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.

With recent technological advances, process logs, which were traditionally deterministic in nature, are being captured from non-deterministic sources, such as uncertain sensors or machine learning models (that predict activities using cameras). In the presence of stochastically-known logs, logs that contain probabilistic information, the need for stochastic trace recovery increases, to offer reliable means of understanding the processes that govern such systems. We design a novel deep learning approach for stochastic trace recovery, based on Diffusion Denoising Probabilistic Models (DDPM), which makes use of process knowledge (either implicitly by discovering a model or explicitly by injecting process knowledge in the training phase) to recover traces by denoising. We conduct an empirical evaluation demonstrating state-of-the-art performance with up to a $\mathbf{2 5 \%}$ improvement over existing methods, along with increased robustness under high noise levels.

Business Process Simulation (BPS) is an approach to analyze the performance of business processes under different scenarios. For example, BPS allows us to estimate the impact of adding one or more resources on the cycle time of a process. The starting point of BPS is a process model annotated with simulation parameters (a BPS model). BPS models may be manually designed, based on information collected from stakeholders and from empirical observations, or automatically discovered from historical execution data. Regardless of its provenance, a key question when using a BPS model is how to assess its quality. In particular, in a setting where we are able to produce multiple alternative BPS models of the same process, this question becomes: How to determine which model is better, to what extent, and in what respect? In this context, this article studies the question of how to measure the quality of a BPS model with respect to its ability to accurately replicate the observed behavior of a process. Rather than pursuing a one-size-fits-all approach, the article recognizes that a process covers multiple perspectives. Accordingly, the article outlines a framework that can be instantiated in different ways to yield quality measures that tackle different process perspectives. The article defines a number of concrete quality measures and evaluates these measures with respect to their ability to discern the impact of controlled perturbations on a BPS model, and their ability to uncover the relative strengths and weaknesses of two approaches for automated discovery of BPS models. The evaluation shows that the proposed measures not only capture how close a BPS model is to the observed behavior, but they also help us to identify the sources of discrepancies.

The classical checking of declarative Linear Temporal Logic on Finite Traces (LTLf) specifications verifies whether conjunctions of sets of formulae are satisfied by collections of finite traces. The data on which the verification is conducted may be corrupted by a number of logging errors or execution deviations at the level of single elements within a trace. The ability to quantitatively assess the extent to which traces satisfy a process specification (and not only if they do so or not at all) is thus key, especially in process mining scenarios. Previous techniques proposed for this aim either require formulae to be extended with quantitative operators or cater to the coarse granularity of whole traces. In this paper, we propose a framework to devise probabilistic measures for declarative process specifications on traces at the level of events, inspired by association rule mining. Thereupon, we describe a technique that measures the degree of satisfaction of these specifications over bags of traces. To assess our approach, we conduct an evaluation with real-world data.

Measuring key performance indicators, such as queue lengths and waiting times, using event logs serve for improvement of resource-driven business processes. However, existing techniques assume the availability of complete life cycle information, including the time a case was scheduled for execution (aka arrival times). Yet, in practice, such information may be missing for a large portion of the recorded cases. In this paper, we propose a methodology to address missing life-cycle data by incorporating predicted information in business processes performance analysis. Our approach builds upon techniques from queueing theory and leverages supervised learning to accurately predict performance indicators based on an event log with missing data. Our experimental results using both synthetic and real-world data demonstrate the effectiveness of our approach.

Discovery plays a key role in data-driven analysis of business processes. The vast majority of contemporary discovery algorithms aims at the identification of control-flow constructs. The increase in data richness, however, enables discovery that incorporates the context of process execution beyond the control-flow perspective. A "control-flow first" approach, where context data serves for refinement and annotation, is limited and fails to detect fundamental changes in the control-flow that depend on context data. In this work, we thus propose a novel approach for combining the control-flow and data perspectives under a single roof by extending inductive process discovery. Our approach provides criteria under which context data, handled through unsupervised learning, take priority over control-flow in guiding process discovery. The resulting model is a process tree, in which some operators carry data semantics instead of control-flow semantics. We evaluate the approach using synthetic and real-world datasets and show that the resulting models are superior to state-of-the-art discovery methods in terms of measures that are based on multi perspective alignments.

Temporal analysis for online monitoring and improvement of complex systems such as hospitals, public transportation networks, or supply chains has been in the focus of several areas in operations management. These include queueing theory for bottleneck analysis, mathematical scheduling for resource assignments to customers, and inventory management for ordering products under uncertain demand. In recent years, with the increasing availability of data sensed by Internet-of-Things (IoT) infrastructures, these online temporal analyses drift towards automated and data-driven solutions. In this tutorial, we cover existing approaches to answer online temporal queries based on sensed data. We discuss two complementary angles, namely operations management and machine learning. The operational approach is driven by models, while machine learning methods are grounded in feature encoding. Both techniques require methods for translating low-level data readings coming from sensors into high-level activities with their temporal relations. Further, some of the techniques consider only dependencies of the sensed entities on their own individual histories, while others take into account dependencies between entities that share system resources. We outline the state-of-the-art in temporal querying, with demonstrations of interesting phenomena and main results using a real-world case study in the healthcare domain. Finally, we chart the territory of online data analytics for complex systems in a broader context and provide future research directions.