TO READ

Introduction

  1. G. Vargas-solar, “Efficient data management for putting forward data centric sciences,” in Proc. 1st Int Workshop on Data Science: Methodologies and Use-Cases (DaS’17) (in press), 2017.
  2. M. L. Kersten, S. Idreos, S. Manegold, and E. Liarou, “The Researcher’s Guide to the Data Deluge: Querying a Scientific Database in Just a Few Seconds,” Proc. of the VLDB Endowment (PVLDB), vol. 4, no. 12, 2011.

Big data analytics environments

  1. M. Athanassoulis and S. Idreos, “Design Tradeoffs of Data Access Methods,” Proceedings of the ACM SIGMOD International Conference on Management of Data, Tutorial, pp. 2195–2200, 2016.
  2. S. Idreos et al., “Past and Future Steps for Adaptive Storage Data Systems : From Shallow to Deep Adaptivity.”
  3. M. Anderson et al., “Bridging the Gap Between HPC and Big Data Frameworks,” Vldb, vol. 10, no. 8, pp. 901–912, 2017.

Exploring and querying data

  1. Wasay, M. Athanassoulis, and S. Idreos, “Queriosity: Automated Data Exploration [Vision],” Proceedings of the IEEE International Congress on Big Data, 2015.
  2. M. Then, G. Stephan, T. Neumann, and A. Kemper, “Automatic Algorithm Transformation for Efficient Multi-Snapshot Analytics on Temporal Graphs,” vol. 10, no. 8, pp. 877–888, 2017.
  3. M. Athanassoulis, “Querying Persistent Graphs using Solid State Storage Why Path Processing over Linked Data ?,” no. March, 2013.
  4. I. Alagiannis, M. Athanassoulis, and A. Ailamaki, “Scaling up analytical queries with column-stores,” Proceedings of the Sixth International Workshop on Testing Database Systems – DBTest ’13, p. 1, 2013.
  5. S. L. Xi, O. Babarinsa, M. Athanassoulis, and S. Idreos, “Beyond the Wall: Near-Data Processing for Databases,” Proceedings of the 11th International Workshop on Data Management on New Hardware, p. 2:1–2:10, 2015.11.
  6. M. Joglekar, T. Rekatsinas, H. Garcia-Molina, A. Parameswaran, and C. Ré, “SLiMFast: Guaranteed Results for Data Fusion and Source Reliability,” 2015.
  7. C. De Sa, K. Olukotun, and C. Ré, “Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling,” pp. 1–33, 2016.

Big data analytics: playing with data

  1. J. Qiu, Q. Wu, G. Ding, Y. Xu, and S. Feng, “A survey of machine learning for big data processing,” EURASIP Journal on Advances in Signal Processing, vol. 2016, no. 1, p. 67, 2016.S. Hadjis, C. Zhang, I. Mitliagkas, D. Iter, and C. Ré, “Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs,” 2016.
  2. Ré et al., “Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype?,” Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 283–284, 2015.
  3. Wasay, “Data Canopy: Accelerating Exploratory Statistical Analysis,” pp. 557–572, 2017.
  4.  J. Ratner, S. H. Bach, H. R. Ehrenberg, and C. Ré, “Snorkel: Fast Training Set Generation for Information Extraction.”