Bremen Big Data Challenge 2017: Predicting University Cafeteria Load
by , , , , , , , , , , ,
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.
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PDF URL: https://www.csl.uni-bremen.de/cms/images/documents/publications/WeinerEtAl_KI2017.pdf
Reference:
Bremen Big Data Challenge 2017: Predicting University Cafeteria Load (Jochen Weiner, Lorenz Diener, Simon Stelter, Eike Externest, Sebastian Kühl, Christian Herff, Felix Putze, Timo Schulze, Mazen Salous, Hui Liu, Dennis Küster, Tanja Schultz), In KI 2017: Advances in Artificial Intelligence (Gabriele Kern-Isberner, Johannes Fürnkranz, Matthias Thimm, eds.), Springer International Publishing, 2017.
Bibtex Entry:
@inproceedings{weiner2017bbdc,
  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},
  title = {Bremen Big Data Challenge 2017: Predicting University Cafeteria Load},
  booktitle = {{KI} 2017: Advances in Artificial Intelligence},
  editor = {Kern-Isberner, Gabriele and F{\"u}rnkranz, Johannes and Thimm, Matthias},
  year = {2017},
  publisher = {Springer International Publishing},
  address = {Cham},
  pages = {380--386},
  isbn = {978-3-319-67190-1},
  doi = {10.1007/978-3-319-67190-1_35},
  url = {https://www.csl.uni-bremen.de/cms/images/documents/publications/WeinerEtAl_KI2017.pdf},
  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.}
}