The field of graph processing and spatial query systems is moving towards scalable and efficient solutions. Researchers are focusing on developing novel frameworks and architectures that can handle large-scale graphs and massive moving objects. Asynchronous processing, distributed training, and load balancing are key innovations that are advancing the field. These advancements are enabling the development of systems that can process complex graphs and spatial queries in real-time, with improved accuracy and efficiency. Notable papers in this area include: ACGraph, which presents a novel asynchronous graph processing system optimized for SSD-based environments with constrained memory resources. Efficient Distributed Exact Subgraph Matching via GNN-PE, which proposes a lightweight dynamic correlation-aware load balancing and hot migration mechanism for distributed exact subgraph matching. A Distributed Training Architecture For Combinatorial Optimization, which proposes a distributed GNN-based training framework for combinatorial optimization. CheetahGIS, which architects a scalable and efficient system for processing streaming spatial queries over massive moving objects.