Peter Benjamin Volk

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Until recently, the use of graphics processing units (GPUs) for query processing was limited by the amount of memory on the graphics card, a few gigabytes at best. Moreover, input tables had to be copied to GPU memory before they could be processed, and after computation was completed, query results had to be copied back to CPU memory. The newest generation(More)
Efficient data structures for in-memory indexing gain in importance due to (1) the exponentially increasing amount of data, (2) the growing main-memory capacity , and (3) the gap between main-memory and CPU speed. In consequence, there are high performance demands for in-memory data structures. Such index structures are used—with minor changes—as primary or(More)
The topic of managing uncertain data has been explored in many ways. Different methodologies for data storage and query processing have been proposed. As the availability of management systems grows, the research on analytics of uncertain data is gaining in importance. Similar to the challenges faced in the field of data management, algorithms for uncertain(More)
With an increasing amount of data and user demands for fast query processing, the optimization of database operations continues to be a challenging task. A common optimization method is to leverage parallel hardware archi-tectures. With the introduction of general-purpose GPU computing, massively parallel hardware has become available within commodity(More)
One of the most important and challenging questions in the area of clustering is how to choose the best-fitting algorithm and param-eterization to obtain an optiml clustering for the considered data. The clustering aggregation concept tries to bypass this problem by generating a set of separate, heterogeneous partitionings of the same data set, from which(More)
Manufacturing process development is under constant pressure to achieve a good yield for stable processes. The development of new technologies, especially in the field of photomask and semiconductor development, is at its physical limits. In this area, data, e.g. sensor data, has to be collected and analyzed for each process in order to ensure process(More)
Regression models are often required for controlling production processes by predicting parameter values. However, the implicit assumption of standard regression techniques that the data set used for parameter estimation comes from a stationary joint distribution may not hold in this context because manufacturing processes are subject to physical changes(More)
In this paper, we present a parallel method to evaluate functions on pairs of elements. It is a challenge to partition the Cartesian product of a set with itself in order to parallelize the function evaluation on all pairs. Our solution uses (a) replication of set elements to allow for partitioning and (b) aggregation of the results gathered for different(More)
—Quality control using scalar quality measures is standard practice in manufacturing. However, there are also quality measures that are determined at a large number of positions on a product, since the spatial distribution is important. We denote such a mapping of local coordinates on the product to values of a measure as a measurement map. In this paper,(More)
With increasing complexity of manufacturing processes, the volume of data that has to be evaluated rises accordingly. The complexity and data volume make any kind of manual data analysis infeasable. At this point, data mining techniques become interesting. The application of current techniques is of complex nature because most of the data is captured by(More)
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