The Onion Technique: Indexing for Linear Optimization Queries

@inproceedings{Chang2000TheOT,
  title={The Onion Technique: Indexing for Linear Optimization Queries},
  author={Yuan-Chi Chang and Lawrence D. Bergman and Vittorio Castelli and Chung-Sheng Li and Ming-Ling Lo and John R. Smith},
  booktitle={SIGMOD Conference},
  year={2000}
}
This paper describes the Onion technique, a special indexing structure for linear optimization queries. Linear optimization queries ask for top-N records subject to the maximization or minimization of linearly weighted sum of record attribute values. Such query appears in many applications employing linear models and is an effective way to summarize representative cases, such as the top-50 ranked colleges. The Onion indexing is based on a geometric property of convex hull, which guarantees that… CONTINUE READING
Highly Influential
This paper has highly influenced 29 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 242 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 154 extracted citations

A Partitioned-Based Method of Convex Skyline for Efficient Processing Top-k Queries

2012 Second International Conference on Cloud and Green Computing • 2012
View 7 Excerpts
Highly Influenced

Efficient Dual-Resolution Layer Indexing for Top-k Queries

2012 IEEE 28th International Conference on Data Engineering • 2012
View 11 Excerpts
Highly Influenced

243 Citations

0102030'00'04'09'14'19
Citations per Year
Semantic Scholar estimates that this publication has 243 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.

Fuzzy Queries in Multimedia Database Systems

PODS • 1998
View 3 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…