Location-based event data is captured in RFID-aided supply chains for tacking individual goods. They are stored in distributed event repositories by involved supply chain parties. Performing anti-counterfeiting checks involves exchange of event data without exposure of sensitive business secrets. Current EPC global standards leave the definition of security strategies open for concrete implementation. We consider data security as the major aspect that needs to be clarified before wide adaption of EPC global standards will be considered by industries. The given work contributes by defining security extensions for EPC global networks and sharing implementation details of our research prototype. We show that incorporating in-memory technology enables history-based access control while keeping response times fast.

In Software-as-a-Service, multiple tenants are typically consolidated into the same database instance to reduce costs. For analytics-as-a-service, in-memory column databases are especially suitable because they offer very short response times. This paper studies the automation of operational tasks in multi-tenant in-memory column database clusters. As a prerequisite, we develop a model for predicting whether the assignment of a particular tenant to a server in the cluster will lead to violations of response time goals. This model is then extended to capture drops in capacity incurred by migrating tenants between servers. We present an algorithm for moving tenants around the cluster to ensure that response time goals are met. In so doing, the number of servers in the cluster may be dynamically increased or decreased. The model is also extended to manage multiple copies of a tenant's data for scalability and availability. We validated the model with an implementation of a multi-tenant clustering framework for SAP's in-memory column database TREX.

Key-value stores which keep the data entirely in main memory can serve applications whose performance criteria cannot be met by disk-based key-value stores. This paper evaluates the performance implications of cache-conscious data placement in an in-memory key-value store by examining how many values have to be stored consecutively in blocks in order to fully exploit memory locality during bandwidth-bound operations. We contribute by introducing a random block traversal main memory access pattern, by describing the corresponding memory access costs as well as by formally and experimentally deriving the correlation between block size and throughput. Our calculations and experiments vary the value and block sizes as well as their placement in the memory and derive their impact on cache-misses throughout the different memory hierarchies, the ability to prefetch data, and the number of needed CPU cycles to perform a certain set of data operations. The paper closes with the insight that a block-wise grouping of relatively few key-value pairs increases the throughput up to a factor six and with a discussion which implications a block-wise grouping of data has on the system design key-value store.

While analyzing a log file of a text-based source code search engine we discovered that developers search for fine-grained syntactical patterns in 36% of queries. Currently, to cope with queries of this kind developers need to use regular expressions, to add redundant terms to the query or to combine searching with other tools provided by the development environment. To improve the expressiveness of the queries, these can be formulated as tree patterns of abstract syntax trees. These search patterns can be expressed by using query languages, such as XPath. However, developers usually do not work with either XPath or with AST. To shield developers from the complexity of query formulation we propose using sample code snippets as queries. The novelty of our approach is the combination of a query language that is very close to the surface programming language and a special database technology to store a large amount of abstract syntax trees. The advantage of this approach over existing source code query languages and search engines is the performance of both query formulation and query execution. This paper describes the technical details of the method and illustrates the value of this approach with performance measures and an industrial controlled experiment. All developers were able to complete the tasks of the experiment faster and more accurately by using our tool (ACS) than by using a text-based search engine. The number of false positives in the result lists was significantly decreased.

Current database benchmarks are either focusing on online transaction processing (OLTP) or on online analytical pro- cessing (OLAP) systems. This traditional separation has to be reevaluated to reflect current trends in the design of database systems. We see a need for a realistic benchmark workload taking both aspects into account. Therefore, we defined a mixed workload and illustrate ways to apply our workload to evaluate the influence of database design on system performance.

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.

The emerging trend towards smart grids defines new requirements for designing enterprise applications for the energy market. Current solutions were built to process single billing runs as time-consuming batch jobs. Rather than processing some readings per year and household, a constant stream of meter readings has to be processed in context of a smart power grid. Additionally, consumers demand for convenient ways to monitor power consumption while getting real-time charging information on a daily basis. In this paper, we share our experiences of integrating meter readings in an industry-specific enterprise application for utilities to support real-time billing. Our prototype enables customers to track consumptions and to view billing details online. Besides, we discuss new business scenarios enabled by this real-timeliness.

RFID implementations leverage competitive business advantages in processing, tracking and tracing of fast-moving goods. Most of them suffer from security threats and the resulting privacy risks as RFID technology was not designed for exchange of sensible data. Emerging global RFID-aided supply chains require open interfaces for data exchange of confidential business data between business partners. We present a mutual authentication model based on one-time passwords preventing tag access by unauthorized third parties. Compared to models using complex on-tag encryption methods our implementation focuses on reducing tag-manufacturing costs while increasing customers' acceptance for RFID technology.

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.

Along with benefits of ERP system companies experience high costs associated to them. Setting up and maintaining the infrastructure and adjusting standard ERP software to a specific customers requirements drive the costs of such systems. To lower the total cost of ownership companies employ software as a service (SaaS) paradigm: instead of owning software they subscribe to it. However, this does not reduce customization costs. To address specific requirements companies develop custom applications on top of their ERP systems. The challenge with this extension approach is that most ERP systems have poorly designed APIs and developing a composite application on top of such system becomes a difficult exercise. In this paper we propose an architecture of an ERP system that supports simple and fast development of composite applications on top of it. We define five requirements towards such an architecture and show how the requirements are satisfied by the proposed architecture. Our proposal emphasizes the importance of ERP data accessibility and contributes with a concept of business object query language that allows building fine-grained queries. All suggestions made in the paper have been prototyped.

We present a flexible approach to the observation of multi-modal communication streams over the course of project-based collaboration such as engineering design. We introduce team communication networks to formalize the occurrence and evolution of actors, resources, and semantic relationships in distributed design information spaces. Analyzing the digital traces of IT-mediated collaboration, we computationally construct team communication networks in an unobtrusive and progressive manner, thus providing a live view into the digital communication and information sharing activities of design teams. A service-based team communication network platform has been implemented and applied in distributed engineering design projects to enable the temporal analysis of information sharing activities and to support in the identification of characteristic communication patterns in distributed collaboration groups.

The quality of software systems heavily depends on their structure, which affects maintainability and readability. However, the ability of humans to cope with the complexity of large software systems is limited. To support reengineering large software systems, software clustering techniques that maximize module cohesion and minimize inter-modular coupling have been developed. The main drawback of these approaches is that they might pull apart elements that were thoughtfully placed together. This paper describes how strongly connected component analysis, dominance analysis, and intra-modular similarity clustering can be applied to identify and to preserve cohesive structures in order to improve the result of reengineering. The use of the proposed method allows a significant reduction of the number of component movements. As a result, the probability of false component movements is reduced. The proposed approach is illustrated by statistics and examples from 18 open source Java projects.

Extending the functionality of Web services without invalidating the code of existing clients has been a challenging task. This paper proposes a new interface design technique called "Generic Web Services" that preserves a service's backwards compatibility while enriching its functionality. This is accomplished by applying interface relaxation technique - shifting the semantics of a service's API from design time to runtime by using identity parameters. The paper gives an example illustrating the idea on a Web service from SAP Enterprise Services Workplace.

The availability of huge system memory, even on standard servers, generated a lot of interest in main memory database engines. In data warehouse systems, highly compressed column-oriented data structures are quite prominent. In order to scale with the data volume and the system load, many of these systems are highly distributed with a shared-nothing approach. The fundamental principle of all systems is a full table scan over one or multiple compressed columns. Recent research proposed different techniques to speedup table scans like intelligent compression or using an additional hardware such as graphic cards or FPGAs. In this paper, we show that utilizing the embedded Vector Processing Units (VPUs) found in standard superscalar processors can speed up the performance of mainmemory full table scan by factors. This is achieved without changing the hardware architecture and thereby without additional power consumption. Moreover, as on-chip VPUs directly access the system's RAM, no additional costly copy operations are needed for using the new SIMD-scan approach in standard main memory database engines. Therefore, we propose this scan approach to be used as the standard scan operator for compressed column-oriented main memory storage. We then discuss how well our solution scales with the number of processor cores; consequently, to what degree it can be applied in multi-threaded environments. To verify the feasibility of our approach, we implemented the proposed techniques on a modern Intel multi-core processor using Intel® Streaming SIMD Extensions (Intel® SSE). In addition, we integrated the new SIMD-scan approach into SAP® Netweaver® Business Warehouse Accelerator. We conclude with describing the performance benefits of using our approach for processing and scanning compressed data using VPUs in column-oriented main memory database systems.