References

 

[ 1 ]         Dean, Jeffrey & Ghemawat, Sanjay (2004). “MapReduce: Simplified Data Processing on Large Clusters”. Retrieved Nov. 23, 2011 [pdf]

[ 2 ]         Andrew Pavlo, E. Paulson, A. Rasin, D. J. Abadi, D. J. Dewitt, S. Madden, and M. Stonebraker, “A Comparison of Approaches to Large-Scale Data Analysis”, Brown University, Retrieved 2010-01-11

[ 3 ]         Greg Jorgensen. “Relational Database Experts Jump The MapReduce Shark“. http://typicalprogrammer.com/?p=16

[ 4 ]         David DeWitt; Michael Stonebraker. “MapReduce: A major step backwards“. craig-henderson.blogspot.com. Retrieved 2008-08-27.

[ 5 ]         Ralf Lämmel, “Google’s MapReduce Programming Model — Revisited”, Microsoft

[ 6 ]         “A New Computation Model for Rack-Based Computing” — paper by Foto N. Afrati; Jeffrey D. Ullman; from Stanford University; Not published as of Nov 2009. This paper is an attempt to develop a general model in which one can compare algorithms for computing in an environment similar to what map-reduce expects.

[ 7 ] Nicole Bidoit Dario Colazzo Noor Malla Federico Ulliana, Maurizio Nolè Carlo Sartiani, Processing XML Queries and Updates on Map/Reduce Clusters, In Proceedings of EDBT 2013, Geona, Italy, 2013 [pdf]

[ 8 ] Lämmel, R. (2008). Google’s MapReduce programming model — Revisited. Science of Computer Programming, 70(1), 1–30. doi:10.1016/j.scico.2007.07.001

[ 9 ] Abadi, D., & Dewitt, D. J. (n.d.). mapReduce and Parallel DBmss : friends or foes ?

[ 10 ] Chang, F., Dean, J., Ghemawat, S., Hsieh, W. C., Wallach, D. A., Burrows, M., Chandra, T., et al. (n.d.). Bigtable : A Distributed Storage System for Structured Data.

[ 11 ] Alexandrov, A., Markl, V., Battr, D., Hueske, F., Ewen, S., & Warneke, D. (n.d.). Massively Parallel Data Analysis with PACTs on Nephele.

[ 12 ] Borkar, V., Carey, M. J., & Li, C. (2012). Inside “ Big Data Management ”: Ogres , Onions , or Parfaits ? [ 9 ] Dittrich, J. (2013). Efficient OR Hadoop : Why not both ?, (January), 1–9.

[ 13 ] Dewitt, D. J., & Gray, J. (1992). Parallel Database Systems : The Future of High Performance Database Processing 1, 36(6), 1–26. [ 11 ] http://asterix.ics.uci.edu