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Auflistung nach Schlagwort "big data"

1 - 6 von 6
  • Konferenzbeitrag
    DeLorean: A Storage Layer to Analyze Physical Data at Scale
    (Datenbanksysteme für Business, Technologie und Web (BTW 2017), 2017) Kußmann, Michael; Berens, Maximilian; Eitschberger, Ulrich; Kilic, Ayse; Lindemann, Thomas; Meier, Frank; Niet, Ramon; Schellenberg, Margarete; Stevens, Holger; Wishahi, Julian; Spaan, Bernhard; Teubner, Jens
    Modern research in high energy physics depends on the ability to analyse massive volumes of data in short time. In this article, we report on DeLorean, which is a new system architecture for high-volume data processing in the domain of particle physics. DeLorean combines the simplicity and performance of relational database technology with the massive scalability of modern cloud execution platforms (Apache Drill for that matter). Experiments show a four-fold performance improvement over state-of-the-art solutions.
  • Workshopbeitrag
    Designing an Interactive Visualization for Coordinating Road Construction Sites in Virtual Reality
    (Mensch und Computer 2019 - Workshopband, 2019) Uhr, Manuela; Haselmann, Sina; Steep, Lea; Eikhoff, Joschka; Steinicke, Frank
    Road works highly affect traffic in major cities, therefore coordination is key to avoid congestion on urban streets and highways. Software tools and interactive visualizations giving insight to complex road works data as well as preexisting spatial and temporal dependencies in between sites are important for the coordination process. In existing 2D visualizations, spatio-temporal dependencies are shown in multiple views, resulting in high cognitive load. In this article we describe the design and evaluation of a visualization using Virtual Reality for exploring multi-dimensional data of road works. The relevance for expert use was reviewed in an interview with local traffic engineers. In addition, a user study was conducted to evaluate the general usability of the prototype. The results reflect an overall positive response and acceptance and show directions for further development.
  • Textdokument
    MAGPIE: A Scalable Data Storage System for Efficient High Volume Data Queries
    (BTW 2019, 2019) Lindemann, Thomas; Brinkmann, Patrick; Dalbah, Fadi; Hakert, Christian; Honysz, Philipp-Jan; Matuszczyk, Daniel; Müller, Nikolas; Schmulbach, Alexander; Todorinski, Stefan Petyov; Tüselmann, Oliver; Wonsak, Shimon; Teubner, Jens
    Modern challenges in huge sized data storage and querying require new approaches in the field of data storage systems. With MAGPIE, we are introducing a hardware-software-co-design, which is efficient in querying data by distributed storage with storage-near pre-processing and designed to be scalable up to large dimensions.
  • Konferenzbeitrag
    Requirements for a public digital forensics cloud
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Morgenstern, Martin; Honekamp, Wilfried
    The acquisition of digital evidence in criminal proceedings has become considerably more important in recent years. At the same time, the amount of data has also increased. The need to use cloud or big data solutions for digital forensics to be able to efficiently process the permanently increasing amount of data and number of cases has been recognised for years. By using public cloud providers, such as Amazon AWS and Microsoft Azure, to secure and analyse digital evidence, resources could be used in a scalable and flexible way. Forensic service providers have had to keep a large number of data carriers for forensic backups because they have to be available immediately in case of an emergency and cannot be procured only when needed.
  • Zeitschriftenartikel
    ScaDS Dresden/Leipzig – A competence center for collaborative big data research
    (it - Information Technology: Vol. 60, No. 5-6, 2018) Jäkel, René; Peukert, Eric; Nagel, Wolfgang E.; Rahm, Erhard
    The efficient and intelligent handling of large, often distributed and heterogeneous data sets increasingly determines the scientific and economic competitiveness in most application areas. Mobile applications, social networks, multimedia collections, sensor networks, data intense scientific experiments, and complex simulations nowadays generate a huge data deluge. Nonetheless, processing and analyzing these data sets with innovative methods open up new opportunities for its exploitation and new insights. Nevertheless, the resulting resource requirements exceed usually the possibilities of state-of-the-art methods for the acquisition, integration, analysis and visualization of data and are summarized under the term big data. ScaDS Dresden/Leipzig, as one Germany-wide competence center for collaborative big data research, bundles efforts to realize data-intensive applications for a wide range of applications in science and industry. In this article, we present the basic concept of the competence center and give insights in some of its research topics.
  • Konferenzbeitrag
    Serverless Big Data Processing using Matrix Multiplication as Example
    (INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, 2019) Werner, Sebastian; Kuhlenkamp, Jörn; Klems, Markus; Müller, Johannes; Tai, Stefan