with consideration of the well-established Moody’s design principles. We conducted extensive user experiments to demonstrate that easyDeclare , when compared with the original graphical representation of Declare , reduces the cognitive load required to interpret Declare models of increasing complexity, making it a promising alternative to enhancing overall comprehension of declarative process discovery tasks.

Controlled experiments in Process Mining primarily rely on synthetic event logs generated from declarative or procedural process modeling languages, which often lack the flexibility needed for precise experimental setups. In this paper, we introduce a novel log generator designed to address this gap by enabling fine-tuned customization of synthetic logs. The log generator, implemented using the declarative language Answer Set Programming (ASP), allows researchers to define sophisticated scenarios that are impossible to express with standard log generators to create specific experimental conditions. • Fine-grained and flexible event log generation with a new position-based modeling language ( PosLan ) and Answer-Set Programming. • Support for synthetic logs with characteristics that mimic complex real-world patterns. • Variability control of the generated event log via Answer-Set Programming.

Together with Information Systems, we celebrate the journal’s 50th anniversary and the 10th anniversary of our joint work on a systematic framework for compliance monitoring functionalities.

This paper addresses the problem of suffix prediction in Business Process Management (BPM) by proposing a Neuro-Symbolic Predictive Process Monitoring (PPM) approach that integrates data-driven learning with temporal logic-based prior knowledge. While recent approaches leverage deep learning models for suffix prediction, they often fail to satisfy even basic logical constraints due to the absence of explicit integration of domain knowledge during training. We propose a novel method to incorporate Linear Temporal Logic over finite traces (LTLf) into the training process of autoregressive sequence predictors. Our approach introduces a differentiable logical loss function, defined using a soft approximation of LTLf semantics and the Gumbel-Softmax trick, which can be combined with standard predictive losses. This ensures the model learns to generate suffixes that are both accurate and logically consistent. Experimental evaluation on three real-world datasets shows that our method improves suffix prediction accuracy and compliance with temporal constraints. We also introduce two variants of the logic loss (local and global) and demonstrate their effectiveness under noisy and realistic settings. While developed in the context of BPM, our framework is applicable to any symbolic sequence generation task and contributes toward advancing Neuro-Symbolic AI.

This paper presents one of the first contributions in the context of Multimodal Predictive Process Monitoring (MM-PPM) . In recent years, Predictive Process Monitoring (PPM) has evolved at the intersection of process mining, machine learning, and data science, as organizations seek to anticipate the future course of ongoing processes. Traditional PPM mainly relies on structured event log data, but many real-world scenarios generate richer information, including text, images, audio, and video. MM-PPM promises to start addressing this rich data scenario by integrating complementary knowledge from heterogeneous modalities through modality-specific representations and information fusion techniques. The growing digitization of healthcare systems, combined with advances in Artificial Intelligence (AI), has accelerated AI-based PPM for analyzing sequences of clinical events, supporting decision-making, enabling personalized care, and improving clinical facility management. Given these characteristics, clinical pathways represent an ideal domain for experimenting with MM-PPM, as they may naturally involve diverse modalities such as structured records, free-text notes, or medical images. To handle multimodal information available with clinical pathways, we introduce MEDUSA , an MM-PPM approach for outcome prediction, which jointly processes medical image information coupled with the storytelling of structural records and text notes collected during the clinical pathway of a patient until the acquisition of the considered image. The evaluation of MEDUSA is done in a COVID-19 case study, to assess the performance of the proposed approach and explain how specific information within each modality influences the decisions of the predictive model.

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

Detecting and repairing anomalies in business process event logs is essential for maintaining process integrity. Building on the observation that real-world anomalies often exhibit specific patterns and extending our earlier semi-supervised, rule-based approach to detect and repair common basic patterns, this paper presents an anomaly detection and repair framework powered by reinforcement learning. The proposed approach automatically learns an intelligent policy to identify and correct typical basic anomaly patterns, moving beyond the limitations of heuristic, rule-based methods. Furthermore, thanks to the flexibility provided by reinforcement learning, the approach can handle more complex patterns formed by combinations of the basic ones. Extensive evaluation using both synthetic and realworld logs demonstrates that the proposed method outperforms traditional trace alignment, edit distance-based techniques, and unsupervised deep learning in the accuracy of anomalous trace repair. It also shows consistent performance across various anomaly types and offers a pattern categorization capability that baseline methods lack.

AI-augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems empowered by AI technology for autonomously unfolding and adapting the execution flow of business processes (BPs) within a set of potentially conflicting procedural and declarative constraints, called process framing . In this respect, framed autonomy enables an ABPMS to autonomously decide how to progress the execution of a BP, as long as the boundaries imposed by the frame are respected. Among these constraints, there could be a partial BP execution that needs to be completed, activating a different near-optimal framing that enables the BP to progress its execution. In this paper, we present an automata-based technique that pairs constraint-based framing with automated planning in AI to recommend, given a partial BP execution trace, the continuation of that trace that minimizes the violation cost of the conforming space defined by the process frame. We report on the results of experiments of increasing complexity to showcase our technique’s performance and scalability.

In the context of process mining, alignments are increasingly being adopted for conformance checking, due to their ability in providing sophisticated diagnostics on the nature and extent of deviations between observed traces and a reference process model. On the downside, deriving alignments is challenging from the computational point of view, even more so when dealing with multiple perspectives in the process, such as, in particular, data. In fact, every observed trace must in principle be compared with infinitely many model traces. In this work, we tackle this computational bottleneck by borrowing the classical idea of encoding from machine learning. Instead of computing alignments directly and exactly, we do so in an approximate way after applying a lossy trace encoding that maps each trace into a corresponding compact, vectorial representation that retains only certain information of the original trace. We study trace encoding-based approximate alignments for processes equipped with event data attributes, from three different angles. First, we indeed show that computing approximate alignments in this way is much more efficient than in the exact setting. Second, we evaluate how accurate such approximate alignments are, considering different encoding strategies that focus on different features of the trace. Our findings suggest that sufficiently rich encodings actually yield good accuracy. Third, we consider the impact of frequency and density of model variants, comparing the effectiveness of using standard approximate multi-perspective alignments as opposed to a variant that incorporates probabilities. As a by-product of this analysis, we also obtain insights on how these two approaches perform in the presence of noise. • Approximate multi-perspective alignments based on trace encodings. • Formal framework to compute approximate alignments against Data Petri nets. • Extension dealing with trace probabilities. • Experimental evaluation witnessing efficiency and accuracy, also in the presence of noise.

Abstract Predictive Process Monitoring (PPM) is a field of Process Mining that aims at predicting how an ongoing execution of a business process will develop in the future using past process executions recorded in event logs. The recent stream of publications in this field shows the need for tools able to support researchers and users in comparing and selecting the techniques that are the most suitable for them. In this paper, we present , a dedicated tool for supporting users in building, comparing and explaining the PPM models that can then be used to perform predictions on the future of an ongoing case. has been constructed by carefully considering the necessary capabilities of a PPM tool and by implementing them in a client-server architecture able to support modularity and scalability. The features of support researchers and practitioners within the entire pipeline for constructing reliable PPM models. The assessment using reactive design patterns and load tests provides an evaluation of the interaction among the architectural elements, and of the scalability with multiple users accessing the prototype in a concurrent manner, respectively. By providing a rich set of different state-of-the-art approaches, offers to Process Mining researchers and practitioners a useful and flexible instrument for comparing and selecting PPM techniques.

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

Plan Recognition is the task of identifying the goals and plans of an agent by observing its behavior within the environment. The problem has been extensively studied in the context of planning, in particular bringing forward stochastic techniques dealing with probability distributions over the possible agent goals, under the assumption that observations are reliable. More recently, a connection between this problem and process mining techniques has been established, paving the way towards the application of alignment-based conformance checking techniques from process mining to tackle plan recognition problems in a setting where observations may be faulty. In this work, we reconcile these two lines of research in a unified framework that deals at once with uncertainty over the goals and the faithfulness of observations. Instead of using ad-hoc techniques to solve this problem, we cast it as a probabilistic trace alignment problem, trading off between the similarity of observations and plans, and the likelihood that the agent is performing those plans. We assess the effectiveness of our approach by conducting a comparative experimental evaluation on state-of-the-art benchmarks.

Within the process mining field, Deterministic Finite State Automata (DFAs) are largely employed as foundation mechanisms to perform formal reasoning tasks over the information contained in the event logs, such as conformance checking, compliance monitoring and cross-organization process analysis, just to name a few. To support the above use cases, in this paper, we investigate how to leverage Model Learning (ML) algorithms for the automated discovery of DFAs from event logs. DFAs can be used as a fundamental building block to support not only the development of process analysis techniques, but also the implementation of instruments to support other phases of the Business Process Management (BPM) lifecycle such as business process design and enactment. The quality of the discovered DFAs is assessed wrt customized definitions of fitness, precision, generalization, and a standard notion of DFA simplicity. Finally, we use these metrics to benchmark ML algorithms against real-life and synthetically generated datasets, with the aim of studying their performance and investigate their suitability to be used for the development of BPM tools.

So far, approaches for business process modeling, enactment and monitoring have mainly based on process specifications consisting of a single process model. This setting aptly captures monolithic scenarios from domains in which all possible behaviors can be folded into a single model. However, this strategy cannot be applied to domains where multiple interacting (procedural) processes simultaneously work over the same objects, in the presence of additional (declarative) constraints relating activities from the same or different processes. A relevant example for this setting is that of healthcare, where co-morbid patients may be subject to multiple clinical pathways at once, in the presence of additional, general constraints capturing basic medical knowledge. To fill this gap, we have previously presented the M3 Framework and an accompanying monitoring technique, which allows for a hybrid representation of a process using both procedural and declarative models, and supports the modular creation of multi-process specifications where domain experts can focus on specific procedures and domain constraints without being forced to merge them into one single specification. In this paper, we make significant extensions to this framework, allowing us to go from simple toy examples towards addressing practical real-life scenarios. We achieve this by introducing a richer form of integration between the interacting process components, in particular supporting asynchronous and synchronous activities that may operate over local and global (shared) data variables. This is framed by a discussion of the business meaning of these concepts, the introduction of the corresponding modeling patterns, and the application of our approach to real-life business processes, the latter being the driving-force behind this paper.

Process Mining is a family of techniques for analyzing business process execution data recorded in event logs. Process models can be obtained as output of automated process discovery techniques or can be used as input of techniques for conformance checking or model enhancement. In Declarative Process Mining, process models are represented as sets of temporal constraints (instead of procedural descriptions where all control-flow details are explicitly modeled). An open research direction in Declarative Process Mining is whether multi-perspective specifications can be supported, i.e., specifications that not only describe the process behavior from the control-flow point of view, but also from other perspectives like data or time. In this article, we address this question by considering SAT (Propositional Satisfiability Problem) as a solving technology for a number of classical problems in Declarative Process Mining, namely, log generation, conformance checking, and temporal query checking. To do so, we first express each problem as a suitable FO (First-Order) theory whose bounded models represent solutions to the problem, and then find a bounded model of such theory by compilation into SAT.

As the need to understand and formalise business processes into a model has grown over the last years, the process discovery research field has gained more and more importance, developing two different classes of approaches to model representation: procedural and declarative. Orthogonally to this classification, the vast majority of works envisage the discovery task as a one-class supervised learning process guided by the traces that are recorded into an input log. In this work instead, we focus on declarative processes and embrace the less-popular view of process discovery as a binary supervised learning task, where the input log reports both examples of the normal system execution, and traces representing a “stranger” behaviour according to the domain semantics. We therefore deepen how the valuable information brought by both these two sets can be extracted and formalised into a model that is “optimal” according to user-defined goals. Our approach, namelyNegDis, is evaluated w.r.t. other relevant works in this field, and shows promising results regarding both the performance and the quality of the obtained solution.

Trace alignment is the problem of finding the best possible execution sequence of a business process (BP) model that reproduces an (observed) execution trace of the same BP by pinpointing where it deviates. One limiting assumption that governs the state-of-the-art alignment algorithms relies in a static cost function assigning fixed costs to all the possible types of deviations related to a BP activity, thus neglecting the specific context in which the deviation takes place and flattening the analysis of its potential impact. In this paper, we relax this assumption by providing a technique based on theoretic manipulations of deterministic finite state automata (DFAs) to build optimal alignments driven by dedicated cost models that assign context-dependent variable costs to the deviations. We show how the algorithm can be implemented relying on automated planning in Artificial Intelligence (AI), which is proven to be an effective tool to address the alignment task in the case of BP models and event logs of remarkable size. Finally, we report on the results of experiments conducted in a real-life case study on incident management and on larger synthetic ones performed through three well-known planning systems to showcase the performance, scalability and versatility of our technique.

The assessment of behavioral rules with respect to a given dataset is key in several research areas, including declarative process mining, association rule mining, and specification mining. An assessment is required to check how well a set of discovered rules describes the input data, and to determine to what extent data complies with predefined rules. Particularly in declarative process mining, Support and Confidence are used most often, yet they are reportedly unable to provide a sufficiently rich feedback to users and cause rules representing coincidental behavior to be deemed as representative for the event logs. In addition, these measures are designed to work on a predefined set of rules, thus lacking generality and extensibility. In this paper, we address this research gap by developing a measurement framework for temporal rules based on (LTLpf). The framework is suitable for any temporal rules expressed in a reactive form and for custom measures based on the probabilistic interpretation of such rules. We show that our framework can seamlessly adapt well-known measures of the association rule mining field to declarative process mining. Also, we test our software prototype implementing the framework on synthetic and real-world data, and investigate the properties characterizing those measures in the context of process analysis.

Abstract Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances. Existing techniques in this field are able to predict, at each step of a process instance, the likelihood that it will lead to an undesired outcome. These techniques, however, focus on generating predictions and do not prescribe when and how process workers should intervene to decrease the cost of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive monitoring with the ability to generate alarms that trigger interventions to prevent an undesired outcome or mitigate its effect. The framework incorporates a parameterized cost model to assess the cost–benefit trade-off of generating alarms. We show how to optimize the generation of alarms given an event log of past process executions and a set of cost model parameters. The proposed approaches are empirically evaluated using a range of real-life event logs. The experimental results show that the net cost of undesired outcomes can be minimized by changing the threshold for generating alarms, as the process instance progresses. Moreover, introducing delays for triggering alarms, instead of triggering them as soon as the probability of an undesired outcome exceeds a threshold, leads to lower net costs.

Existing well-investigated Predictive Process Monitoring techniques typically construct a predictive model based on past process executions and then use this model to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution. This can make Predictive Process Monitoring too rigid to deal with the variability of processes working in real environments that continuously evolve and/or exhibit new variant behaviours over time. As a solution to this problem, we evaluate the use of three different strategies that allow the periodic rediscovery or incremental construction of the predictive model so as to exploit new available data. The evaluation focuses on the performance of the new learned predictive models, in terms of accuracy and time, against the original one, and uses a number of real and synthetic datasets with and without explicit Concept Drift. The results provide an evidence of the potential of incremental learning algorithms for predicting process monitoring in real environments.

Declarative business process (BP) models define the behavior of BPs as a set of temporal constraints, which can be summarized as a deterministic finite state automaton (DFA). Declarative BP discovery aims at inferring such constraints from event logs. To this aim, it requires as additional input the set of candidate constraints to be verified with respect to the event log. Intuitively, this restricts the discovery task to a conformance checking activity between a predefined set of constraint templates and an event log, preventing to learn any observed behavior that is not captured by those templates. In this paper, we investigate how to leverage Model Learning (ML) for the automated discovery of the DFA underlying the behavior of a declarative BP model, without using any further a-priori information in addition to the event log. To assess the quality of the discovered DFA, we introduce a novel definition of the standard process mining quality metrics, i.e., precision, generalization and simplicity, tailored to DFAs. Finally, a preliminary evaluation performed with real-life logs shows that ML enables to generate extremely simpler DFAs than state-of-the-art BP declarative discovery techniques, keeping similar values of precision and generalization.

Alignments provide sophisticated diagnostics that pinpoint deviations in a trace with respect to a process model. Alignment-based approaches for conformance checking have so far used crisp process models as a reference. Recent probabilistic conformance checking approaches check the degree of conformance of an event log as a whole with respect to a stochastic process model, without providing alignments. For the first time, we introduce a conformance checking approach based on trace alignments using stochastic Workflow nets. This requires to handle the two possibly contrasting forces of the cost of the alignment on the one hand and the likelihood of the model trace with respect to which the alignment is computed on the other.

Declarative process modeling languages are especially suitable to model loosely-structured, unpredictable business processes. One of the most prominent of these languages is Declare. The Declare language can be used for all process mining branches and a plethora of techniques have been implemented to support process mining with Declare. However, using these techniques can become cumbersome in practical situations where different techniques need to be combined for analysis. In addition, the use of Declare constraints in practice is often hampered by the difficulty of modeling them: the formal expression of Declare is difficult to understand for users without a background in temporal logics, whereas its graphical notation has been shown to be unintuitive. In this paper, we present RuM, a novel application for rule mining that addresses the abovementioned issues by integrating multiple Declare-based process mining methods into a single unified application. The process mining techniques provided in RuM strongly rely on the use of Declare models expressed in natural language, which has the potential of mitigating the barriers of the language bias. The application has been evaluated by conducting a qualitative user evaluation with eight process analysts.

The assessment of behavioral rules with respect to a given dataset is key in several research areas, including declarative process mining, association rule mining, and specification mining. The assessment is required to check how well a set of discovered rules describes the input data, as well as to determine to what extent data complies with predefined rules. In declarative process mining, in particular, some measures have been taken from association rule mining and adapted to support the assessment of temporal rules on event logs. Among them, support and confidence are used most often, yet they are reportedly unable to provide a sufficiently rich feedback to users and often cause spurious rules to be discovered from logs. In addition, these measures are designed to work on a predefined set of rules, thus lacking generality and extensibility. In this paper, we address this research gap by developing a general measurement framework for temporal rules based on Linear-time Temporal Logic with Past on Finite Traces (LTLpf). The framework is independent from the rule-specification language of choice and allows users to define new measures. We show that our framework can seamlessly adapt well-known measures of the association rule mining field to declarative process mining. Also, we test our software prototype implementing the framework on synthetic and real-world data, and investigate the properties characterizing those measures in the context of process analysis.

Robotic Process Automation (RPA) is a technology to develop software bots that automate repetitive sequences of interactions between users and software applications (a.k. a. routines). To take full advantage of this technology, organizations need to identify and to scope their routines. This is a challenging endeavor in large organizations, as routines are usually not concentrated in a handful of processes, but rather scattered across the process landscape. Accordingly, the identification of routines from User Interaction (UI) logs has received significant attention. Existing approaches to this problem assume that the UI log is segmented, meaning that it consists of traces of a task that is presupposed to contain one or more routines. However, a UI log usually takes the form of a single unsegmented sequence of events. This paper presents an approach to discover candidate routines from unsegmented UI logs in the presence of noise, i.e. events within or between routine instances that do not belong to any routine. The approach is implemented as an open-source tool and evaluated using synthetic and real-life UI logs.

Automated process discovery techniques enable users to generate business process models from event logs extracted from enterprise information systems.Traditional techniques in this field generate procedural process models (e.g., in the BPMN notation).When dealing with highly variable processes, the resulting procedural models are often too complex to be practically usable.An alternative approach is to discover declarative process models, which represent the behavior of the process as a set of constraints.Declarative process discovery techniques have been shown to produce simpler models than procedural ones, particularly for processes with high variability.However, the bulk of approaches for automated discovery of declarative process models focus on the control-flow perspective, ignoring the data perspective.This paper addresses the problem of discovering declarative process models with data conditions.Specifically, the paper tackles the problem of discovering constraints that involve two activities of the process such that each of these two activities is associated with a condition that must hold when the activity occurs.The paper presents and compares two approaches to the problem of discovering such conditions.The first approach uses clustering techniques in conjunction with a rule mining technique, while the second approach relies on redescription mining techniques.The two approaches (and their variants) are empirically compared using a combination of synthetic and real-life event logs.The experimental results show that the former approach outperforms the latter when it comes to re-discovering constraints artificially injected in a log.Also, the former approach is in most of the cases more computationally efficient.On the other hand, redescription mining discovers rules with higher confidence (and lower support) suggesting that it may be used to discover constraints that hold for smaller subsets of cases of a process.

Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity, or remaining cycle time of a given process case. These insights could be used to support operational managers in taking remedial actions as business processes unfold, e.g., shifting resources from one case onto another to ensure the latter is completed on time. A number of methods to tackle the remaining cycle time prediction problem have been proposed in the literature. However, due to differences in their experimental setup, choice of datasets, evaluation measures, and baselines, the relative merits of each method remain unclear. This article presents a systematic literature review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 such methods based on 17 real-life datasets originating from different industry domains.

Predictive business process monitoring refers to the act of making predictions about the future state of ongoing cases of a business process, based on their incomplete execution traces and logs of historical (completed) traces. Motivated by the increasingly pervasive availability of fine-grained event data about business process executions, the problem of predictive process monitoring has received substantial attention in the past years. In particular, a considerable number of methods have been put forward to address the problem of outcome-oriented predictive process monitoring, which refers to classifying each ongoing case of a process according to a given set of possible categorical outcomes—e.g., Will the customer complain or not? Will an order be delivered, canceled, or withdrawn? Unfortunately, different authors have used different datasets, experimental settings, evaluation measures, and baselines to assess their proposals, resulting in poor comparability and an unclear picture of the relative merits and applicability of different methods. To address this gap, this article presents a systematic review and taxonomy of outcome-oriented predictive process monitoring methods, and a comparative experimental evaluation of eleven representative methods using a benchmark covering 24 predictive process monitoring tasks based on nine real-life event logs.

Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy, and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures, and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering 12 publicly-available real-life event logs, 12 proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.

In recent years, monitoring the compliance of business processes with relevant regulations, constraints, and rules during runtime has evolved as major concern in literature and practice. Monitoring not only refers to continuously observing possible compliance violations, but also includes the ability to provide fine-grained feedback and to predict possible compliance violations in the future. The body of literature on business process compliance is large and approaches specifically addressing process monitoring are hard to identify. Moreover, proper means for the systematic comparison of these approaches are missing. Hence, it is unclear which approaches are suitable for particular scenarios. The goal of this paper is to define a framework for Compliance Monitoring Functionalities (CMF) that enables the systematic comparison of existing and new approaches for monitoring compliance rules over business processes during runtime. To define the scope of the framework, at first, related areas are identified and discussed. The CMFs are harvested based on a systematic literature review and five selected case studies. The appropriateness of the selection of CMFs is demonstrated in two ways: (a) a systematic comparison with pattern-based compliance approaches and (b) a classification of existing compliance monitoring approaches using the CMFs. Moreover, the application of the CMFs is showcased using three existing tools that are applied to two realistic data sets. Overall, the CMF framework provides powerful means to position existing and future compliance monitoring approaches.

Cloud computing is rapidly emerging as a new information technology that aims at providing improved efficiency in the private and public sectors, as well as promoting growth, competition, and business dynamism. Cloud computing represents, today, an opportunity also from the perspective of business process analytics since data recorded by process-centered cloud systems can be used to extract information about the underlying processes. Cloud computing architectures can be used in cross-organizational environments in which different organizations execute the same process in different variants and share information about how each variant is executed. If the process is characterized by low predictability and high variability, business rules become the best way to represent the process variants. The contribution of this paper consists in providing: (i) a cloud computing multi-tenancy architecture to support cross-organizational process executions; (ii) an approach for the systematic extraction/composition of distributed data into coherent event logs carrying process-related information of each variant; (iii) the integration of online process mining techniques for the runtime extraction of business rules from event logs representing the process variants running on the infrastructure. The proposed architecture has been implemented and applied for the execution of a real-life process for acknowledging an unborn child performed in four different Dutch municipalities.

Many processes are characterized by high variability, making traditional process modeling languages cumbersome or even impossible to be used for their description. This is especially true in cooperative environments relying heavily on human knowledge. Declarative languages, like Declare, alleviate this issue by not describing what to do step-by-step but by defining a set of constraints between actions that must not be violated during the process execution. Furthermore, in modern cooperative business, time is of utmost importance. Therefore, declarative process models should be able to take this dimension into consideration. Timed Declare has already previously been introduced to monitor temporal constraints at runtime, but it has until now only been possible to provide an alert when a constraint has already been violated without the possibility of foreseeing and avoiding such violations. In this paper, we introduce an extended version of Timed Declare with a formal timed semantics for the entire language. The semantics degenerates to the untimed semantics in the expected way. We also introduce a translation to timed automata, which allows us to detect inconsistencies in models prior to execution and to early detect that a certain task is time sensitive. This means that either the task cannot be executed after a deadline (or before a latency), or that constraints are violated unless it is executed before (or after) a certain time. This makes it possible to use declarative process models to provide a priori guidance instead of just a posteriori detecting that an execution is invalid. We also outline how a Declare model with time can be used in resource planning and how Declare has been integrated into CPN Tools.

Today, large business processes are composed of smaller, autonomous, interconnected subsystems, achieving modularity and robustness. Quite often, these large processes comprise software components as well as human actors, they face highly dynamic environments and their subsystems are updated and evolve independently of each other. Due to their dynamic nature and complexity, it might be difficult, if not impossible, to ensure at design-time that such systems will always exhibit the desired/expected behaviors. This, in turn, triggers the need for runtime verification and monitoring facilities. These are needed to check whether the actual behavior complies with expected business constraints, internal/external regulations and desired best practices. In this work, we present Mobucon EC, a novel monitoring framework that tracks streams of events and continuously determines the state of business constraints. In Mobucon EC, business constraints are defined using the declarative language Declare. For the purpose of this work, Declare has been suitably extended to support quantitative time constraints and non-atomic, durative activities. The logic-based language Event Calculus (EC) has been adopted to provide a formal specification and semantics to Declare constraints, while a light-weight, logic programming-based EC tool supports dynamically reasoning about partial, evolving execution traces. To demonstrate the applicability of our approach, we describe a case study about maritime safety and security and provide a synthetic benchmark to evaluate its scalability.

Process mining techniques can be used to effectively discover process models from logs with example behaviour. Cross-correlating a discovered model with information in the log can be used to improve the underlying process. However, existing process discovery techniques have two important drawbacks. The produced models tend to be large and complex, especially in flexible environments where process executions involve multiple alternatives. This “overload” of information is caused by the fact that traditional discovery techniques construct procedural models explicitly showing all possible behaviours. Moreover, existing techniques offer limited possibilities to guide the mining process towards specific properties of interest. These problems can be solved by discovering declarative models. Using a declarative model, the discovered process behaviour is described as a (compact) set of rules. Moreover, the discovery of such models can easily be guided in terms of rule templates. This paper uses DECLARE, a declarative language that provides more flexibility than conventional procedural notations such as BPMN, Petri nets, UML ADs, EPCs and BPEL. We present an approach to automatically discover DECLARE models. This has been implemented in the process mining tool ProM. Our approach and toolset have been applied to a case study provided by the company Thales in the domain of maritime safety and security.