@article{CucchiHesseKawaetal.2019,
  author    = {Cucchi, Karma and Hesse, Falk and Kawa, Nura and Wang, Changhong and Rubin, Yoram},
  title     = {Ex-situ priors: A Bayesian hierarchical framework for defining informative prior distributions in hydrogeology},
  series = {Advances in water resources},
  volume    = {126},
  journal   = {Advances in water resources},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0309-1708},
  doi       = {10.1016/j.advwatres.2019.02.003},
  pages     = {65 -- 78},
  year      = {2019},
  abstract  = {Stochastic modeling is a common practice for modeling uncertainty in hydrogeology. In stochastic modeling, aquifer properties are characterized by their probability density functions (PDFs). The Bayesian approach for inverse modeling is often used to assimilate information from field measurements collected at a site into properties' posterior PDFs. This necessitates the definition of a prior PDF, characterizing the knowledge of hydrological properties before undertaking any investigation at the site, and usually coming from previous studies at similar sites. In this paper, we introduce a Bayesian hierarchical algorithm capable of assimilating various information-like point measurements, bounds and moments-into a single, informative PDF that we call ex-situ prior. This informative PDF summarizes the ex-situ information available about a hydrogeological parameter at a site of interest, which can then be used as a prior PDF in future studies at the site. We demonstrate the behavior of the algorithm on several synthetic case studies, compare it to other methods described in the literature, and illustrate the approach by applying it to a public open-access hydrogeological dataset.},
  language  = {en}
}
@article{GrillenbergerRomeike2015,
  author    = {Grillenberger, Andreas and Romeike, Ralf},
  title     = {Teaching Data Management},
  series = {KEYCIT 2014 - Key Competencies in Informatics and ICT},
  journal   = {KEYCIT 2014 - Key Competencies in Informatics and ICT},
  number    = {7},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  issn      = {1868-0844},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-82648},
  pages     = {133 -- 150},
  year      = {2015},
  abstract  = {Data management is a central topic in computer science as well as in computer science education. Within the last years, this topic is changing tremendously, as its impact on daily life becomes increasingly visible. Nowadays, everyone not only needs to manage data of various kinds, but also continuously generates large amounts of data. In addition, Big Data and data analysis are intensively discussed in public dialogue because of their influences on society. For the understanding of such discussions and for being able to participate in them, fundamental knowledge on data management is necessary. Especially, being aware of the threats accompanying the ability to analyze large amounts of data in nearly real-time becomes increasingly important. This raises the question, which key competencies are necessary for daily dealings with data and data management. In this paper, we will first point out the importance of data management and of Big Data in daily life. On this basis, we will analyze which are the key competencies everyone needs concerning data management to be able to handle data in a proper way in daily life. Afterwards, we will discuss the impact of these changes in data management on computer science education and in particular database education.},
  language  = {en}
}