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Auflistung nach Autor:in "Krestel, Ralf"

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  • Zeitschriftenartikel
    Das Fachgebiet „Informationssysteme“ am Hasso-Plattner-Institut
    (Datenbank-Spektrum: Vol. 17, No. 1, 2017) Naumann, Felix; Krestel, Ralf
    Das Hasso-Plattner-Institut (HPI) ist ein privat finanziertes Institut an der Universität Potsdam. Stifter ist Professor Hasso Plattner, Mitgründer und Aufsichtsratsvorsitzender des Software-Konzerns SAP. Das Fachgebiet Informationssysteme, das von Prof. Dr. Felix Naumann geleitet wird, beschäftigt sich mit dem effizienten und effektiven Umgang mit heterogenen Daten und Texten. Gegründet wurde das Fachgebiet 2006 und bietet derzeit 12 Doktoranden und circa 15 Masterstudenten eine Forschungsumgebung.
  • Zeitschriftenartikel
    Data-driven analysis and prediction of norm acceptance
    (Informatik Spektrum: Vol. 45, No. 4, 2022) Krestel, Ralf; Kuhn, Annegret; Hasselbring, Wilhelm
    That norms matter for politics is a widely shared observation. Existing political science research on norm diffusion, norm localization, and contestations is, however, constrained due to methodological manageability of empirical data. To face this research challenge, we propose an interdisciplinary research collaboration between political and computer science. Using the show case of energy politics, we want to conduct unsupervised and semi-supervised content analysis and fusion with the help of automated text mining methods to analyze the influence of different types of so-called norm entrepreneurs on the public acceptance and, respectively, contestations of different energy policies.
  • Zeitschriftenartikel
    Measuring and Facilitating Data Repeatability in Web Science
    (Datenbank-Spektrum: Vol. 19, No. 2, 2019) Risch, Julian; Krestel, Ralf
    Accessible and reusable datasets are a necessity to accomplish repeatable research. This requirement poses a problem particularly for web science, since scraped data comes in various formats and can change due to the dynamic character of the web. Further, usage of web data is typically restricted by copyright-protection or privacy regulations, which hinder publication of datasets. To alleviate these problems and reach what we define as “partial data repeatability”, we present a process that consists of multiple components. Researchers need to distribute only a scraper and not the data itself to comply with legal limitations. If a dataset is re-scraped for repeatability after some time, the integrity of different versions can be checked based on fingerprints. Moreover, fingerprints are sufficient to identify what parts of the data have changed and how much. We evaluate an implementation of this process with a dataset of 250 million online comments collected from five different news discussion platforms. We re-scraped the dataset after pausing for one year and show that less than ten percent of the data has actually changed. These experiments demonstrate that providing a scraper and fingerprints enables recreating a dataset and supports the repeatability of web science experiments.