Spatial Indexing

1
Spatial Indexing

• SAMs

2
Spatial Indexing

• Point Access Methods can index only points. What
  about regions?
• Z-ordering and quadtrees
• Use the transformation technique and a PAM
• New methods Spatial Access Methods SAMs
• R-tree and variations

3
Problem

• Given a collection of geometric objects (points,
  lines, polygons, ...)
• organize them on disk, to answer spatial queries
  (range, nn, etc)

4
Transformation Technique

• Map an d-dim MBR into a point ex.
• (xmin, xmax) (ymin, ymax) gt
• (xmin, xmax, ymin, ymax)
• Use a PAM to index the 2d points
• Given a range query, map the query into the 2d
  space and use the PAM to answer it

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R-tree

• Guttman 84 Main idea allow parents to overlap!
• gt guaranteed 50 utilization
• gt easier insertion/split algorithms.
• (only deal with Minimum Bounding Rectangles -
  MBRs)

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R-tree

• A multi-way external memory tree
• Index nodes and data (leaf) nodes
• All leaf nodes appear on the same level
• Every node contains between m and M entries
• The root node has at least 2 entries (children)

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Example

• eg., w/ fanout 4 group nearby rectangles to
  parent MBRs each group -gt disk page

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Example

• F4

P1
P3
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P4
D
P2
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Example

• F4 m2, M4

P5
P6
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P6
P1
P2
P3
P4
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A
P1
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P4
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P5
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R-trees - format of nodes

• (MBR obj_ptr) for leaf nodes

x-low x-high y-low y-high ...
obj ptr
...
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R-trees - format of nodes

• (MBR node_ptr) for non-leaf nodes

x-low x-high y-low y-high ...
node ptr
...
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R-treesSearch
P5
P6
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P1
P2
P3
P4
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P1
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P3
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P4
D
P2
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R-treesSearch
P5
P6
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P1
P2
P3
P4
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P1
A
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P3
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P4
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P2
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R-treesSearch

• Main points
• every parent node completely covers its
  children
• a child MBR may be covered by more than one
  parent - it is stored under ONLY ONE of them.
  (ie., no need for dup. elim.)
• a point query may follow multiple branches.
• everything works for any(?) dimensionality

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R-treesInsertion
Insert X
P1
P3
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B
X
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P4
D
P2
X
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R-treesInsertion
Insert Y
P1
P3
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P4
Y
D
P2
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R-treesInsertion

• Extend the parent MBR

P1
P3
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P4
Y
D
P2
Y
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R-treesInsertion

• How to find the next node to insert the new
  object?
• Using ChooseLeaf Find the entry that needs the
  least enlargement to include Y. Resolve ties
  using the area (smallest)
• Other methods (later)

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R-treesInsertion

• If node is full then Split ex. Insert w

P1
P3
K
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P4
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P2
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R-treesInsertion

• If node is full then Split ex. Insert w

P3
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P5
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P1
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P4
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Q2
Q1
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R-treesSplit

• Split node P1 partition the MBRs into two groups.

• (A1 plane sweep,
• until 50 of rectangles)
• A2 linear split
• A3 quadratic split
• A4 exponential split
• 2M-1 choices

P1
K
C
A
W
B
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R-treesSplit

• pick two rectangles as seeds
• assign each rectangle R to the closest seed

seed1
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R-treesSplit

• pick two rectangles as seeds
• assign each rectangle R to the closest
  seed
• closest the smallest increase in area

seed1
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R-treesSplit

• How to pick Seeds
• LinearFind the highest and lowest side in each
  dimension, normalize the separations, choose the
  pair with the greatest normalized separation
• Quadratic For each pair E1 and E2, calculate the
  rectangle JMBR(E1, E2) and d J-E1-E2. Choose
  the pair with the largest d

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R-treesInsertion

• Use the ChooseLeaf to find the leaf node to
  insert an entry E
• If leaf node is full, then Split, otherwise
  insert there
• Propagate the split upwards, if necessary
• Adjust parent nodes

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R-TreesDeletion

• Find the leaf node that contains the entry E
• Remove E from this node
• If underflow
• Eliminate the node by removing the node entries
  and the parent entry
• Reinsert the orphaned (other entries) into the
  tree using Insert
• Other method (later)

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R-trees Variations

• R-tree DO not allow overlapping, so split the
  objects (similar to z-values)
• R-tree change the insertion, deletion
  algorithms (minimize not only area but also
  perimeter, forced re-insertion )
• Hilbert R-tree use the Hilbert values to insert
  objects into the tree

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Spatial Access Methods

• PAMs
• Grid File
• kd-tree based (LSD-, hB- trees)
• Z-ordering B-tree
• R-tree
• Variations R-tree, Hilbert R-tree

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R-tree
Multi-way external memory structure, indexes
MBRs Dynamic structure
P1
P3
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P4
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P2
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R-tree

• The original R-tree tries to minimize the area of
  each enclosing rectangle in the index nodes.
• Is there any other property that can be
  optimized?

R-tree ? Yes!
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R-tree

• Optimization Criteria
• (O1) Area covered by an index MBR
• (O2) Overlap between directory MBRs
• (O3) Margin of a directory rectangle
• (O4) Storage utilization
• Sometimes it is impossible to optimize all the
  above criteria at the same time!

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R-tree

• ChooseSubtree
• If next node is a leaf node, choose the node
  using the following criteria
• Least overlap enlargement
• Least area enlargement
• Smaller area
• Else
• Least area enlargement
• Smaller area

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R-tree

• SplitNode
• Choose the axis to split
• Choose the two groups along the chosen axis
• ChooseSplitAxis
• Along each axis, sort rectangles and break them
  into two groups (M-2m2 possible ways where one
  group contains at least m rectangles). Compute
  the sum S of all margin-values (perimeters) of
  each pair of groups. Choose the one that
  minimizes S
• ChooseSplitIndex
• Along the chosen axis, choose the grouping that
  gives the minimum overlap-value

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R-tree

• Forced Reinsert
• defer splits, by forced-reinsert, i.e. instead
  of splitting, temporarily delete some entries,
  shrink overflowing MBR, and re-insert those
  entries
• Which ones to re-insert?
• How many? A 30

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R-tree variations

• What about static datasets?
• (no ins/del) Hilbert
• What about other bounding shapes?

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R-trees - variations

• what about static datasets (no ins/del/upd)?
• Q Best way to pack points?

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R-trees - variations

• what about static datasets (no ins/del/upd)?
• Q Best way to pack points?
• A1 plane-sweep
• great for queries on x
• terrible for y

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R-trees - variations

• what about static datasets (no ins/del/upd)?
• Q Best way to pack points?
• A1 plane-sweep
• great for queries on x
• bad for y

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R-trees - variations

• what about static datasets (no ins/del/upd)?
• Q Best way to pack points?
• A1 plane-sweep
• great for queries on x
• terrible for y
• Q how to improve?

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R-trees - variations

• A plane-sweep on HILBERT curve!

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R-trees - variations

• A plane-sweep on HILBERT curve!
• In fact, it can be made dynamic (how?), as well
  as to handle regions (how?)

42
R-trees - variations

• Dynamic (Hilbert R-tree)
• each point has an h-value (hilbert value)
• insertions like a B-tree on the h-value
• but also store MBR, for searches

43
Hilbert R-tree

• Data structure of a node?

x-low, ylow x-high, y-high
LHV
ptr
h-value gt LHV MBRs inside parent MBR
44
R-trees - variations

• Data structure of a node?

B-tree
x-low, ylow x-high, y-high
LHV
ptr
h-value gt LHV MBRs inside parent MBR
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R-trees - variations

• Data structure of a node?

R-tree
x-low, ylow x-high, y-high
LHV
ptr
h-value gt LHV MBRs inside parent MBR