Contrary to recent trends in database systems research focussing on scaling out workloads on a cluster of commodity computers, this demo will break grounds for scale-up. We show that an elastic multi-tenancy solution can be achieved by combining a many-core server with a low footprint main memory database system. Total transactional throughput for TPC-C like order-entry transactions reaches up to 2 million transactions per second on a 32 core server while the number of tenants sharing a single server can be varied from a few to hundreds of separate tenants without diminishing total throughput. Contrary to common belief, a scale-up solution provides high flexibility for tenants with growing throughput needs and allows for simple sharing of common resources between different tenants while minimizing hardware and computing overhead. We show that our approach can handle changes in tenant requirements with minimal impact on other tenants on the server. Additionally, we prove that our architecture provides sufficient per-tenant throughput to handle big tenants and scales well with database size.

The quest for real-time business intelligence requires executing mixed transaction and query processing workloads on the same current database state. However, as Harizopoulos et al. [6] showed for transactional processing, co-execution using classical concurrency control techniques will not yield the necessary performance -- even in re-emerging main memory database systems. Therefore, we designed an in-memory database system that separates transaction processing from OLAP query processing via periodically refreshed snapshots. Thus, OLAP queries can be executed without any synchronization and OLTP transaction processing follows the lock-free, mostly serial processing paradigm of H-Store [8]. In this paper, we analyze different snapshot mechanisms: Hardware-supported Page Shadowing, which lazily copies memory pages when changed by transactions, software controlled Tuple Shadowing, which generates a new version when a tuple is modified, software controlled Twin Tuple, which constantly maintains two versions of each tuple and HotCold Shadowing, which effectively combines Tuple Shadowing and hardware-supported Page Shadowing by clustering update-intensive objects. We evaluate their performance based on the mixed workload CH-BenCHmark which combines the TPC-C and the TPC-H benchmarks on the same database schema and state.

Decentral recommender systems appear well suited for mobile scenarios, but have not been investigated thoroughly or implemented very much so far. We have designed and implemented a system to recommend images on personal digital assistants (PDAs). Our approach also incorporates recommending for groups of users that are present at a public shared display. The system exchanges rating vectors among PDAs, computes local matrices of item similarity and utilizes them to generate recommendations. Our innovation in comparison to existing systems includes improving the extensibility of the data model by introducing versioned rating vectors. In addition, we have optimized the storage requirements on the mobile device. We have evaluated the approach in a small user study. Furthermore, the scalability of our system was analyzed using a standard recommender data set resulting in positive findings.