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Spatial Queries

Spatial Queries. Spatial Queries. Given a collection of geometric objects (points, lines, polygons, ...) organize them on disk, to answer efficiently point queries range queries k-nn queries spatial joins (‘all pairs’ queries). Spatial Queries.

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Spatial Queries

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  1. Spatial Queries

  2. Spatial Queries • Given a collection of geometric objects (points, lines, polygons, ...) • organize them on disk, to answer efficiently • point queries • range queries • k-nn queries • spatial joins (‘all pairs’ queries)

  3. Spatial Queries • Given a collection of geometric objects (points, lines, polygons, ...) • organize them on disk, to answer • point queries • range queries • k-nn queries • spatial joins (‘all pairs’ queries)

  4. Spatial Queries • Given a collection of geometric objects (points, lines, polygons, ...) • organize them on disk, to answer • point queries • range queries • k-nn queries • spatial joins (‘all pairs’ queries)

  5. Spatial Queries • Given a collection of geometric objects (points, lines, polygons, ...) • organize them on disk, to answer • point queries • range queries • k-nn queries • spatial joins (‘all pairs’ queries)

  6. Spatial Queries • Given a collection of geometric objects (points, lines, polygons, ...) • organize them on disk, to answer • point queries • range queries • k-nn queries • spatial joins (‘all pairs’ queries)

  7. R-tree … 2 3 5 7 8 4 6 11 10 9 2 12 1 13 3 1

  8. R-trees - Range search pseudocode: check the root for each branch, if its MBR intersects the query rectangle apply range-search (or print out, if this is a leaf)

  9. P1 P3 I C A G H F B J q E P4 P2 D R-trees - NN search

  10. P1 P3 I C A G H F B J q E P4 P2 D R-trees - NN search • Q: How? (find near neighbor; refine...)

  11. R-trees - NN search • A1: depth-first search; then range query P1 I P3 C A G H F B J E P4 q P2 D

  12. R-trees - NN search • A1: depth-first search; then range query P1 P3 I C A G H F B J E P4 q P2 D

  13. R-trees - NN search • A1: depth-first search; then range query P1 P3 I C A G H F B J E P4 q P2 D

  14. R-trees - NN search: Branch and Bound • A2: [Roussopoulos+, sigmod95]: • At each node, priority queue, with promising MBRs, and their best and worst-case distance • main idea: Every face of any MBR contains at least one point of an actual spatial object!

  15. MBR face property • MBR is a d-dimensional rectangle, which is the minimal rectangle that fully encloses (bounds) an object (or a set of objects) • MBR f.p.: Every face of the MBR contains at least one point of some object in the database

  16. Search improvement • Visit an MBR (node) only when necessary • How to do pruning? Using MINDIST and MINMAXDIST

  17. MINDIST • MINDIST(P, R) is the minimum distance between a point P and a rectangle R • If the point is inside R, then MINDIST=0 • If P is outside of R, MINDIST is the distance of P to the closest point of R (one point of the perimeter)

  18. MINDIST computation • MINDIST(p,R) is the minimum distance between p and R with corner points l and u • the closest point in R is at least this distance away u=(u1, u2, …, ud) R u ri = li if pi < li = ui if pi > ui = pi otherwise p p MINDIST = 0 l p l=(l1, l2, …, ld)

  19. MINMAXDIST • MINMAXDIST(P,R): for each dimension, find the closest face, compute the distance to the furthest point on this face and take the minimum of all these (d) distances • MINMAXDIST(P,R) is the smallest possible upper bound of distances from P to R • MINMAXDIST guarantees that there is at least one object in R with a distance to P smaller or equal to it.

  20. MINMAXDIST computation • MINMAXDIST(p,R) guarantees there is an object within the MBR at a distance less than or equal to MINMAXDIST • the closest point in R is less than this distance away u=(u1, u2, …, ud) R u rmk = uk if pk < ½(lk +uk) = lk otherwise; rMi = ui if pi > ½(li +ui) = li otherwise p MINDIST = 0 l l=(l1, l2, …, ld) p

  21. MINDIST and MINMAXDIST • MINDIST(P, R) <= NN(P) <=MINMAXDIST(P,R) MINMAXDIST R1 R4 R3 MINDIST MINDIST MINMAXDIST MINDIST MINMAXDIST R2

  22. Pruning in NN search • Downward pruning: An MBR R is discarded if there exists another R’ s.t. MINDIST(P,R)>MINMAXDIST(P,R’) • Downward pruning: An object O is discarded if there exists an R s.t. the Actual-Dist(P,O) > MINMAXDIST(P,R) • Upward pruning: An MBR R is discarded if an object O is found s.t. the MINDIST(P,R) > Actual-Dist(P,O)

  23. Pruning 1 example • Downward pruning: An MBR R is discarded if there exists another R’ s.t. MINDIST(P,R)>MINMAXDIST(P,R’) R R’ MINDIST MINMAXDIST

  24. Pruning 2 example • Downward pruning: An object O is discarded if there exists an R s.t. the Actual-Dist(P,O) > MINMAXDIST(P,R) R Actual-Dist O MINMAXDIST

  25. Pruning 3 example • Upward pruning: An MBR R is discarded if an object O is found s.t. the MINDIST(P,R) > Actual-Dist(P,O) R MINDIST Actual-Dist O

  26. Ordering Distance • MINDIST is an optimistic distance where MINMAXDIST is a pessimistic one. MINDIST P MINMAXDIST

  27. NN-search Algorithm • Initialize the nearest distance as infinite distance • Traverse the tree depth-first starting from the root. At each Index node, sort all MBRs using an ordering metric and put them in an Active Branch List (ABL). • Apply pruning rules 1 and 2 to ABL • Visit the MBRs from the ABL following the order until it is empty • If Leaf node, compute actual distances, compare with the best NN so far, update if necessary. • At the return from the recursion, use pruning rule 3 • When the ABL is empty, the NN search returns.

  28. K-NN search • Keep the sorted buffer of at most k current nearest neighbors • Pruning is done using the k-th distance

  29. Another NN search: Best-First • Global order [HS99] • Maintain distance to all entries in a common Priority Queue • Use only MINDIST • Repeat • Inspect the next MBR in the list • Add the children to the list and reorder • Until all remaining MBRs can be pruned

  30. 2 Nearest Neighbor Search (NN) with R-Trees • Best-first (BF) algorihm: y axis Root E 10 E 7 E E 3 1 2 E E e f 1 2 8 1 2 8 E E 8 E 2 g E d 1 5 6 i E E E E E E h E E 8 7 9 9 5 6 6 4 query point 2 13 17 5 9 contents 5 4 omitted E 4 search b a region i f h g e a c d 2 b c E 3 5 2 10 13 13 10 13 18 13 x axis E E E 10 0 8 8 2 4 6 4 5 Action Heap Result {empty} Visit Root E E E 1 2 8 1 2 3 E follow E E E {empty} E E 5 5 8 1 9 4 5 3 2 6 2 E E follow E E E E {empty} E E 17 13 5 5 8 2 9 9 7 4 5 3 2 6 8 E follow E E E E E {(h, )} E 17 13 8 5 8 9 5 9 7 4 5 3 6 g E i E E E E 13 10 5 8 7 5 9 4 5 3 13 6 Report h and terminate

  31. HS algorithm Initialize PQ (priority queue) InesrtQueue(PQ, Root) While not IsEmpty(PQ) R= Dequeue(PQ) If R is an object Report R and exit (done!) If R is a leaf page node For each O in R, compute the Actual-Dists, InsertQueue(PQ, O) If R is an index node For each MBR C, compute MINDIST, insert into PQ

  32. Best-First vs Branch and Bound • Best-First is the “optimal” algorithm in the sense that it visits all the necessary nodes and nothing more! • But needs to store a large Priority Queue in main memory. If PQ becomes large, we have thrashing… • BB uses small Lists for each node. Also uses MINMAXDIST to prune some entries

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