The New Jersey Data Reduction Report

@article{Barbar1997TheNJ,
  title={The New Jersey Data Reduction Report},
  author={Daniel Barbar{\'a} and William DuMouchel and Christos Faloutsos and Peter J. Haas and Joseph M. Hellerstein and Yannis E. Ioannidis and H. V. Jagadish and Theodore Johnson and Raymond T. Ng and Viswanath Poosala and Kenneth A. Ross and Kenneth C. Sevcik},
  journal={IEEE Data Eng. Bull.},
  year={1997},
  volume={20},
  pages={3-45}
}
There is often a need to get quick approximate answers from large databases. This leads to a need for data reduction. There are many di erent approaches to this problem, some of them not traditionally posed as solutions to a data reduction problem. In this paper we describe and evaluate several popular techniques for data reduction. Historically, the primary need for data reduction has been internal to a database system, in a cost-based query optimizer. The need is for the query optimizer to… CONTINUE READING
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