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P265 - BTW2017 - Datenbanksysteme für Business, Technologie und Web

https://dl.gi.de/handle/20.500.12116/21090
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Auflistung P265 - BTW2017 - Datenbanksysteme für Business, Technologie und Web nach Autor:in "Binnig, Carsten"

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  • Konferenzbeitrag
    IncMap: A Journey towards Ontology-based Data Integration
    (Datenbanksysteme für Business, Technologie und Web (BTW 2017), 2017) Pinkel, Christoph; Binnig, Carsten; Jimenez-Ruiz, Ernesto; Kharlamov, Evgeny; Nikolov, Andriy; Schwarte, Andreas; Heupel, Christian; Kraska, Tim
    Ontology-based data integration (OBDI) allows users to federate over heterogeneous data sources using a semantic rich conceptual data model. An important challenge in ODBI is the curation of mappings between the data sources and the global ontology. In the last years, we have built IncMap, a system to semi-automatically create mappings between relational data sources and a global ontology. IncMap has since been put into practice, both for academic and in industrial applications. Based on the experience of the last years, we have extended the original version of IncMap in several dimensions to enhance the mapping quality: (1) IncMap can detect and leverage semantic-rich patterns in the relational data sources such as inheritance for the mapping creation. (2) IncMap is able to leverage reasoning rules in the ontology to overcome structural differences from the relational data sources. (3) IncMap now includes a fully automatic mode that is often necessary to bootstrap mappings for a new data source. Our experimental evaluation shows that the new version of IncMap outperforms its previous version as well as other state-of-the-art systems.
  • Konferenzbeitrag
    Spotlytics: How to Use Cloud Market Places for Analytics?
    (Datenbanksysteme für Business, Technologie und Web (BTW 2017), 2017) Kraska, Tim; Dadashov, Elkhan; Binnig, Carsten
    In contrast to fixed-priced cloud computing services, Amazon’s Spot market uses a demand-driven pricing model for renting out virtual machine instances. This allows for remarkable savings when used intelligently. However, a peculiarity of Amazon’s Spot market is, that machines can suddenly be taken away from the user if the price on the market increases. This can be considered as a distinct form of a machine failure. In this paper, we first analyze Amazon’s current spot market rules and based on the results develop a general market model. This model is valid for Amazon’s current Spot service but also many potential variations of it, as well as other cloud computing markets. Using the developed market model, we then make recommendations on how to deploy analytical systems with the following three fault-tolerance/recovery strategies: re-execution as used by traditional database systems, checkpointing as, for example, used by Hadoop, and lineage-based recovery as, for example, used by Spark. The main insights are that for traditional database systems using significantly more instances/machines can be cheaper, whereas for systems with checkpoint recovery the opposite is true, while lineage-based recovery is not beneficial for cloud markets at all.