Combining Two Worlds: MonetDB with Multi-Dimensional Index Structure Support to Efficiently Query Scientific Data

Reproducibility and generalizability are important criteria for today’s data management society. Hence, stand-alone solutions that work well in isolation, but cannot convince at system level lead to a frustrating user experience. As a consequence, in our demo, we take the step of accelerating queries on scientific data by integrating the multi-dimensional index structure Elf into the main-memory-optimized database management system MonetDB. The overall intention is to show that the stand-alone speed ups of using Elf can also be observed when integrated into a holistic system storing scientific data sets. In our prototypical implementation, we demonstrate the performance of an Elf-backed MonetDB on the standard OLAP-benchmark, TPC-H, and the genomic multi-dimensional range query benchmark from the scientific data community. Queries can be run live on both benchmarks by the audience, while they are able to create different indexes to accelerate selection performance.