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P214 - BTW2013 - Datenbanksysteme für Business, Technologie und Web

https://dl.gi.de/handle/20.500.12116/21086
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Auflistung P214 - BTW2013 - Datenbanksysteme für Business, Technologie und Web nach Erscheinungsdatum

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  • Konferenzbeitrag
    Logical recovery from single-page failures
    (Datenbanksysteme für Business, Technologie und Web (BTW) 2021, 2013) Graefe, Goetz; Seeger, Bernhard
    Modern hardware technologies and ever-increasing data sizes increase probability and frequency of local storage failures, e.g., unrecoverable read errors on individual disk sectors or pages on flash storage. Our prior work has formalized singlepage failures and outlined efficient methods for their detection and recovery. These prior techniques rely on old backup copies of individual pages, e.g., as part of a database backup or as old versions retained after a page migration. Those might not be available, however, e.g., after recent index creation in “non-logged” or “allocation-only logging” mode, which industrial database products commonly use. The present paper introduces techniques for single-page recovery without backup copies, e.g., pages of new indexes created in allocation-only logging mode. By rederiving lost contents of individual pages, these techniques enable efficient recovery of data lost due to damaged storage structures or storage devices. Recovery performance depends on the size of the failure and of the required data sources; it is independent of the sizes of device, index structure, etc.
  • Konferenzbeitrag
    Seamless integration of archiving functionality in OLTP/OLAP database systems using accelerator technologies
    (Datenbanksysteme für Business, Technologie und Web (BTW) 2036, 2013) Stolze, Knut; Köth, Oliver; Beier, Felix; Caballero, Carlos; Li, Ruiping
    The recent version of the IBM DB2 Analytics Accelerator introduces the High Performance Storage Saver as a new product feature. It paves another part of the way towards integrating OLTP and OLAP into a single database system. We present the technical details of this approach, which integrates archiving functionality into the DB2 relational database systems with seamless and transparent access to the archive data. The IBM DB2 Analytics Accelerator for DB2 for z/OS offers the storage area for the archive and also delivers exceptional performance for querying the data online, archived and non-archived data alike. In this paper, we describe the administrative interfaces controlling which table partitions shall be archived (or restored) and the associated monitoring interfaces. En- hancements in the DB2 optimizer provide control whether archive data shall be considered for query processing or not. Strong focus was laid on using simple interfaces, and we present our approach taken during product design and development.
  • Konferenzbeitrag
    ScyPer: a hybrid OLTP&OLAP distributed main memory database system for scalable real-time analytics
    (Datenbanksysteme für Business, Technologie und Web (BTW) 2044, 2013) Mühlbauer, Tobias; Rödiger, Wolf; Reiser, Angelika; Kemper, Alfons; Neumann, Thomas
    ScyPer is an abbreviation for Scaled-out HyPer, a version of the HyPer main memory hybrid OLTP&OLAP database system that horizontally scales out on sharednothing commodity hardware. Our demo shows that ScyPer a) achieves a near-linear scale-out of OLAP query throughput with the number of active nodes, b) sustains a constant OLTP throughput, c) is resilient to node failures, and d) offers real-time analytical capabilities through market-leading query response times and periodically forked TX-consistent virtual memory snapshots with sub-second lifetime durations.
  • Konferenzbeitrag
    Lernen häufiger Muster aus intervallbasierten Datenströmen - Semantik und Optimierungen
    (Datenbanksysteme für Business, Technologie und Web (BTW) 2031, 2013) Geesen, Dennis; Appelrath, H. -Jürgen; Grawunder, Marco; Nicklas, Daniela
    Das Erkennen und Lernen von Mustern über Ereignisdatenströmen ist eine wesentliche Voraussetzung für effektive kontextbewusste Anwendungen, wie sie bspw. in intelligenten Wohnungen (Smart Homes) vorkommen. Zur Erkennung dieser Muster werden i.d.R. Verfahren aus dem Bereich des Frequent Pattern Mining (FPM) eingesetzt. Das Erlernen relevanter Muster findet aktuell entweder auf aufgezeichneten Ereignisströmen statt oder wird online mit Hilfe spezieller, an die Be- sonderheiten der Stromverarbeitung angepasste FPM-Algorithmen durchgeführt. Auf diese Weise muss entweder auf die Onlineverarbeitung verzichtet oder existierende und bewährte effiziente FPM-Algorithmen können nicht eingesetzt werden. In diesem Beitrag stellen wir einen Ansatz vor, der es ermöglicht, beliebige Datenbank-basierte FPM-Algorithmen ohne Anpassung auch auf Datenströmen durchzuführen. Da unsere Semantik auf der bekannten relationalen Algebra basiert, können weitere Optimierungen bspw. durch Anfrageumschreibungen erfolgen. Wir evaluieren den Ansatz im Datenstrom-Framework Odysseus und zeigen, dass bspw. beim Einsatz des FPM- Algorithmus „FP-Growth“ das Lernen in konstanter Zeit erfolgen kann und somit ein kontinuierliches Lernen auf dem Datenstrom möglich ist.
  • Konferenzbeitrag
    A mutual pruning approach for RkNN join processing
    (Datenbanksysteme für Business, Technologie und Web (BTW) 2016, 2013) Emrich, Tobias; Kröger, Peer; Niedermayer, Johannes; Renz, Matthias; Züfle, Andreas
    A reverse k-nearest neighbour (RkNN) query determines the objects from a database that have the query as one of their k-nearest neighbors. Processing such a query has received plenty of attention in research. However, the effect of running multiple RkNN queries at once (join) or within a short time interval (bulk/group query) has, to the best of our knowledge, not been addressed so far. In this paper, we analyze RkNN joins and discuss possible solutions for solving this problem. During our performance analysis we provide evaluation results showing the IO and CPU performance of the compared algorithms for a variety of different setups.
  • Konferenzbeitrag
    Privacy-aware multidimensional indexing
    (Datenbanksysteme für Business, Technologie und Web (BTW) 2022, 2013) Grebhahn, Alexander; Schäler, Martin; Köppen, Veit; Saake, Gunter
    Deleting data from a database system in a forensic secure environment and in a high performant way is a complex challenge. Due to redundant copies and additional information stored about data items, it is not appropriate to delete only data items themselves. Additional challenges arise when using multidimensional index structures. This is because information of data items are used to index the space. As initial result, we present different deletion levels, to overcome this challenge. Based on this classification, we analyze how data can be reconstructed from the index and modify index structures to improve privacy of data items. Second, we benchmark our index structure modifications and quantify our modifications. Our results indicate that forensic secure deletion is possible with modification of multidimensional index structures having only a small impact on computational performance, in some cases.
  • Konferenzbeitrag
    Pack indexing for time-constrained in-memory query processing
    (Datenbanksysteme für Business, Technologie und Web (BTW) 2023, 2013) Jaekel, Tobias; Voigt, Hannes; Kissinger, Thomas; Lehner, Wolfgang
    Main memory databases management systems are used more often and in a wide spread of application scenarios. To take significant advantage of the main memory read performance, most techniques known from traditional disk-centric database systems have to be adapted and re-designed. In the field of indexing, many mainmemory-optimized index structures have been proposed. Most of these works aim at primary indexing. Secondary indexes are rarely considered in the context of main memory databases. Either query performance is sufficiently good without secondary indexing or main memory is a resource too scarce to invest in huge secondary indexes. A more subtle trade between benefit and costs of secondary indexing has not been considered so far. In this paper we present Pack Indexing, a secondary indexing technique for main memory databases that allows a precise trade-off between the benefit in query execution time gained with a secondary index and main memory invested for that index. Compared to traditional indexing, Pack Indexing achieves this by varying the granularity of indexing. We discuss the Pack Indexing concept in detail and describe how the concept can be implemented. To demonstrate the usefulness and the effectiveness of our approach, we present several experiments with different datasets.
  • Editiertes Buch
  • Konferenzbeitrag
    DrillBeyond: open-world SQL queries using web tables
    (Datenbanksysteme für Business, Technologie und Web (BTW) 2050, 2013) Eberius, Julian; Thiele, Maik; Braunschweig, Katrin; Lehner, Wolfgang
    The Web consists of a huge number of documents, but also large amounts structured information, for example in the form of HTML tables containing relationalstyle data. One typical usage scenario for this kind of data is their integration into a database or data warehouse in order to apply data analytics. However, in today's business intelligence tools there is an evident lack of support for so-called situational or ad-hoc data integration. In this demonstration we will therefore present DrillBeyond, a novel database and information retrieval engine which allows users to query a local database as well as the web datasets in a seamless and integrated way with standard SQL. The audience will be able to pose queries to our DrillBeyond system which will be answered partly from local data in the database and partly from datasets that originate from the Web of Data. We will demonstrate the integration of the web tables back into the DBMS in order to apply its analytical features.
  • Konferenzbeitrag
    Datenmanagementpatterns in Simulationsworkflows
    (Datenbanksysteme für Business, Technologie und Web (BTW) 2030, 2013) Reimann, Peter; Schwarz, Holger
    Simulationsworkflows müssen oftmals große Datenmengen verarbeiten, die in einer Vielzahl proprietärer Formate vorliegen. Damit diese Daten von den im Workflow eingebundenen Programmen und Diensten verarbeitet werden können, müssen sie in passende Formate transformiert werden. Dies erhöht die Komplexität der Workflowmodellierung, welche i.d.R. durch die Wissenschaftler selbst erfolgt. Dadurch können sich diese weniger auf den Kern der eigentlichen Simulation konzentrieren. Zur Behebung dieses Defizits schlagen wir einen Ansatz vor, mit dem die Aktivitäten zur Datenbereitstellung in Simulationsabläufen abstrakt modelliert werden können. Wissenschaftler sollen keine Implementierungsdetails, sondern lediglich die Kernaspekte der Datenbereitstellung in Form von Patterns beschreiben. Die Spezifikation der Patterns soll dabei möglichst in der Sprache der mathematischen Simulationsmodelle erfolgen, mit denen Wissenschaftler vertraut sind. Eine Erweiterung des Workflowsystems bildet die Patterns automatisch auf ausführbare Workflowfragmente ab, welche die Datenbereitstellung umsetzen. Dies alles reduziert die Komplexität der Modellierung von Simulationsworkflows und erhöht die Produktivität der Wissenschaftler.