Next activity prediction in business process monitoring is crucial for optimizing resource allocation and decision-making in service-oriented environments. Existing approaches often fail to integrate control flow with event attributes, resulting in incomplete modeling of process dynamics and inability to capture temporal dependencies between events. We propose HiGPP (History-informed Graph-based Process Predictor), a novel method that constructs unified history-informed graphs from event logs, incorporating both control flow and multi-view event attributes. HiGPP innovatively encodes the temporal sequence and contextual data of event attributes using attribute-specific embedding layers and gated recurrent units (GRUs), effectively capturing historical dynamics within node embeddings. By leveraging GraphSAGE to aggregate neighborhood information, HiGPP refines embeddings to capture both local and global graph structures. HiGPP achieves superior performance in next activity prediction, with an average improvement of more than 2% in all evaluation metrics compared to the best baseline method. Our code is available at https://github.com/HiGPP/HiGPP .