Enhancing Predictive Process Monitoring with Sequential Graphs and Trace Attention

Predictive process monitoring, a technique within the field of process mining, aims primarily to forecast the next execution activity of ongoing traces. This capability enables organizations to proactively discover anomalies and enhance intelligent decision-making in business processes. Existing predictive methods only consider the individual context of traces or the structural constraints of process models, failing to comprehensively describe the state of ongoing traces. Therefore, this paper proposes SGAP (Sequential Graph for next Activity Prediction), a predictive model that combines contextual information within traces and collaborative behaviors among traces. In SGAP, sequential graphs are constructed by transforming activities and traces from event logs into nodes and edges, which offers a flexible framework for understanding the impact of various trace contexts on predictions. Moreover, to adaptively handle the message passing across multiple and heterogeneous edges in sequential graphs, we devise a trace attention mechanism that considers different sequential patterns of edges. Experimental results conducted over six real-world datasets demonstrate the effectiveness of sequential graph modeling on trace contexts and highlight the significance of SGAP in forecasting the next activities.