arXiv:2607. 01785v1 Announce Type: new Abstract: Next activity prediction helps service-oriented processes anticipate upcoming steps before delays, exceptions, or service-level risks occur.
arXiv:2607. 27797v1 Announce Type: new Abstract: Predictive process monitoring (PPM) leverages event logs to forecast the future of running process instances, for instance, predicting the next activity, the remaining time until case completion, or the time to the next event.
arXiv:2507. 22524v3 Announce Type: replace Abstract: We propose HGCN(O), a self-tuning toolkit using Graph Convolutional Network (GCN) models for event sequence prediction.
arXiv:2606. 15868v1 Announce Type: new Abstract: Next activity prediction (NAP) is a cornerstone of predictive process monitoring (PPM), enabling organizations to move from retrospective analysis to proactive process steering.
arXiv:2606. 18726v1 Announce Type: cross Abstract: Structurally constrained event sequence generation remains challenging because generated paths must preserve transition feasibility, temporal order, termination, and attribute consistency.
MISApp is a profile‑free framework that predicts the next mobile app a user will launch by learning multi‑hop session graphs. It captures transition dependencies across different structural ranges, incorporates temporal context and spatial categorization, and models intent evolution from recent interactions. Experiments on two real‑world datasets show MISApp outperforms baselines in both standard and cold‑start settings while remaining efficient, and analyses reveal that multi‑hop relations provide higher‑order predictive signals and interpretable attention weights.
The paper introduces a Unified Particle Filter LSTM (Unified PF‑LSTM) for data‑driven process simulation, which maintains a weighted set of recurrent‑state hypotheses to better capture latent process conditions from incomplete event logs. By summarizing this particle belief with a weighted mean and moment‑generating‑function features, the model predicts next‑activity probabilities and conditional sojourn‑time quantiles. Experiments on three real‑world emergency department datasets show that the framework consistently outperforms existing data‑driven baselines in reproducing routing, duration, and system‑level behavior, especially when process dynamics are only partially reflected in the logs.
The paper introduces a decision‑aware suffix prediction framework that combines neural suffix predictors with decision mining rules extracted from event logs. By incorporating case‑ and event‑level attributes into the prediction process, the approach improves accuracy for short prefixes and rare process variants. Experiments on multiple event logs demonstrate that the framework not only enhances prediction performance but also provides intrinsic interpretability through mined decision rules.
arXiv:2604. 25834v2 Announce Type: replace Abstract: With the rapid development of the Internet, users have increasingly higher expectations for the recommendation accuracy of online content consumption platforms.
arXiv:2608.28237v1 Announce Type: new Abstract: Predictive Process Monitoring (PPM) models are increasingly deployed in dynamic environments where concept drift causes the underlying process distribu...
arXiv:2607. 23556v1 Announce Type: cross Abstract: Temporal graphs are increasingly used to model dynamic systems in diverse domains such as social networks, financial networks, and traffic networks.
arXiv:2306.11313v5 Announce Type: replace-cross Abstract: Point process models are widely used for continuous-time discrete-event data, where each data point includes time and additional information...