The introduction of a 64 bit address space in commodity operating systems and the constant drop in hardware prices made large capacities of main memory in the order of terabytes technically feasible and economically viable. Especially column-oriented in-memory databases are a promising platform to improve data management for enterprise applications. As in-memory databases hold the primary persistence in volatile memory, some form of recovery mechanism is required to prevent potential data loss in case of failures. Two desirable characteristics of any recovery mechanism are (1) that it has a minimal impact on the running system, and (2) that the system recovers quickly and without any data loss after a failure. This paper introduces an efficient logging mechanism for dictionary-compressed column structures that addresses these two characteristics by (1) reducing the overall log size by writing dictionary-compressed values and (2) allowing for parallel writing and reading of log files. We demonstrate the efficiency of our logging approach by comparing the resulting log-file size with traditional logical logging on a workload produced by a productive enterprise system.

Read-optimized columnar databases use differential updates to handle writes by maintaining a separate write-optimized delta partition which is periodically merged with the read-optimized and compressed main partition. This merge process introduces significant overheads and unacceptable downtimes in update intensive systems, aspiring to combine transactional and analytical workloads into one system. In the first part of the paper, we report data analyses of 12 SAP Business Suite customer systems. In the second half, we present an optimized merge process reducing the merge overhead of current systems by a factor of 30. Our linear-time merge algorithm exploits the underlying high compute and bandwidth resources of modern multi-core CPUs with architecture-aware optimizations and efficient parallelization. This enables compressed in-memory column stores to handle the transactional update rate required by enterprise applications, while keeping properties of read-optimized databases for analytic-style queries.

Virtualization is mainly employed for increasing the utilization of a lightly-loaded system by consolidation, but also to ease the administration based on the possibility to rapidly provision or migrate virtual machines. These facilities are crucial for efficiently managing large data centers. At the same time, modern hardware --- such as Intel's Nehalem microarchitecure --- change critical assumptions about performance bottlenecks and software systems explicitly exploiting the underlying hardware --- such as main memory databases --- gain increasing momentum.

Enterprise applications are presently built on a 20-year old data management infrastructure that was designed to meet a specific set of requirements for OLTP systems. In the meantime, enterprise applications have become more sophisticated, data set sizes have increased, requirements on the freshness of input data have been strengthened, and the time allotted for completing business processes has been reduced. To meet these challenges, enterprise applications have become increasingly complicated to make up for short-comings in the data management infrastructure. To address this issue we investigate recent trends in data management such as main memory databases, column stores and compression techniques with regards to the workload requirements and data characteristics derived from actual customer systems. We show that a main memory column store is better suited for to days enterprise systems, which we validate by using SAP's Net Weaver Business Warehouse Accelerator and a realistic set of data from an inventory management application.

Many relational databases exhibit complex dependencies between data attributes, caused either by the nature of the underlying data or by explicitly denormalized schemas. In data warehouse scenarios, calculated key figures may be materialized or hierarchy levels may be held within a single dimension table. Such column correlations and the resulting data redundancy may result in additional storage requirements. They may also result in bad query performance if inappropriate independence assumptions are made during query compilation. In this paper, we tackle the specific problem of detecting functional dependencies between columns to improve the compression rate for column-based database systems, which both reduces main memory consumption and improves query performance. Although a huge variety of algorithms have been proposed for detecting column dependencies in databases, we maintain that increased data volumes and recent developments in hardware architectures demand novel algorithms with much lower runtime overhead and smaller memory footprint. Our novel approach is based on entropy estimations and exploits a combination of sampling and multiple heuristics to render it applicable for a wide range of use cases. We demonstrate the quality of our approach by means of an implementation within the SAP NetWeaver Business Warehouse Accelerator. Our experiments indicate that our approach scales well with the number of columns and produces reliable dependence structure information. This both reduces memory consumption and improves performance for nontrivial queries.

In this paper, we describe a main memory hybrid database system called HYRISE, which automatically partitions tables into vertical partitions of varying widths depending on how the columns of the table are accessed. For columns accessed as a part of analytical queries (e.g., via sequential scans), narrow partitions perform better, because, when scanning a single column, cache locality is improved if the values of that column are stored contiguously. In contrast, for columns accessed as a part of OLTP-style queries, wider partitions perform better, because such transactions frequently insert, delete, update, or access many of the fields of a row, and co-locating those fields leads to better cache locality. Using a highly accurate model of cache misses, HYRISE is able to predict the performance of different partitionings, and to automatically select the best partitioning using an automated database design algorithm. We show that, on a realistic workload derived from customer applications, HYRISE can achieve a 20% to 400% performance improvement over pure all-column or all-row designs, and that it is both more scalable and produces better designs than previous vertical partitioning approaches for main memory systems.

Mining for association rules and frequent patterns is a central activity in data mining. However, most existing algorithms are only moderately suitable for real-world scenarios. Most strategies use parameters like minimum support, for which it can be very difficult to define a suitable value for unknown datasets. Since most untrained users are unable or unwilling to set such technical parameters, we address the problem of replacing the minimum-support parameter with top- n strategies. In our paper, we start by extending a top- n implementation of the ECLAT algorithm to improve its performance by using heuristic search strategy optimizations. Also, real-world datasets are often distributed and modern database architectures are switching from expensive SMPs to cheaper shared-nothing blade servers. Thus, most mining queries require distribution handling. Since partitioning can be forced by user-defined semantics, it is often forbidden to transform the data. Therefore, we developed an adaptive top- n frequent-pattern mining algorithm that simplifies the mining process on real distributions by relaxing some requirements on the results. We first combine the PARTITION and the TPUT algorithms to handle distributed top- n frequent-pattern mining. Then, we extend this new algorithm for distributions with real-world data characteristics. For frequent-pattern mining algorithms, equal distributions are important conditions, and tiny partitions can cause performance bottlenecks. Hence, we implemented an approach called MAST that defines a minimum absolute-support threshold. MAST prunes patterns with low chances of reaching the global top- n result set and high computing costs. In total, our approach simplifies the process of frequent-pattern mining for real customer scenarios and data sets. This may make frequent-pattern mining accessible for very new user groups. Finally, we present results of our algorithms when run on the SAP NetWeaver BW Acceleratorwith standard and real business datasets.