Isotopenangereicherte hochreine Germanium (HPGe) Strahlungsdetektorkristalle mit herausragender chemischen Reinheit und kristalliner Perfektion spielen eine Schlüsselrolle zur Beantwortung fundamentaler Fragen bezüglich Neutrinos und Dunkler Materie. Diese Dissertation untersucht die einhergehende Materialaufbereitung von Germanium angereichert an 76Ge, Kristallzüchtung, sowie die notwendige Charakterisierung und Beschreibung von Defekten. In dieser Arbeit wird ein Wasserstoff-Reduktionsprozess eingesetzt um 50 kg an angereicherten GeO2 zu halbleitenden Ge zu reduzieren, was anschließend mittels Zonenreinigung auf intrinsische Reinheit gebracht wurde. Die Prozesse wurden hinsichtlich einer minimalen Exposition durch kosmische Strahlung und einer maximaler Ausbeute optimiert. Zudem wurde eine Zonenreinigungstechnologie für HPGe in Quarzglasbooten zur Erreichung der angestrebten Netto-Ladungsträgerdichte von 10^10 cm^-3 entwickelt. [0 0 1] HPGe Einkristalle mit niedriger Defektdichte wurden mittels des Czochralski-Verfahrens gezüchtet. Ein dynamischer Beugungseffekt zeigte die Existenz von Leerstellencluster in versetzungsfreien Ge mit einem Durchmesser von 100 nm und einer Dichte von 10^5 cm^-3 auf, was mit einem Abfall der Ladungsträgerlebensdauer von über 400 auf 83 µs verbunden war. Des Weiteren wurde Versetzungslinien und –typ Selektion in [2 1 1] und [1 1 0] Ge Kristallen durch Weißstrahltopografie und einer damit einhergehenden umfangreichen Burgersvektor-Analyse nachgewiesen. Die hohen Lebensdauern weisen auf eine verbesserte zukünftige Detektorkristalltechnologie hin. Letztendlich wurde die aus der Kristallzüchtung resultierende Versetzungsmultiplikation durch Diskrete Versetzungsdynamik unter Verwendung und Modifizierung des Open-Source Codes ParaDiS beschrieben. Exponentielle Versetzungsmultiplikation wurde beobachtet, mit einer linearen Proportionalität der mittleren Versetzungsgeschwindigkeit und des Multiplikationsparameters zur effektiven Schubspannung. / Isotopically enriched high-purity germanium (HPGe) radiation detector crystals with outstanding crystalline perfection play a key role in answering fundamental questions concerning neutrinos and dark matter. This thesis investigates the related material processing of germanium isotopically enriched in 76Ge, crystal growth, and the necessary defect characterization and description. In this work, a hydrogen reduction process was employed to reduce around 50 kg of enriched GeO2 to semiconducting Ge, which was subsequently purified by zone-refining to intrinsic purity. The processes were optimized towards a minimal exposure to cosmic radiation and a maximal yield. Furthermore, HPGe zone-refining technology in fused silica boats has been developed to reach the target net charge carrier density of 10^10 cm^-3. [0 0 1] HPGe single crystals with low defect density were grown by the Czochralski method. A dynamical X-ray diffraction effect revealed the presence of voids in dislocation-free Ge with a diameter of 100 nm and a density of 10^5 cm^-3, which was accompanied by a decrease in charge carrier lifetime from above 400 to 83 µs. Additionally, dislocation line and type selection were demonstrated in [2 1 1] and [1 1 0] Ge crystals by white beam X-ray topography accompanied by an extensive Burgers vector analysis. The high lifetimes suggest an improved future detector crystal technology. Finally, the dislocation multiplication during crystal growth could be described by discrete dislocation dynamics, utilizing and modifying the open-source code ParaDiS. Exponential dislocation multiplication was observed with an average dislocation velocity and multiplication parameter linearly proportional to the effective shear stress.

Compressed bitmap indices are heavily used in scientific and commercial database systems because they largely improve query performance for various workloads. Early research focused on finding tailor-made index compression schemes that are amenable for modern processors. Improving performance further typically comes at the expense of a lower compression rate, which is in many applications not acceptable because of memory limitations. Alternatively, tailor-made hardware allows to achieve a performance that can only hardly be reached with software running on general-purpose CPUs. In this paper, we will show how to create a custom instruction set framework for compressed bitmap processing that is generic enough to implement most of the major compressed bitmap indices. For evaluation, we implemented WAH, PLWAH, and COMPAX operations using our framework and compared the resulting implementation to multiple state-of-the-art processors. We show that the custom-made bitmap processor achieves speedups of up to one order of magnitude by also using two orders of magnitude less energy compared to a modern energy-efficient Intel processor. Finally, we discuss how to embed our processor with database-specific instruction sets into database system environments.

As databases accumulate growing amounts of data at an increasing rate, adaptive indexing becomes more and more important. At the same time, applications and their use get more agile and flexible, resulting in less steady and less predictable workload characteristics. Being inert and coarse-grained, state-of-the-art index tuning techniques become less useful in such environments. Especially the full-column indexing paradigm results in lot of indexed but never queried data and prohibitively high memory and maintenance costs. In our demonstration, we present Self-Managing Indexes, a novel, adaptive, fine-grained, autonomous indexing infrastructure. In its core, our approach builds on a novel access path that automatically collects useful index information, discards useless index information, and competes with its kind for resources to host its index information. Compared to existing technologies for adaptive indexing, we are able to dynamically grow and shrink our indexes, instead of incrementally enhancing the index granularity. In the demonstration, we visualize performance and system measures for different scenarios and allow the user to interactively change several system parameters.

With rapidly increasing datasets and more dynamic workloads, adaptive partial indexing becomes an important way to keep indexing efficiently. During times of changing workloads, the query performance suffers from inefficient tables scans while the index tuning mechanism adapts the partial index. In this paper we present the Adaptive Index Buffer. The Adaptive Index Buffer reduces the cost of table scans by quickly indexing tuples in memory until the partial index has adapted to the workload again. We explain the basic operating mode of an Index Buffer and discuss how it adapts to changing workload situations. Further, we present three experiments that show the Index Buffer at work.