@inproceedings{weiner2017bremen,
  title        = {Bremen Big Data Challenge 2017: Predicting University Cafeteria Load},
  author       = {Weiner, Jochen and Diener, Lorenz and Stelter, Simon and Externest, Eike and
    K{\"u}hl, Sebastian and Herff, Christian and Putze, Felix and Schulze, Timo and Salous, Mazen
    and Liu, Hui and K{\"u}ster, Dennis and Schultz, Tanja},
  year         = 2017,
  month        = sep,
  booktitle    = {{KI} 2017: Advances in Artificial Intelligence - 40th Annual German Conference on
    AI},
  publisher    = {Springer International Publishing},
  address      = {Cham},
  pages        = {380--386},
  doi          = {10.1007/978-3-319-67190-1_35},
  isbn         = {978-3-319-67190-1},
  editor       = {Kern-Isberner, Gabriele and F{\"u}rnkranz, Johannes and Thimm, Matthias},
  abstract     = {Big data is a hot topic in research and industry. The availability of data has
    never been as high as it is now. Making good use of the data is a challenging research topic in
    all aspects of industry and society. The Bremen Big Data Challenge invites students to dig deep
    into big data. In this yearly event students are challenged to use the month of March to analyze
    a big dataset and use the knowledge they gained to answer a question. In this year's Bremen Big
    Data Challenge students were challenged to predict the load of the university cafeteria from the
    load of past years. The best of 24 teams predicted the load with a root mean squared error of
    8.6 receipts issued in five minutes, with a fusion system based on the top 5 entries achieving
    an even better result of 8.28.},
  code         = {https://bbdc.csl.uni-bremen.de/index.php/2017},
  url          = {https://halcy.de/cites/pdf/weiner2017bremen.pdf},
}