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.

Abstract Event logs are data sets recording the executions (called cases) of a business process. Several process discovery algorithms have been defined to mine event logs and discover models of how activities of logged processes are being executed (activity traces). In the process discovery problems, the Pareto principle plays an important role. In fact, it is quite common that a large portion of log traces is held by a small fraction of top-frequent variants. Hence, accounting for the expected Pareto distribution of event logs, traditional process discovery algorithms are commonly used to discover process models by analyzing the prevalent trace behaviors. However, the Pareto principle is not always verified, especially in complex processes, where the majority of traces in the event log is often spanned on a high number of top-frequent trace-variants. In addition, traditional process discovery algorithms perform an offline analysis of event logs, assuming that logged processes remain in a steady state over time. But, the steady state is rarely the real-world case due to conceptual drifts. In this study, we use the traditional process discovery algorithms under the dynamic conditions of real-world processes. To this aim, we define two approaches, namely $$ ^{\textsf{P}} $$ P and $$ ^{\textsf{NP}} $$ NP , which detect drifts in the conformance of traces to process models and discover new process models on drifted traces. In $$ ^{\textsf{P}} $$ P , a new process model is discovered on an extraction-based representation of a drift. In $$ ^{\textsf{NP}} $$ NP , a new process model is discovered on an abstraction-based representation of a drift. The experimental results analyze the performance of the proposed approaches, also compared to a few related methods, showing the effectiveness of $$ ^{\textsf{P}} $$ P in Pareto cases and $$ ^{\textsf{NP}} $$ NP in non-Pareto cases, respectively.

Process discovery aims to automatically discover a process model to explain the behavior of event traces recorded in an event log during the execution of the activities of an underlying process. Several powerful process discovery algorithms are already formulated in process mining to identify regular control-flow structures in event logs and strike different trade-offs between the accuracy in capturing the behavior recorded in the event $\log$ and the complexity of the derived process model. This is commonly done under the assumption that log event traces are distributed according to the Pareto principle with a large portion of event traces held by a small fraction of top-frequent variants. However, the Pareto principle is not always satisfied in several real-life, complex processes. For example, the majority of log event traces produced in various healthcare or gaming processes is often spanned on a high number of top-frequent varianttraces. Various techniques (e.g. event filtering, trace extraction and trace abstraction) are already formulated in process mining to cope traditional process discovery algorithms also with event logs that do not conform the Pareto principle. Following this line of research, we explore the performance of a trace extraction method introduced to support the quest for Pareto-like event trace distribution during process discovery. The trace extraction is done resorting to a deep embedding representation of event traces, which sees traces at an abstraction level that removes noise and anomalous trace excerpt by enabling the discovery of simpler process models with higher accuracy. The deep embedding is used in combination with clustering. Experiments with several benchmark event logs show the effectiveness of the two proposed methods also compared to prior methods.

Forecasting future states of running process instances is one of the main challenges of Predictive Process Monitoring (PPM). Several deep learning approaches have recently achieved a valuable accuracy performance by addressing this task. On the other hand, with the recent boom of Large Language Models (LLMs) in multiple fields, LLMs have started attracting attention in PPM research also. In this study, we leverage the rich context of textual data to transform information recorded in event logs in smart textual data ready for boosting accurate PPM learning. In detail, we propose LUPIN, a LLM approach to predict the activity suffix of a running process instance. First it encodes historical running process instances in semantic text stories formulated according to narrative templates that account for information recorded in the event log. Then it fine tunes a pre-trained LLM model – medium BERT – on the text stories of historic running instances of a business process, to predict the activity suffix of any future running instance of the same business process. Finally, LUPIN integrates the XAI Integrated Gradient (IG) algorithm to explain how each part of the textual description of a running process instance has an effect on the prediction of its activity completion. The experimental evaluation explores the accuracy performance of LUPIN compared to that of several related methods and draws insights from the explanation retrieved through the IG algorithm.

The evolution of Artificial Intelligence (AI) has been driven by two core components: data and algorithms. Historically, AI research has predominantly followed the Model-Centric paradigm, which focuses on developing and refining models, while often treating data as static. This approach has led to the creation of increasingly sophisticated algorithms, which demand vast amounts of manually labeled and meticulously curated data. However, as data becomes central to AI development, it is also emerging as a significant bottleneck. The Data-Centric AI (DCAI) paradigm shifts the focus towards improving data quality, enabling the achievement of accuracy levels that are unattainable with Model-Centric approaches alone. This special issue presents recent advancements in DCAI, offering insights into the paradigm and exploring future research directions, aiming to contextualize the contributions included in this issue.

Predictive process monitoring (PPM) techniques have become a key element in both public and private organizations by enabling crucial operational support of their business processes. Thanks to the availability of large amounts of data, different solutions based on machine and deep learning have been proposed in the literature for the monitoring of process instances. These state-of-the-art approaches leverage accuracy as main objective of the predictive modeling, while they often neglect the interpretability of the model. Recent studies have addressed the problem of interpretability of predictive models leading to the emerging area of Explainable AI (XAI). In an attempt to bring XAI in PPM, in this paper we propose a fully interpretable model for outcome prediction. The proposed method is based on a set of fuzzy rules acquired from event data via the training of a neuro-fuzzy network. This solution provides a good trade-off between accuracy and interpretability of the predictive model. Experimental results on different benchmark event logs are encouraging and motivate the importance to develop explainable models for predictive process analytics.

Predictive process monitoring has recently become one of the main enablers of data-driven insights in process mining. As an application of predictive analytics, process prediction is mainly concerned with predicting the evolution of running traces based on models extracted from historical event logs. This paper presents a process mining approach, which uses convolutional neural networks to equip the execution scenario of a business process with a means to predict the next activity in a running trace. The basic idea is to convert the temporal data enclosed in the historical event log of a business process into spatial data so as to treat them as images. To this purpose, every trace of the event log is first transformed into the set of its prefix traces (i.e. sequences of events that represent the prefix of a trace). These prefix traces are mapped into 2D image-like data structures. Created spatial data are finally used to train a Convolutional Neural Network, in order to learn a deep learning model capable to predict the next activity (i.e. the activity associated to the event occurring after the last event in the considered prefix trace). This predictive deep model can be employed as a powerful service to support participants in performing business processes since it guarantees a higher utilization by acting proactively in anticipation. Preliminary tests with two benchmark logs are carried out to investigate the viability of the proposed approach.