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Veranstaltungen im Wintersemester 2014 / Bachelor-Seminar: Big Data Analytics
Literatur

Literatur:

  • Ahmed, N.K. et al., 2014. Graph Sample and Hold: A Framework for Big-graph Analytics. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’14. New York, NY, USA: ACM, pp. 1446–1455.
  • Dean, T. et al., 2013. Fast, Accurate Detection of 100,000 Object Classes on a Single Machine. In Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. CVPR ’13. Washington, DC, USA: IEEE Computer Society, pp. 1814–1821.
  • Dong, X. et al., 2014. Knowledge Vault: A Web-scale Approach to Probabilistic Knowledge Fusion. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’14. New York, NY, USA: ACM, pp. 601–610.
  • Gonzalez, J.E. et al., 2012. PowerGraph: Distributed Graph-parallel Computation on Natural Graphs. In Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation. OSDI’12. Berkeley, CA, USA: USENIX Association, pp. 17–30.
  • Han, W.-S. et al., 2013. TurboGraph: A Fast Parallel Graph Engine Handling Billion-scale Graphs in a Single PC. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’13. New York, NY, USA: ACM, pp. 77–85.
  • Liu, C. et al., 2010. Distributed Nonnegative Matrix Factorization for Web-scale Dyadic Data Analysis on Mapreduce. In Proceedings of the 19th International Conference on World Wide Web. WWW ’10. New York, NY, USA: ACM, pp. 681–690.
  • Ottaviano, G., Venturini, R., 2014. Partitioned Elias-Fano Indexes. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR ’14. New York, NY, USA: ACM, pp. 273–282.
  • Rakthanmanon, T. et al., 2012. Searching and Mining Trillions of Time Series Subsequences Under Dynamic Time Warping. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’12. New York, NY, USA: ACM, pp. 262–270.
  • Recht, B. et al., 2011. Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent. In Advances in Neural Information Processing Systems. pp. 693–701.
  • Yu, H.-F. et al., 2012. Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems. In Proceedings of the 2012 IEEE 12th International Conference on Data Mining. ICDM ’12. Washington, DC, USA: IEEE Computer Society, pp. 765–774.