Improving throughout of continuous k-nearest neighbor queries with multi-threaded techniques

Traditional moving objects database has faced the rapid evolution of modern CMP processor. To evaluate massive concurrent continuous queries towards moving objects, parallel processing techniques and cache-conscious algorithms adapting to memory hierarchy and multi-core architecture should be developed to maximize the processor computation abilities. This paper introduces a multi-staged engine (MSE) for high performance and adaptable execution of massive concurrent continuous queries processing, which exploits pipeline strategy and departs the continuous query processing into three simultaneous stages: preprocessing, executing and dispatching modules to improve the parallelism with multi-threaded technology. Based on MSE framework and grid index for moving objects, we present a multi-threaded algorithm (MT-CNN) for massive continuous k nearest neighbor queries processing. MT-CNN algorithm uses threaded workload parallelism and cache-conscious execution reorganization strategies to improve the spatial and temporal locality. Experimental evaluation on a dual-core platform and analysis show that MT-CNN algorithm achieves a performance improvement over the existing traditional optimization counterparts.