Summary Recently, the mainstream hardware vendors such as Intel and AMD have made significant efforts to integrate the central processing unit (CPU) and the graphics processing unit (GPU) into a single chip, which forms a coupled CPU‐GPU architecture. Data transfer between the CPU and the GPU through a Peripheral Component Interconnect Express bus is eliminated on this architecture, which provides new opportunities for database community to optimize query processing. Because of the lack of comprehensive evaluation of database systems on coupled CPU‐GPU platforms, it is difficult for academic and industry researchers to make appropriate decisions on improvement and optimization directions. In this paper, we conduct an extensive experimental study to evaluate an online analytical processing system on Intel and AMD machines. The performance difference is measured and analyzed when executing queries on integrated GPUs and multicore CPUs. The impacts of various parameters, data sizes, and optimization techniques on performance are also investigated. The results provide preliminary insights into database query and operator behaviors on state‐of‐the‐art coupled CPU‐GPU architectures.

Previous studies have shown mining closed patterns provides more benefits than mining the complete set of frequent patterns, since closed pattern mining leads to more compact results and more efficient algorithms. It is quite useful in a data stream environment where memory and computation power are major concerns. This paper studies the problem of mining closed sequential patterns over data stream sliding windows. A synopsis structure IST (Inverse Closed Sequence Tree) is designed to keep inverse closed sequential patterns in current window. An efficient algorithm SeqStream is developed to mine closed sequential patterns in stream windows incrementally, and various novel strategies are adopted in SeqStream to prune search space aggressively. Extensive experiments on both real and synthetic data sets show that SeqStream outperforms PrefixSpan, CloSpan and BIDE by a factor of about one to two orders of magnitude.