No abstract

A shared-nothing architecture is state-of-the-art for deploying a distributed analytical in-memory database management system: it preserves the in-memory performance advantage by processing data locally on each node but is difficult to scale out. Modern switched fabric communication links such as InfiniBand narrow the performance gap between local and remote DRAM data access to a single order of magnitude. Based on these premises, we introduce a distributed in-memory database architecture that separates the query execution engine and data access: this enables a) the usage of a large-scale DRAM-based storage system such as Stanford's RAMCloud and b) the push-down of bandwidth-intensive database operators into the storage system. We address the resulting challenges such as finding the optimal operator execution strategy and partitioning scheme. We demonstrate that such an architecture delivers both: the elasticity of a shared-storage approach and the performance characteristics of operating on local DRAM.

Over the last decade, the role of information technology in enterprises has been transforming from one of providing automation services to one of enabling business innovation. IT's charter is now closely aligned with the business goals and processes in a company and to support this charter, enterprise application architecture is shifting towards what's commonly referred to as a services-oriented architecture (SOA), or an enterprise-services architecture [1, 2]. In this talk, I want to discuss the shift to this new architecture and some ramifications of this, in particular some challenges posed by this shift for our research community to pursue. KeywordsService-oriented architecture, model-driven development, data management, metadata management.

The goal of EII systems is to provide uniform access to multiple data sources without having to first load them into a data warehouse. Since the late 1990's, several EII products have appeared in the marketplace and significant experience has been accumulated from fielding such systems. This collection of articles, by individuals who were involved in this industry in various ways, describes some of these experiences and points to the challenges ahead.

Abstract. While column-oriented in-memory databases have been primarily designed to support fast OLAP queries and business intelligence applications, their analytical performance makes them a promising platform for data mining tasks found in life sciences. One such system is the HANA database, SAP's in-memory data management solution. In this contribution we show how HANA meets some inherent requirements of data mining in life sciences. Furthermore, we conducted a case study in the area of proteomics research. As part of this study we implemented a proteomics analysis pipeline in HANA. We also implemented a flexible data analysis toolbox that can be used by life sciences researchers to easily design and evaluate their analysis models.