Reverse Furthest Neighbors in Spatial Databases. Bin Yao , Feifei Li, Piyush Kumar Florida State University, USA. A Novel Query Type. Reverse Furthest Neighbors (RFN) Given a point q and a data set P, find the set of points in P that take q as their furthest neighbor Two versions :
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Reverse Furthest Neighbors in Spatial Databases
Bin Yao, Feifei Li, Piyush Kumar
Florida State University, USA
Given a point q and a data set P, find the set of points in P that take q as their furthest neighbor
P: a set of sites of interest in a region
For any site, it could find the sites that take itself as their furthest neighbors
This has an implication that visitors to the RFN of a site are unlikely to visit this site because of the long distance.
Ideally, it should put more efforts in advertising itself in those sites.
P: a set of customers
Q: a set of business competitors offering similar products
A distance measure reflecting the rating of customer(p) to competitor(q)’s product.
A larger distance indicates a lower preference.
For any competitor in Q, an interesting query is to discover the customers that dislike his product the most among all competing products in the market.
Lemma: Any point from the furthest Voronoi cell(fvc) of p takes p as its furthest neighbor among all points in P.
Lemma: the furthest point for p from P is always a vertex of
the convex hull of P. (i.e., only vertices of CH have RFN.)
CHFC(Query q; R-tree T (on P))
// compute only once
The qhull algorithm
Adapt qhull to R-tree
Limitation: query group size may not fit in memory
Solution: Approximate convex hull of Q (Dudley’s approximation)
Number of IOs
CPU: vary A, Q=1000
IOs: vary A, Q=1000
BRFN number of IOs
MRFN number of IOs