Proteomes are characterized by large protein-abundance differences, cell-type- and time-dependent expression patterns and post-translational modifications, all of which carry biological information that is not accessible by genomics or transcriptomics. Here we present a mass-spectrometry-based draft of the human proteome and a public, high-performance, in-memory database for real-time analysis of terabytes of big data, called ProteomicsDB. The information assembled from human tissues, cell lines and body fluids enabled estimation of the size of the protein-coding genome, and identified organ-specific proteins and a large number of translated lincRNAs (long intergenic non-coding RNAs). Analysis of messenger RNA and protein-expression profiles of human tissues revealed conserved control of protein abundance, and integration of drug-sensitivity data enabled the identification of proteins predicting resistance or sensitivity. The proteome profiles also hold considerable promise for analysing the composition and stoichiometry of protein complexes. ProteomicsDB thus enables navigation of proteomes, provides biological insight and fosters the development of proteomic technology.

Enterprise applications such as available-to-promise (ATP), financial accounting, and dunning typically employ a mixed database workload with short-running transactional as well as analytical queries with resource-intensive aggregations. The latter type of queries can be significantly accelerated by using materialized views with pre-calculated aggregates. However, this speed-up comes with the cost of view maintenance which is necessary to guarantee consistency when the underlying data changes. In this paper, we evaluate existing view maintenance strategies in the context of a columnar in-memory database that is designed for mixed workloads. We propose a novel view maintenance strategy that takes the main-delta architecture and resulting merge process of columnar storage into account. A further contribution is a cost model which determines the best maintenance strategy given a specific workload. Our experiments using an ATP application show that our novel strategy outperforms other strategies in mixed workloads with an insert-ratio of more than 40 percent.

Database workloads generated by enterprise applications are comprised of short-running transactional as well as long-running analytical queries with resource-intensive aggregations. The expensive aggregate queries can be significantly accelerated by using materialized views. This speed-up, however, comes with the cost of materialized view maintenance which is necessary to guarantee consistency when the underlying data changes.While several view maintenance strategies are applicable in the context of an in-memory column store, their performance depends on various factors, most importantly the ratio between queries accessing the materialized view and queries altering the base data, called insert ratio. As a contribution in this paper, we propose algorithms that determine the best-performing view maintenance strategy based on the currently monitored factors. Using our novel materialized aggregate engine, we are able to switch between view maintenance strategies on demand. We have created cost models for the identified view maintenance strategies that determine at which insert ratio it is advisable to switch to another strategy. Our benchmarks in SanssouciDB reveal that for all identified workloads, switching between maintenance strategies is more beneficial than staying with a single strategy.