R-tree Analysis. R-trees - performance analysis How many disk (=node) accesses we’ll need for range nn spatial joins why does it matter?

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Presentation transcript:

R-tree Analysis

R-trees - performance analysis How many disk (=node) accesses we’ll need for range nn spatial joins why does it matter?

R-trees - performance analysis A: because we can design split etc algorithms accordingly; also, do query- optimization motivating question: on, e.g., split, should we try to minimize the area (volume)? the perimeter? the overlap? or a weighted combination? why?

R-trees - performance analysis How many disk accesses for range queries? query distribution wrt location? “ “ wrt size?

R-trees - performance analysis How many disk accesses for range queries? query distribution wrt location? uniform; (biased) “ “ wrt size? uniform

R-trees - performance analysis easier case: we know the positions of parent MBRs, eg:

R-trees - performance analysis How many times will P1 be retrieved (unif. queries)? P1 x1 x2

R-trees - performance analysis How many times will P1 be retrieved (unif. POINT queries)? P1 x1 x

R-trees - performance analysis How many times will P1 be retrieved (unif. POINT queries)? A: x1*x2 P1 x1 x

R-trees - performance analysis How many times will P1 be retrieved (unif. queries of size q1xq2)? P1 x1 x q1 q2

R-trees - performance analysis Minkowski sum q1 q2 q1/2 q2/2

R-trees - performance analysis How many times will P1 be retrieved (unif. queries of size q1xq2)? A: (x1+q1)*(x2+q2) P1 x1 x q1 q2

R-trees - performance analysis Thus, given a tree with n nodes (i=1,... n) we expect

R-trees - performance analysis Thus, given a tree with n nodes (i=1,... n) we expect ‘volume’ ‘surface area’ count

R-trees - performance analysis Observations: for point queries: only volume matters for horizontal-line queries: (q2=0): vertical length matters for large queries (q1, q2 >> 0): the count N matters

R-trees - performance analysis Observations (cont’ed) overlap: does not seem to matter formula: easily extendible to n dimensions (for even more details: [Pagel +, PODS93], [Kamel+, CIKM93])

R-trees - performance analysis Conclusions: splits should try to minimize area and perimeter ie., we want few, small, square-like parent MBRs rule of thumb: shoot for queries with q1=q2 = 0.1 (or =0.05 or so).

R-trees - performance analysis Range queries - how many disk accesses, if we just now that we have - N points in n-d space? A: ?

R-trees - performance analysis Range queries - how many disk accesses, if we just now that we have - N points in n-d space? A: can not tell! need to know distribution

R-trees - performance analysis What are obvious and/or realistic distributions?

R-trees - performance analysis What are obvious and/or realistic distributions? A: uniform A: Gaussian / mixture of Gaussians A: self-similar / fractal. Fractal dimension ~ intrinsic dimension

R-trees - performance analysis Formulas for range queries and k-nn queries: use fractal dimension [Kamel+, PODS94], [Korn+ ICDE2000] [Kriegel+, PODS97]

R-trees–performance analysis Assuming Uniform distribution: where And D is the density of the dataset, f the fanout [TS96], N the number of objects

Project Deadlines Phase 1 : Proposal Oct 11, 2002 Phase 2 : Progress Report Nov 11, 2002 Phase 3: Final Report Dec 10, 2002