In this paper, we study parallel join processing to improve the performance of the merge phase of sort-merge join by integrating all parallelism provided by mainstream CPUs. Modern CPUs support SIMD instruction sets with wider SIMD registers which allows to process multiple data items per each instruction. Thus, we devise an efficient parallel join algorithm, called Parallel Merge Join with SIMD instructions (PMJS). In our proposed algorithm, we utilize data parallelism by exploiting SIMD instructions. And we also accelerate the performance by avoiding the usage of conditional branch instructions. Furthermore, to take advantage of the multiple cores, our proposed algorithm is threaded in multi-thread environments. In our multi-thread algorithm, to distribute workload evenly to each thread, we devise an efficient workload balancing algorithm based on the kernel density estimator which allows to estimate the workload of each thread accurately.

MapReduce is considered as the de facto framework for storing and processing massive data due to its fascinating features: simplicity, flexibility, fault tolerance and scalability. However, since the MapReduce framework does not provide an efficient access method to data (i.e., an index), whole data should be retrieved even though a user wants to access a small portion of data. Thus, in this paper, we devise an efficient algorithm constructing quadtrees with MapReduce. Our proposed algorithms reduce the index construction time by utilizing a sampling technique to partition a data set. To improve the query performance, we extend the quadtree construction algorithm in which the adjacent nodes of a quadtree are integrated when the number of points located in the nodes is less than the predefined threshold. Furthermore, we present an effective algorithm for incremental update. Our experimental results show the efficiency of our proposed algorithms in diverse environments.

A triangle enumerating problem is one of fundamental problems of graph data. Although several triangle enumerating algorithms based on MapReduce have been proposed, they still suffer from generating a lot of intermediate data. In this paper, we propose the efficient MapReduce algorithms to enumerate every triangle in the massive graph based on a vertex partition. Since a triangle is composed of an edge and a wedge, our algorithms check the existence of an edge connecting the end-nodes of each wedge. To generate every triangle from a graph in parallel, we first split a graph into several vertex partitions and group the edges and wedges in the graph for each pair of vertex partitions. Then, we form the triangles appearing in each group. Furthermore, to enhance the performance of our algorithm, we remove the duplicated wedges existing in several groups. Our experimental evaluation shows the performance of our proposed algorithm is better than that of the state-of-the-art algorithm in diverse environments.