The New Jersey Data Reduction Report

  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.},
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
Highly Influential
This paper has highly influenced 11 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 235 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 153 extracted citations

Database Theory — ICDT 2001

Lecture Notes in Computer Science • 2001
View 12 Excerpts
Highly Influenced

Hand-OLAP: Semantics-Aware Compression of Data Cubes for Effective and Efficient OLAP in Mobile Enviroments

2011 IEEE 12th International Conference on Mobile Data Management • 2011
View 4 Excerpts
Highly Influenced

Delivering Semantics-aware Compressed OLAP Views in Mobile Environments with Hand-OLAP

2009 21st IEEE International Conference on Tools with Artificial Intelligence • 2009
View 4 Excerpts
Highly Influenced

Prediction-Based QoS Management for Real-Time Data Streams

2006 27th IEEE International Real-Time Systems Symposium (RTSS'06) • 2006
View 4 Excerpts
Highly Influenced

236 Citations

Citations per Year
Semantic Scholar estimates that this publication has 236 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 24 references

To appear

FL Orlando, Feb.
37 • 1998

An algorithm for unbiased random sampling

J. Sander
A Density - based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , Proc . Second International Conference on Knowledge Discovery and Data Mining • 1996

Estimating the number of classes in a finite population

P. J. Haas, L. Stokes
IBM Research Report RJ 10025, • 1996

Space efficient maintenance of top sellers list in large databases

Phillip Gibbons, Yossi Matias
Unpublished manuscript, Bell Labs, • 1996

Similar Papers

Loading similar papers…