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Multimedia Database Systems

Jan 14, 2016

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ashlyn

Department of Informatics Aristotle University of Thessaloniki Fall 2008. Multimedia Database Systems. Indexing Part B Metric-based Indexing Techniques. Περιεχόμενα. Εισαγωγή Μετρικές Δομές Οργάνωσης των Δεδομένων Μετρικοί Χώροι Ερωτήματα Ομοιότητας Μ-δένδρο Slim- δένδρο - PowerPoint PPT Presentation

  • *Multimedia Database SystemsIndexing Part BMetric-based Indexing Techniques

    Department of InformaticsAristotle University of ThessalonikiFall 2008

  • -Slim- M-trees

    **

  • -> .. .. (, + ) (, ) -> (R, R*, M, Slim )*

  • =(D,d) D d 3 , d(Ox, Oy) = d(Oy, Ox), d(Ox, Oy) > 0 (Ox Oy) d(Ox, Ox) = 0 , d(Ox, Oy) d(Ox, Oz) + d(Oz, Oy)*

  • 3 Q a 2 Q ( best so far) d(Q,b) = 7.81 d(Q,c) : d(Q,b) d(Q,c) + d(b,c)d(Q,b) - d(b,c) d(Q,c) 7.81 - 2.30 d(Q,c) 5.51 d(Q,c) c 5.51 Q best so far 2*abcQ

    a

    b

    c

    a

    6.70

    7.07

    b

    2.30

    c

  • Q D r(Q), range(Q, r(Q)) Oj d(Oj, Q) r(Q) k Q D k 1, k NN(Q, k) k Q.*

  • d (black-box) CPU ( ) / ( )*M-tree

  • R- - *Euclidean L2M-tree

  • ..

    routing objects

    *M-tree

    Oj joid(Oj) d(Oj, P(Oj)) Oj

    Or routing rptr(T(Or)) (covering tree) T(Or)r(Or) Ord(Or, P(Or)) Or

  • range(Q, r(Q)) d(Or, Q) > r(Q) + r(Or), Oj (Or) : d(Oj, Q) > r(Q). (Or) |d(Op, Q) d(Or, Op)| > r(Q) + r(Or), d(Or, Q) > r(Q) + r(Or) Or 40%*-tree

  • kNN branch-and-bound 2 PR - dmin(T(Or)) = max{d(Or, Q) r(Or), 0} ( ) k ( )*-tree

  • - , - Or On d(Or, On) r(Or)*-tree

  • *PromotionPartition (split)

  • : promoting partioning ( ) ( )* (split)

  • Promotion , promoted m_RAD promote r(Op1) + r(Op2) ( ) mM_RAD M_LB_DIST RANDOM 2 SAMPLING , ( )* (promotion)

  • Partition routing , , 1 2 Generalized Hyperplane, Oj routing . d(Oj, Op1) d(Oj, Op2), Oj 1 2Balanced: d(Oj, Op1) d(Oj, Op2) Oj N. , . 1 p1 2 p2 , .* (partition)

  • - . / CPU .*

  • paged, (balanced) , , . features* M-tree

  • (-), : (minimum spanning tree MST) Slim-down post-processing tighter . (fat-factor, bloat-factor) *Slim-tree

  • -

    *Slim-tree

    OidiIdentifier iD(Oi, Rep(Oi)) Oi Rep(Oi)Oi i

    Oi - Radiusi D(Oi, Rep(Oi)) Oi Rep(Oi)Ptr(TOi) -NEntries(Ptr(TOi)) Ptr(TOi)

  • Slim-

    ChooseSubtree ChooseSubtreerandom: mindist: minoccup: (occupancy)*Slim-tree

  • random: minMax: . . MST: , ( ) . *Slim-tree

  • 2 1 2 - 2 2 - 2 Fat-factorBloat-factor*Slim-tree

  • Fat-factor Bloat-factor 0 10 1 *Slim-tree

  • Slim-down (tighter)

    i , c b i, j, c. j , i c j. i 1 2 . (full round) 2 , , 1 2*Slim-tree

  • Slim-down c i j 2, i , i i * a Slim- Sierpinsky (bloat-factor = 0.03) b (bloat-factor = 0.01)Slim-tree

  • Slim- - *Slim-tree

  • Slim- : ChooseSubtree - (MST) Slim-down , , fat-factor bloat-factor

    * Slim-tree

  • ; exact / *

  • M-trees k-NN (10 k-) CHV10.000 45 UV CV cluster

    *

  • Improvement in efficiency (IE), .Precision of approximation (P)

    Relative distance error ()

    = 0 / *

  • Approximation through relative distance errors* . .

  • Approximate search through distance distributions* . , ( =0,01).

  • Approximation through the slowdown of distance improvements* (precision) .

  • *Approximation through the slowdown of distance improvements .

  • precision. 10 100 100 10 . 3 . . .*

  • CV *

  • CHV *

  • UV

    *

  • 10*

  • : *

  • MS (metric spaces) VS (vector spaces)

    VSLp (vector spaces, Lp distance)*

  • CS (changing space) RC (reducing comparisons) *

  • NG (no guarantees)DG (deterministic guarantees)PG (probabilistic guarantees)PGpar (probabilistic guarantees, parametric)PGnpar (probabilistic guarantees, non-parametric)*

  • SA (static approach)

    (interactive approach)

    *

  • .

    *

  • *

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