Advances in modern hardware, such as increases in the size of main memory available on computers, have made it possible to analyze data at a much higher rate than before. In this paper, we demonstrate that there is tremendous room for improvement in the processing of analytical queries on modern commodity hardware. We introduce BIPie, an engine for query processing implementing highly efficient decoding, selection, and aggregation for analytical queries executing on a columnar storage engine in MemSQL. We demonstrate that these operations are interdependent, and must be fused and considered together to achieve very high performance. We propose and compare multiple strategies for decoding, selection and aggregation (with GROUP BY), all of which are designed to take advantage of modern CPU architectures, including SIMD. We implemented these approaches in MemSQL, a high performance hybrid transaction and analytical processing database designed for commodity hardware. We thoroughly evaluate the performance of the approach across a range of parameters, and demonstrate a two to four times speedup over previously published TPC-H Query 1 performance.

Over the last two releases SQL Server has integrated two specialized engines into the core system: the Apollo column store engine for analytical workloads and the Hekaton in-memory engine for high-performance OLTP workloads. There is an increasing demand for real-time analytics, that is, for running analytical queries and reporting on the same system as transaction processing so as to have access to the freshest data. SQL Server 2016 will include enhancements to column store indexes and in-memory tables that significantly improve performance on such hybrid workloads. This paper describes four such enhancements: column store indexes on in-memory tables, making secondary column store indexes on disk-based tables updatable, allowing B-tree indexes on primary column store indexes, and further speeding up the column store scan oper ator.

SQL Server 2012 introduced two innovations targeted for data warehousing workloads: column store indexes and batch (vectorized) processing mode. Together they greatly improve performance of typical data warehouse queries, routinely by 10X and in some cases by a 100X or more. The main limitations of the initial version are addressed in the upcoming release. Column store indexes are updatable and can be used as the base storage for a table. The repertoire of batch mode operators has been expanded, existing operators have been improved, and query optimization has been enhanced. This paper gives an overview of SQL Server's column stores and batch processing, in particular the enhancements introduced in the upcoming release.

We consider a problem of rigid body motion planning in a static 3D environment. In the past, methods based on random sampling like Probabilistic Roadmap and its variants proved to be able to efficiently solve many important instances of that problem. Performance of these methods degrades drastically in the presence of narrow passages. We propose a different approach to motion planning which combines elements of both cell decomposition methods and sampling based methods. We estimate signed distance to the boundary of free space at sampling points and use that information to guide farther exploration. Cell decomposition is used to generate deterministic sampling positions with non-uniform and dynamically adjusted densities. We report the results of experiments with implementation of our method.