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Separability of Polyhedra for Optimal Filtering of Spatial and Constraint Data

Summary: Approximate d‑D objects by minimal convex polyhedra whose facets are normal to a chosen set of axes (axes not tied to coordinates), and optimize axis selection to trade filtering quality against storage/access cost. Introduce separability classification and algorithms to minimize axes for a target filter quality or to optimize quality under a fixed axis budget; suited to low‑dim, directionally skewed static spatial data. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1047
Venue
PODS
Year
1995
Pagerank
5.4947899e-05
Overall Rank
5,461 | 62.01%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
620 Constraint Programming and Database Languages: A Tutorial 1995 PODS 0.00019005954
2,970 Variable Independence and Aggregation Closure 1996 PODS 7.7971058e-05
5,715 Measuring Infinite Relations (Extended Abstract) 1995 PODS 5.356394e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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