This article aims to foster a collaborative environment between the visual analytics and process mining communities by bringing together analysis methods, techniques, and tools from the process mining and visual analytics domains to devise a new knowledge-assisted, human-in-the-loop approach to process mining. Building on recent advances in methods emphasizing the role of human knowledge in analysis, we introduce knowledge-assisted interactive visual process mining (KAVA-PM) as a framework where analysts’ tacit knowledge and the externalizations of this knowledge play a key role. To achieve this, we extend an established conceptual model of KAVA that combines interactive visualizations and automated methods to support a richer process mining analysis practice that has human experts and their knowledge at its core. The paper outlines the key components of KAVA-PM as a conceptual model and its relations, proposes key analytical patterns, and demonstrates the use and validity of the patterns through usage scenarios. We then present challenges and open problems which we validate through a survey with experts. We anticipate that along with the conceptual model, these challenges will bring the VA and PM communities together along a shared research agenda where the role of humans and their knowledge is better established. • Conceptual model adapted to process mining for distinguishing between tacit and explicit knowledge. • Key research challenges validated by the visual analytics and process mining communities through an international survey.

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.

Counterfactual explanations are one of the prominent eXplainable Artificial Intelligence (XAI) techniques, and suggest changes to input data that could alter predictions, leading to more favourable outcomes. Existing counterfactual methods do not readily apply to temporal domains, such as that of process mining, where data take the form of traces of activities that must obey to temporal background knowledge expressing which dynamics are possible and which not. Specifically, counterfactuals generated off-the-shelf may violate the background knowledge, leading to inconsistent explanations. This work tackles this challenge by introducing a novel approach for generating temporally constrained counterfactuals, guaranteed to comply by design with background knowledge expressed in Linear Temporal Logic on process traces (LTLp). We do so by infusing automata-theoretic techniques for LTLp inside a genetic algorithm for counterfactual generation. The empirical evaluation shows that the generated counterfactuals are temporally meaningful and more interpretable for applications involving temporal dependencies.

Proactive scheduling creates robust offline schedules that optimize resource utilization and minimize job flow times. This work addresses scheduling challenges in business processes, often encountered in service systems, which differ from traditional applications like manufacturing due to inherent uncertainties in activity durations, and human resource availability. We model the business process scheduling problem (BPSP) as a variation of stochastic resource-constrained multi-project scheduling (RCMPSP), and apply process mining to infer unknown parameter values from historical event data. To overcome the randomness in activity durations, we transform the problem into its deterministic counterpart, and prove that the latter provides a lower bound on the Makespan of the stochastic problem. Our approach integrates data-driven Monte Carlo simulation with constraint programming to generate proactive schedules that guarantee, with high probability, that the Makespan remains below a predefined threshold. We evaluate our approach using synthetic datasets with varying levels of uncertainty and size. In addition, we apply the approach to a real-world dataset from an outpatient cancer hospital, demonstrating its effectiveness in optimizing the process Makespan by an average of 5% to 14%.

Predictive Process Monitoring leverages machine learning models to predict the future states of business processes. Most Predictive Process Monitoring approaches rely on black-box models which, while powerful, lack interpretability, limiting their applicability in critical decision-making scenarios. Explainable Predictive Process Monitoring has emerged to address this gap, focusing on delivering actionable and transparent insights into predictions. Current approaches, however, often fail to incorporate multiple process perspectives and granularity levels in their explanations, overlooking crucial factors that influence process outcomes. This paper proposes a novel Explainable Predictive Process Monitoring approach to deliver explanations, integrating multiple process perspectives at various levels of granularity. The proposed approach addresses the limitations of existing methods, providing comprehensive, process context-aware explanations. The effectiveness of the proposed method is assessed through experimental evaluations on real-life event logs, showing how the integration of diverse process perspectives improves the interpretability and predictive insights of local explanations with improvements ranging from 2%–3% to 15%–20% depending upon the event log under analysis.

Recent research in Computer Science has investigated the use of Deep Learning (DL) techniques to complement outcomes or decisions within a Discrete Event Simulation (DES) model. The main idea of this combination is to maintain a white box simulation model complement it with information provided by DL models to overcome the unrealistic or oversimplified assumptions of traditional DESs. State-of-the-art techniques in BPM combine Deep Learning and Discrete Event Simulation in a post-integration fashion: first an entire simulation is performed, and then a DL model is used to add waiting times and processing times to the events produced by the simulation model. In this paper, we aim at taking a step further by introducing RIMS (Runtime Integration of Machine Learning and Simulation). Instead of complementing the outcome of a complete simulation with the results of predictions a posteriori, RIMS provides a tight integration of the predictions of the DL model at runtime during the simulation. This runtime-integration enables us to fully exploit the specific predictions while respecting simulation execution, thus enhancing the performance of the overall system both w.r.t. the single techniques (Business Process Simulation and DL) separately and the post-integration approach. In particular, the runtime integration ensures the accuracy of intercase features for time prediction, such as the number of ongoing traces at a given time, by calculating them during directly the simulation, where all traces are executed in parallel. Additionally, it allows for the incorporation of online queue information in the DL model and enables the integration of other predictive models into the simulator to enhance decision point management within the process model. These enhancements improve the performance of RIMS in accurately simulating the real process in terms of control flow, as well as in terms of time and congestion dimensions. Especially in process scenarios with significant congestion – when a limited availability of resources leads to significant event queues for their allocation – the ability of RIMS to use queue features to predict waiting times allows it to surpass the state-of-the-art. We evaluated our approach with real-world and synthetic event logs, using various metrics to assess the simulation model's quality in terms of control-flow, time, and congestion dimensions.

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.

In recent years, trace generation has emerged as a significant challenge within the Process Mining community. Deep Learning (DL) models have demonstrated accuracy in reproducing the features of the selected processes. However, current DL generative models are limited in their ability to adapt the learned distributions to generate data samples based on specific conditions or attributes. This limitation is particularly significant because the ability to control the type of generated data can be beneficial in various contexts, enabling a focus on specific behaviours, exploration of infrequent patterns, or simulation of alternative "what-if" scenarios.In this work, we address this challenge by introducing a conditional model for process data generation based on a conditional variational autoencoder (CVAE). Conditional models offer control over the generation process by tuning input conditional variables, enabling more targeted and controlled data generation. Unlike other domains, CVAE for process mining faces specific challenges due to the multiperspective nature of the data and the need to adhere to control-flow rules while ensuring data variability. Specifically, we focus on generating process executions conditioned on control flow and temporal features of the trace, allowing us to produce traces for specific, identified sub-processes. The generated traces are then evaluated using common metrics for generative model assessment, along with additional metrics to evaluate the quality of the conditional generation.

Linear-time temporal logic on finite traces (LTLf) is rapidly becoming a de-facto standard to produce specifications in many application domains (including planning, business process management, run-time monitoring, and reactive synthesis). Several studies have challenged the satisfiability problem thus far. In this paper, we focus instead on unsatisfiable LTLf specifications, with the objective of extracting the subset of formulae that cause inconsistencies within them, i.e., the unsatisfiable cores. We provide four algorithms to this end, which leverage the adaptation of a range of state-of-the-art algorithms to LTLf satisfiability checking. We implement those algorithms extending the respective implementations and carry out an experimental evaluation on a set of reference benchmarks, restricting to the unsatisfiable specifications. The results put in evidence that the different algorithms and tools exhibit complementary features determining their efficiency and efficacy. Indeed, our findings suggest exploring different strategies and algorithmic solutions for the extraction of unsatisfiable cores from LTLf specifications, thus confirming the challenging and multi-faceted nature of this problem.

Recent research in Computer Science has investigated the use of Deep Learning (DL) techniques to complement outcomes or decisions within a Discrete Event Simulation (DES) model. The main idea of this combination is to maintain a white box simulation model but to complement it with information provided by DL models. State-of-the-art techniques in BPM combine Deep Learning and Discrete event simulation in a post-integration fashion: first an entire simulation is performed, and then a DL model is used to add waiting times and processing times to the events produced by the simulation model. In this paper, we aim at taking a step further by introducing RIMS (Runtime Integration of Machine Learning and Simulation). Instead of complementing the outcome of a complete simulation with the results of predictions "a posteriori", RIMS provides a tight integration of the predictions of the DL model at runtime during the simulation. This runtime-integration enables us to fully exploit the specific predictions thus enhancing the performance of the overall system both w.r.t. the single techniques (Business Process Simulation and DL) separately and the post-integration approach. The runtime-integration enables us to also incorporate the queue as an intercase feature in the DL model, thus further improving the performance in process scenarios where the queue plays an important role.

Prescriptive Process Monitoring systems aim at recommending, during the execution of a business process, interventions that, if followed, prevent poor performance of the process. Such interventions have to be (i) reliable: they have to guarantee the achievement of the desired outcome or performance and (ii) flexible: they cannot overturn the normal process execution. Most of the Prescriptive Process Monitoring solutions perform well in terms of recommendation reliability but provide the users with recommendations expressed in terms of specific activities that have to be executed without caring about their feasibility. We propose a new Outcome-Oriented Prescriptive Process Monitoring system recommending temporal relations among activities that have to be guaranteed during the process execution. The proposed system is based on a Machine Learning model that learns the correlations between temporal relations among activities and the (positive) outcome of the process. Then, given the prefix of an ongoing process, the model is queried to return the most promising recommendations. The main contribution is that the proposed system softens the mandatory execution of an activity at a given point in time, thus leaving more freedom to the user in deciding the interventions to put in place. This is achieved by providing recommendations that are expressed as Linear Temporal Logic formulas over activities. The proposed system has been widely assessed using a pool of 22 real-life datasets. The results demonstrate the reliability of the provided recommendations by achieving an F1 score higher than 90% on 18 datasets out of 22.

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.

Abstract In the last years, process mining has significantly matured and has increasingly been applied by companies in industrial contexts. However, with the growing number of process mining methods, practitioners might find it difficult to identify which ones to apply in specific contexts and to understand the specific business value of each process mining technique. This paper’s main objective is to develop a business-oriented framework capturing the main process mining use cases and the business-oriented questions they can answer. We conducted a Systematic Literature Review (SLR) and we used the review and the extracted data to develop a framework that (1) classifies existing process mining use cases connecting them to specific methods implementing them, and (2) identifies business-oriented questions that process mining use cases can answer. Practitioners can use the framework to navigate through the available process mining use cases and to identify the process mining methods suitable for their needs.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

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.

The enactment of business processes is generally supported by information systems that record data about each process execution (a.k.a. case). This data can be analyzed via a family of methods broadly known as process mining. Predictive process monitoring is a process mining technique concerned with predicting how running (uncompleted) cases will unfold up to their completion. In this paper, we propose a predictive process monitoring framework for estimating the probability that a given predicate will be fulfilled upon completion of a running case. The framework takes into account both the sequence of events observed in the current trace, as well as data attributes associated to these events. The prediction problem is approached in two phases. First, prefixes of previous (completed) cases are clustered according to control flow information. Second, a classifier is built for each cluster using event data attributes to discriminate between cases that lead to a fulfillment of the predicate under examination and cases that lead to a violation within the cluster. At runtime, a prediction is made on a running case by mapping it to a cluster and applying the corresponding classifier. The framework has been implemented in the ProM toolset and validated on a log pertaining to the treatment of cancer patients in a large hospital.

The capability to store data about Business Process (BP) executions in so-called Event Logs has brought to the identification of a range of key reasoning services (consistency, compliance, runtime monitoring, prediction) for the analysis of process executions and process models. Tools for the provi sion of these services typically focus on one form of reasoning alone. Moreover, they are often very rigid in dealing with forms of incomplete information about the process execution. While this enables the development of ad hoc solutions, it also poses an obstacle for the adoption of reasoning-based solutions in the BP community. In this paper, we introduce the notion of Structured Processes with Observability and Time (SPOT models), able to support incompleteness (of traces and logs), and temporal constraints on the activity duration and between activities. Then, we exploit the power of abduction to provide a flexible, yet computationally effective framework able to reinterpret key reasoning services in terms of incompleteness and observability in a uniform way.

The need to extend business process languages with the capability to model complex data objects along with the control flow perspective has lead to significant practical and theoretical advances in the field of Business Process Modeling (BPM).On the practical side, there are several suites for control flow and data modeling; nonetheless, when it comes to formal verification, the data perspective is abstracted away due to the intrinsic difficulty of handling unbounded data. On the theoretical side, there is significant literature providing decidability results for expressive data-aware processes. However, they struggle to produce a concrete impact as being far from real BPM architectures and, most of all, not providing actual verification tools. In this paper we aim at bridging such a gap: we provide a concrete framework which, on the one hand, being based on Petri Nets and relational models, is close to the widely used BPM suites, and on the other is grounded on solid formal basis which allow to perform formal verification tasks. Moreover, we show how to encode our framework in an action language so as to perform reachability analysis using virtually any state-of-the-art planner.

The debate on advantages and disadvantages of declarative versus procedural process modeling languages for different usage scenarios has been intense. Procedural languages are more suited for describing operational processes while declarative ones for expressing regulations/guidelines and, in many situations, the need of combining the benefits of the two rises. Instead of forcing modelers to use a hybrid language, we envisage to keep the two specifications separate and propose a technique that automatically adapts procedural models so as to comply with sets of declarative rules. This not only fits scenarios where, e.g., company processes have to be modified according to changing external rules, but, more in general, it presents a way to take advantage of the flexibility of declarative while maintaining the high level of support provided by procedural languages. Furthermore, by comparing the original and the resulting procedural models, the impact of rules is clearly exposed. In this paper, we frame the problem above by providing its theoretical characterization and propose an automata-based solution, which is then evaluated against approaches leveraging state-of-the-art techniques for process discovery and model repair.

The capability to store data about Business Process executions in so-called Event Logs has brought to the identification of a range of key reasoning services (consistency, compliance, runtime monitoring, prediction) for the analysis of process executions and process models. Tools for the provision of these services typically focus on one form of reasoning alone. Moreover, they are often very rigid in dealing with forms of incomplete information about the process execution. While this enables the development of ad hoc solutions, it also poses an obstacle for the adoption of reasoning-based solutions. In this paper we exploit the power of abduction to provide a flexible, and yet computationally effective framework able to reinterpret key reasoning services in terms of incompleteness and observability in a uniform and effective way.

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 modelling language based on LTL for finite traces. In this paper, we use a combination of an Apriori algorithm and a group of algorithms for Sequence Analysis to improve the performances of the Declare Miner. Using synthetic and real life event logs, we show that the new implemented core of the plug-in allows for a significant performance improvement.

Semantically annotating business processes is about enriching the elements of a process description (e.g., activities, events) with annotations taken from an ontology defining the domain semantics of the elements of the process. Semantic annotations enable to provide business analysts with tools and services that, by exploiting automated reasoning and inference engines, facilitate the analysis, search and validation of business processes. However, besides the benefits deriving from these automated services, semantic annotations have further unexplored implications on the modelling of business processes. In this paper we present a principled experimental user evaluation showing, as main outcomes, that semantic annotations also directly impact (i) on the modelling process, suggesting that they favour a more accurate and careful design of the models, and (ii) on the resulting process models, as the models obtained using semantic annotations are qualitatively better than those obtained without using them.

It is nowadays well-established that the construction of quality domain ontologies benefits from the involvement in the modelling process of more actors, possibly having different roles and skills. To be effective, the collaboration between these actors has to be fostered, enabling each of them to actively and readily participate to the development of the ontology, favoring as much as possible the direct involvement of the domain experts in the authoring activities. Recent works have shown that ontology modelling tools based on wikis' paradigm and technology could contribute in meeting these collaborative requirements. This paper investigates, both at the theoretical and empirical level, the effectiveness of wiki features for collaborative ontology authoring in supporting teamworks composed of domain experts and knowledge engineers, as well as their impact on the entire process of collaborative ontology modelling and entity lifecycle.

Using templates for conceptual modelling is becoming a fashionable way to guide and facilitate Domain Experts in providing rich and good quality knowledge. A possibility to build templates without starting from scratch is grounding them on existing foundational and core ontologies. In this paper we investigate how these ontologies can be effectively used for the construction of templates able to guarantee a balance between usability and rigorousness. We report findings and lesson learned from a survey carried out for the evaluation of templates built from existing foundational and core ontologies in the enterprise domain.

SUMMARY Web applications are often used to expose business processes implemented as software systems. This paper describes a technique for recovering business processes based on a dynamic analysis of the applications behavior. The technique described here does not require any access to internal software artifacts of the application, such as source code or documentation. An initial process is inferred to by means of the analysis of execution traces, in which the execution of GUI elements such as forms and links is recorded. The recovered process is then abstracted by clustering its elements according to four different criteria: structural, page‐based, dependency‐based and semantical. A case study has been conducted with the aim of evaluating understandability and readability of the reverse engineered processes as well as the clustering techniques used in refining them. Copyright © 2010 John Wiley & Sons, Ltd.

Specification models recovered from existing software applications can support developers in comprehending and checking the applications during maintenance and evolution operations. Often, in fact, a huge amount of business knowledge is embedded in the application implementation while documentation is not available or not aligned with the actual software implementation. In order to (re)acquire and preserve the business knowledge, specifications recovery techniques are adopted. In this paper we present reBPMN, a tool that recovers business process models from execution traces of target applications. It recovers the process exposed by means of Web interfaces and it applies a multi-objective process reduction technique, which minimizes at the same time process complexity, non-conformances, and loss of business content. This allows us to obtain processes having high readability by decreasing their structural complexity, while preserving the completeness of the described business and domain-specific information. A case study shows the effectiveness of reBPMN in recovering readable and business-meaningful processes.

Enriching business process models with semantic annotations that are taken from an ontology has become a crucial need in service provisioning, integration and composition, and business processes management. We represent semantically annotated business processes as part of an Web ontology lanuage knowledge base that formalizes the business process structure, the business domain, a set of criteria that describe correct semantic annotations, and a set of constraints that describe requirements on the business process itself. In this paper, we show how the Semantic Web representation and reasoning techniques can be 1) exploited by our aspect-oriented approach to modularize exception-handling (as well as other crosscutting) mechanisms and 2) effectively applied to formalize and automatically verify constraints on the management of exceptional flows (as well as other relevant flows) in business processes. The benefits of the Semantic Web and the aspect-oriented technologies are illustrated in a case study, where exceptional flows are modularized separately and managed at the semantic level due to the proposed approach.

Abstract Semantic annotations are a way to provide a precise meaning to business process elements, which supports reasoning on properties and constraints. Among the obstacles preventing widespread adoption of semantic annotations are the technical skills required to manage the formalization of the semantics and the difficulty of reconciling the different viewpoints of different analysts working on the same business process. In this paper, we support business analysts in the collaborative annotation of business processes by means of a tool inspired to the Wiki pages model. Using this tool, analysts can concurrently work on process elements, ontology concepts, process annotation or constraint specification. The underlying formalism is not exposed in the Wiki pages, where natural language templates are used. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper we consider the problem of sharing the costs of multicast transmissions in ad hoc wireless networks. Assuming that the receiving users are selfish, we provide strategy- proof mechanisms that are either optimally budget balanced or efficient for the case in which the distance-power gradient α =1 or the stations belong to a ne-dimensional Euclidean space.Then, by extending to multicasting previous results on wireless broadcasting,we show the existence of efficiently computable 2(3 d.1)-approximate budget balance mechanisms in any d -dimensional space for every α ≥ d.