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.

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.

Data Petri nets (DPNs) have gained traction as a model for data-aware processes, thanks to their ability to balance simplicity with expressiveness, and because they can be automatically discovered from event logs. While model checking techniques for DPNs have been studied, more complex analysis tasks that are highly relevant for BPM are beyond methods known in the literature. We focus here on equivalence and inclusion of process behaviour with respect to language and configuration spaces, optionally taking data into account. Such comparisons are important in the context of key process mining tasks, namely process repair and discovery, and related to conformance checking. To solve these tasks, we propose approaches for bounded DPNs based on constraint graphs, which are faithful abstractions of the reachable state space. Though the considered verification tasks are undecidable in general, we show that our method is a decision procedure DPNs that admit a finite history set. This property guarantees that constraint graphs are finite and computable, and was shown to hold for large classes of DPNs that are mined automatically, and DPNs presented in the literature. The new techniques are implemented in the tool ada, and an evaluation proving feasibility is provided.

Within the growing area of data-aware processes, Data Petri nets (DPNs) with arithmetic data have recently gained popularity thanks to their ability to balance simplicity with expressiveness. DPNs can be automatically mined from event data, but these process discovery techniques typically come without any correctness guarantees. In particular, the generated models may violate the crucial property of data-aware soundness. While data-aware soundness can be checked automatically for a large class of models, nothing is known about how to repair such processes once a violation is detected. In this paper we are concerned with repairing DPNs so that the refined model satisfies the desired soundness properties. Our approach is based on conservative behavioural changes, which are minimally invasive in the sense that the behaviour of the repaired model coincides with that of the original model except for (prefixes of) traces that caused the violation. We show experimentally that the approach can be used to repair unsound DPNs from the literature.

Conformance checking is a key process mining task to confront the normative behavior imposed by a process model with the actual behavior recorded in a log. While this problem has been extensively studied for pure control-flow processes, data-aware conformance checking has received comparatively little attention. In this paper, we tackle the conformance checking problem for the challenging scenario of processes that combine data and control-flow dimensions. Concretely, we adopt the formalism of data Petri nets (DPNs) and show how solid, well-established automated reasoning techniques from the area of Satisfiability Modulo Theories (SMT) can be effectively harnessed to compute conformance metrics and optimal data-aware alignments. To this end, we introduce the CoCoMoT (Computing Conformance Modulo Theories) framework, with a fourfold contribution. First, we show how SMT allows to leverage SAT-based encodings for the pure control-flow setting to the data-aware case. Second, we introduce a novel preprocessing technique based on a notion of property-preserving clustering, to speed up the computation of conformance checking outputs. Third, we show how our approach extends seamlessly to the more comprehensive conformance checking artifacts of multi- and anti-alignments. Fourth, we describe a proof-of-concept implementation based on state-of-the-art SMT solvers, and report on experiments. Finally, we discuss how CoCoMoT directly lends itself to further process mining tasks like log analysis by clustering and model repair, and the use of SMT facilitates the support of even richer multi-perspective models, where, for example, more expressive DPN guards languages are considered or generic datatypes (other than integers or reals) are employed.