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