Persistent Buffer Management with Optimistic Consistency

Finding the best way to leverage non-volatile memory (NVM) on modern database systems is still an open problem. The answer is far from trivial since the clear boundary between memory and storage present in most systems seems to be incompatible with the intrinsic memory-storage duality of NVM. Rather than treating NVM either solely as memory or solely as storage, in this work we propose how NVM can be simultaneously used as both in the context of modern database systems. We design a persistent buffer pool on NVM, enabling pages to be directly read/written by the CPU (like memory) while recovering corrupted pages after a failure (like storage). The main benefits of our approach are an easy integration in the existing database architectures, reduced costs (by replacing DRAM with NVM), and faster peak-performance recovery

Robust Data Management

Mobility gains more and more importance from a technological as well as social perspective. On the one hand, mobility is required from the personal and professional environment in order to keep pace with the developments in a global world. On the other hand, the existence of wireless networks and the success of cell-phones enable a wide usage of mobile communication infrastructure. While mobile devices (especially cell-phone) are becoming more and more a general vehicle to perform a wide spectrum of applications like internet browsing, etc. many, many issues are still unsolved in order to provide a technologically solid and well accepted mobile infrastructure. In the following, we focus on the term of robustness as a mean to achieve this goal. No only the general possibility to communicate via mobile devices using wireless networks is the question, but the reliable, secure, and finally simple way of doing it must be the core research in the context of mobile environments

Modeling Large Scale OLAP Scenarios

In the recent past, different multidimensional data models were introduced to model OLAP (‘Online Analytical Processing’) scenarios. Design problems arise, when the modeled OLAP scenarios become very large and the dimensionality increases, which greatly decreases the support for an efficient ad-hoc data analysis process. Therefore, we extend the classical multidimensional model by grouping functionally dependent attributes within single dimensions, yielding in real orthogonal dimensions, which are easy to create and to maintain on schema design level. During the multidimensional data analysis phase, this technique yields in nested data cubes reflecting an intuitive two-step navigation process: classification-oriented ‘drill-down’/ ‘roll-up’ and description-oriented‘split’/ ‘merge’ operators on data cubes. Thus, the proposed Nested Multidimensional Data Model provides great modeling flexibility during the schema design phase and application-oriented restrictiveness during the data analysis phase

The Data Center under your Desk: How Disruptive is Modern Hardware for DB System Design?

While we are already used to see more than 1,000 cores within a single machine, the next processing platforms for database engines will be heterogeneous with built-in GPU-style processors as well as specialized FPGAs or chips with domain-specific instruction sets. Moreover, the traditional volatile as well as the upcoming non-volatile RAM with capacities in the 100s of TBytes per machine will provide great opportunities for storage engines but also call for radical changes on the architecture of such systems. Finally, the emergence of economically affordable, high-speed/low-latency interconnects as a basis for rack-scale computing is questioning long-standing folklore algorithmic assumptions but will certainly play an important role in the big picture of building modern data management platforms. In this talk, we will try to classify and review existing approaches from a performance, robustness, as well as energy efficiency perspective and pinpoint interesting starting points for further research activities

POIESIS: A tool for quality-aware ETL process redesign

We present a tool, called POIESIS, for automatic ETL process enhancement. ETL processes are essential data-centric activities in modern business intelligence environments and they need to be examined through a viewpoint that concerns their quality characteristics (e.g., data quality, performance, manageability) in the era of Big Data. POIESIS responds to this need by providing a user-centered environment for quality-aware analysis and redesign of ETL flows. It generates thousands of alternative flows by adding flow patterns to the initial flow, in varying positions and combinations, thus creating alternative design options in a multidimensional space of different quality attributes. Through the demonstration of POIESIS we introduce the tool's capabilities and highlight its efficiency, usability and modifiability, thanks to its polymorphic design. © 2015, Copyright is with the authors.Peer ReviewedPostprint (published version

A machine learning approach for layout inference in spreadsheets

Spreadsheet applications are one of the most used tools for content generation and presentation in industry and the Web. In spite of this success, there does not exist a comprehensive approach to automatically extract and reuse the richness of data maintained in this format. The biggest obstacle is the lack of awareness about the structure of the data in spreadsheets, which otherwise could provide the means to automatically understand and extract knowledge from these files. In this paper, we propose a classification approach to discover the layout of tables in spreadsheets. Therefore, we focus on the cell level, considering a wide range of features not covered before by related work. We evaluated the performance of our classifiers on a large dataset covering three different corpora from various domains. Finally, our work includes a novel technique for detecting and repairing incorrectly classified cells in a post-processing step. The experimental results show that our approach deliver s very high accuracy bringing us a crucial step closer towards automatic table extraction.Peer ReviewedPostprint (published version

An Alternative Relational OLAP Modeling Approach

Schema design is one of the fundamentals in database theory and practice as well. In this paper, we discuss the problem of locally valid dimensional attributes in a classification hierarchy of a typical OLAP scenario. In a first step, we show that the traditional star and snowflake schema approach is not feasible in this very natural case of a hierarchy. Therefore, we sketch two alternative modeling approaches resulting in practical solutions and a seamless extension of the traditional star and snowflake schema approach: In a pure relational approach, we replace each dimension table of a star / snowflake schema by a set of views directly reflecting the classification hierarchy. The second approach takes advantage of the object-relational extensions. Using object-relational techniques in the context for the relational representation of a multidimensional OLAP scenario is a novel approach and promises a clean and smooth schema design

Pathways to servers of the future

The Special Session on “Pathways to Servers of the Future” outlines a new research program set up at Technische Universität Dresden addressing the increasing energy demand of global internet usage and the resulting ecological impact of it. The program pursues a novel holistic approach that considers hardware as well as software adaptivity to significantly increase energy efficiency, while suitably addressing application demands. The session presents the research challenges and industry perspective