State of the art design-time compliance checking techniques are generally limited to verifying control-flow relations. In this paper, we propose an algorithm for verifying design-time compliance of business process models including data. For a faithful resemblance to real-world cases, we include a degree of uncertainty in the outcomes of activities, such that a single execution is not guaranteed to lead to a unique data state. We represent the compliance requirements using conditional obligations. Our proposed approach uses a symbolic representation to compactly represent the possible data states across an execution, and evaluates them as a constraint satisfaction problem to first prune the state space of paths that require no further analysis and finally to evaluate if an execution violates one of the regulatory requirements. The performance of the approach is evaluated against existing algorithms implemented in NuXMV. The results show that our approach significantly outperforms the existing algorithms across the board.

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.

Process mining (PM)-based goal recognition (GR) techniques, which infer goals or targets based on sequences of observed actions, have shown efficacy in real-world engineering applications. This study explores the applicability of PM-based GR in identifying target poses for users employing powered transhumeral prosthetics. These prosthetics are designed to restore missing anatomical segments below the shoulder, including the hand. In this article, we aim to apply the GR techniques to identify the intended movements of users, enabling the motors on the powered transhumeral prosthesis to execute the desired motions precisely. In this way, a powered transhumeral prosthesis can assist individuals with disabilities in completing movement tasks. PM-based GR techniques were initially designed to infer goals from sequences of observed actions, where discrete event names represent actions. However, the electromyography electrodes and kinematic sensors on powered transhumeral prosthetic devices register sequences of continuous, real-valued data measurements. Therefore, we rely on methods to transform sensor data into discrete events and integrate these methods with the PM-based GR system to develop target pose recognition approaches. Two data transformation approaches are introduced. The first approach relies on the clustering of data measurements collected before the target pose is reached (the clustering approach). The second approach uses the time series of measurements collected while the dynamic user movement to perform linear discriminant analysis (LDA) classification and identify discrete events (the dynamic LDA approach). These methods are evaluated through offline and human-in-the-loop (online) experiments and compared with established techniques, such as static LDA, an LDA classification based on data collected at static target poses, and GR approaches based on neural networks. Real-time human-in-the-loop experiments further validate the effectiveness of the proposed methods, demonstrating that PM-based GR using the dynamic LDA classifier achieves superior F 1 score and balanced accuracy compared to state-of-the-art techniques.

Goal Recognition (GR) is a research problem that studies ways to infer the goal of an intelligent agent based on its observed behavior and knowledge of the environment in which the agent operates. A common assumption of GR is that the environment is static. However, in many real-world scenarios, for example, recognizing customers’ preferences, it is necessary to recognize the goals of multiple agents or multiple goals of a single agent over an extended period. Therefore, it is reasonable to expect the environment to change throughout a series of goal recognition tasks. This paper presents three process mining-based solutions to the problem of adaptive GR in a changing environment implemented as different control strategies of a system for solving standard GR problems. As a standard GR system that gets controlled, we use the system grounded in process mining techniques, as it can adjust its internal GR mechanisms based on data collected while observing the operating agents. We evaluated our control strategies over synthetic and real-world datasets. The synthetic datasets were generated using the extended version of the Goal Recognition Amidst Changing Environments (GRACE) tool. The datasets account for different types of changes and drifts in the environment. The evaluation results demonstrate a trade-off between the GR performance over time and the effort invested in adaptations of the GR mechanisms of the system, showing that few well-planned adaptations can lead to a consistently high GR performance.

A transhumeral prosthesis restores missing anatomical segments below the shoulder, including the hand. Active prostheses utilize real-valued, continuous sensor data to recognize patient target poses, or goals, and proactively move the artificial limb. Previous studies have examined how well the data collected in stationary poses, without considering the time steps, can help discriminate the goals. In this case study paper, we focus on using time series data from surface electromyography electrodes and kinematic sensors to sequentially recognize patients' goals. Our approach involves transforming the data into discrete events and training an existing process mining-based goal recognition system. Results from data collected in a virtual reality setting with ten subjects demonstrate the effectiveness of our proposed goal recognition approach, which achieves significantly better precision and recall than the state-of-the-art machine learning techniques and is less confident when wrong, which is beneficial when approximating smoother movements of prostheses.

The problem of goal recognition requests to automatically infer an accurate probability distribution over possible goals an autonomous agent is attempting to achieve in the environment. The state-of-the-art approaches for goal recognition operate under full knowledge of the environment and possible operations the agent can take. This knowledge, however, is often not available in real-world applications. Given historical observations of the agents' behaviors in the environment, we learn skill models that capture how the agents achieved the goals in the past. Next, given fresh observations of an agent, we infer their goals by diagnosing deviations between the observations and all the available skill models. We present a framework that serves as an outline for implementing such data-driven goal recognition systems and its instance system implemented using process mining techniques. The evaluations we conducted using our publicly available implementation confirm that the approach is well-defined, i.e., all system parameters impact its performance, has high accuracy over a wide range of synthetic and real-world domains, which is comparable with the more knowledge-demanding state-of-the-art approaches, and operates fast.

Event logs capture the execution of business processes, such that each task is represented by an event and each individual execution is a chronological sequence of events, called an event trace. Event logs allow after-the-act and runtime analysis of deployed business processes to verify whether their execution complies with regulations and business requirements. Checking the compliance of a single sequence of events in a trace is straightforward and a number of approaches have been proposed to address this. However, some regulations or business rules span multiple process instances, requiring a cross-instance analysis. In order to check whether such requirements are maintained at all times, multiple traces need to be analysed together, which can result in a combinatorial computational complexity. In this article, we present a novel approach that efficiently checks runtime regulatory compliance based on event logs, while supporting cross-instance rule evaluation and extensible function evaluation over sequences of attribute data values. The efficiency and applicability of the proposed method is tested in a two-pronged evaluation, showing a significant improvement over existing techniques with respect to capabilities as well as computational complexity. The approach presented in this paper is subject to a patent application, with patent number WO2021/248201.

Organisations have to cope with large numbers of business rules and existing regulations governing the business in which they operate. Such rules are difficult to maintain due to their size and complexity, and it is increasingly challenging to ensure that each business process adheres to those rules. As such, automated extraction of business processes from rules has three clear advantages: (1) visualisation of all possible executions allowed by the rules, (2) automated execution and compliance by design, (3) identification of “inefficiencies” in the business rules. Existing approaches, however, only allow for the generation of partial traces based on input specifications and cannot handle many different input cases resulting in a full process. This paper presents a formal method to visualise and operationalise such sets of rules as a verifiable business process that is compliant by design, which allows us to analyse all possible execution paths. Additionally, we formally prove correctness of the business processes generated by our method. The approach is implemented in a tool and evaluated on both performance and correctness, showing that even for highly complex sets of rules the approach performs well and outperforms a well-known state-of-the-art approach. Evaluation on a real-life process shows the feasibility of the presented approach.

In this paper, we propose a methodology to verify the regulatory compliance of a composition of multiple interacting business process models. The proposed solution is based on providing a compliance certification for the components of the composition, and evaluating the compliance of the encompassing composition by aggregating the information shared by the certifications, going beyond existing techniques merely checking compliance on message flows. An advantage of adopting such methodology is that the computational details of the inner process models are not needed to verify compliance of the composition, so that companies can decide to not disclose such details while still being able to show that adopting their services would lead to compliant solutions in all possible scenarios.

The existence of different process variants is inevitable in many modern organizations. However, variability in business process support has proven to be a challenge as it requires a flexible business process specification that supports the required process variants, while at the same time being compliant with policies and regulations. Declarative approaches could support variability, by providing rules constraining process behavior and thereby allowing different variants. However, manual specification of these rules is complicated and error-prone. As such, tools are required to ensure that duplication and overlap of rules is avoided as much as possible, while retaining maintainability. In this paper, we present an approach to represent different process variants in a single compound prime event structure, and provide a method to subsequently derive variability rules from this compound prime event structure. The approach is evaluated by conducting an exploratory evaluation on different sets of real-life business process variants, including a real-life case from the Dutch eGovernment, to demonstrate the effectiveness and applicability of the approach.

Business processes design and execution environments increasingly need support from modular services in service compositions to offer the flexibility required by rapidly changing requirements. With each evolution, however, the service composition must continue to adhere to laws and regulations, resulting in a demand for automated compliance checking. Existing approaches, if at all, either offer only verification after the fact or linearize models to such an extent that parallel information is lost. We propose a mapping of service compositions to Kripke structures by using colored Petri nets. The resulting model allows preventative compliance verification using well-known temporal logics and model checking techniques while providing full insight into parallel executing branches and the local next invocation. Furthermore, the mapping causes limited state explosion, and allows for significant further model reduction. The approach is validated on a case study from a telecom company in Australia and evaluated with respect to performance and expressiveness. We demonstrate that the proposed mapping has increased expressiveness while being less vulnerable to state explosion than existing approaches, and show that even large service compositions can be verified preventatively with existing model checking techniques.

This article presents a method for checking the conformance between an event log capturing the actual execution of a business process, and a model capturing its expected or normative execution. Given a process model and an event log, the method returns a set of statements in natural language describing the behavior allowed by the model but not observed in the log and vice versa. The method relies on a unified representation of process models and event logs based on a well-known model of concurrency, namely event structures. Specifically, the problem of conformance checking is approached by converting the event log into an event structure, converting the process model into another event structure, and aligning the two event structures via an error-correcting synchronized product. Each difference detected in the synchronized product is then verbalized as a natural language statement. An empirical evaluation shows that the proposed method can handle real datasets and produces more concise and higher-level difference descriptions than state-of-the-art conformance checking methods. In a survey designed according to the technology acceptance model, practitioners showed a preference towards the proposed method with respect to a state-of-the-art baseline.

Organisations typically have to cope with large numbers of business rules and existing regulations governing the business in which they operate. Due to the size and complexity of those rules, maintenance is difficult and it is increasingly complicated to ensure that each business process adheres to those rules. As such, automated extraction of business processes from rules has a number of clear advantages: (1) visualisation of all possible executions allowed by the rules, (2) automated execution and compliance by design, (3) identification of "inefficiencies" in the business rules. Existing approaches, however, only allow to generate partial traces based on input specifications and cannot handle many different input cases resulting in a full process. This paper presents a formal method to visualise and operationalise such sets of rules as a verifiable business process that is compliant by design and allows us to analyse all possible execution paths. In addition, it maintains information of all distinct input cases, to preserve dependencies between consecutive exclusive paths.

Nowadays, organizations have to adjust their business processes along with the changing environment in order to maintain a competitive advantage. Changing a part of the system to support the business process implies changing the entire system, which leads to complex redesign activities. In this paper, a bottom-up process mining and simulation-based methodology is proposed to be employed in redesign activities. The methodology starts with identifying relevant performance issues, which are used as basis for redesign. A process model is “mined” and simulated as a representation of the existing situation, followed by the simulation of the redesigned process model as prediction of the future scenario. Finally, the performance criteria of the current business process model and the redesigned business process model are compared such that the potential performance gains of the redesign can be predicted. We illustrate the methodology with three case studies from three different domains: gas industry, government institution and agriculture.