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Query Processing in Spatial Network Databases. presented by Hao Hong. Outline. Introduction Related work Spatial query processing in Euclidean Space Disk-based graph representations- CCAM structure Spatial query in network databases Architecture Spatial queries: Nearest neighbor query

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outline
Outline
  • Introduction
  • Related work
    • Spatial query processing in Euclidean Space
    • Disk-based graph representations- CCAM structure
  • Spatial query in network databases
    • Architecture
    • Spatial queries:
      • Nearest neighbor query
      • Other queries
  • Performances
  • Summary
introduction
Introduction
  • Motivation
  • Euclidean distance vs. network distance
    • Euclidean distance <= network distance
  • Conventional spatial queries:
    • K Nearest neighbor query: retrieves the k ponits closest to the query location
    • Range query: retrieves the points covered by certain range
    • Intersection join: retrieves all the intersected points from the query location sets
outline1
Outline
  • Introduction
  • Related work
    • Spatial query processing in Euclidean Space
    • Disk-based graph representations- CCAM structure
  • Spatial query in network databases
    • Architecture
    • Spatial queries:
      • Nearest neighbor query
      • Other queries
  • Performances
  • Summary
spatial query processing in euclidean space
Spatial query processing in Euclidean Space
  • An R-tree index
    • Multidimentional extension of B-tree (quoted from Query Processing in Spatial Network Databases)
    • MBR: Minimum Bounding Rectangle
    • Spatial points are clustered according to the distances between their MBR
    • Hereby, R-tree is fast for spatial query
spatial query processing in euclidean space1
Spatial query processing in Euclidean Space
  • an example

n1

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disk based graph representations ccam structure
Disk-based graph representations- CCAM structure
  • A graph can be represented as
    • An adjacency list
    • Two-dimensional matrix
  • The Connectivity-clustered Access Method (CCAM) structure
    • Stores the single dimensional lists
    • Stores the lists of neighbor nodes together
disk based graph representations ccam structure1
Disk-based graph representations- CCAM structure
  • An example

A B-tree in order of node id

A graph

Disk pages

n1

List 1

page1

n1

n4

5

......

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List 5

n2

n5

n1

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List 3

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......

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List 4

Adjacency list of n1

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null

outline2
Outline
  • Introduction
  • Related work
    • Spatial query processing in Euclidean Space
    • Disk-based graph representations- CCAM structure
  • Spatial query in network databases
    • Architecture
    • Spatial queries:
      • Nearest neighbor query
      • Other queries
  • Performances
  • Summary
architecture
Architecture
  • The above example

Network R-tree

Adjacency component

The network

List 1

page1

E1

E2

n1

......

List 5

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n1

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n4

n5

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List 3

page2

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1

Adjacency list of n1

List 4

E2

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n3

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page2

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MBR(n1, n2)

page1

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MBR(n1, n4)

page1

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MBR(n1, n5)

n4

architecture1
Architecture

Disk pages

Network R-tree

listPtr

2

MBR(n1, n2)

page1

Adjacency

component :

Enanle fast access to

Neighbor nodes

and the corresponding

polyline

listPtr

7

MBR(n1, n4)

E1

E2

listPtr

5

MBR(n1, n5)

page2

...

......

n1

n2

n3

n4

n5

...

......

... ...

listPtr

4

MBR(n4, n5)

MBR(n1, n2)

pageN

Page2

page1

pageN

polyline

Component:

Stores the endpoints

And the MBR of each

segment

Polyline of n1,n2

MBR(n1, n4)

pageN

Polyline of n1,n4

MBR(n1, n5)

pageN

Polyline of n1, n5

pageM

...

...

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primitive operations
Primitive operations
  • Check_entity(seg,p): if entity p is covered by seg, then it returns true
  • Find_segment(p): returns the segment which covers entity p
    • If there are more than one result, then return the first one
    • If there is no result, then return the most appoximate one
  • Find_entities(seg): returns the entities which are covered by the specified segment seg
  • Compute_ND(p1, p2): returns the network distance between the specified entities p1 and p2
    • P1 and p2 are arbitrary points in the network
outline3
Outline
  • Introduction
  • Related work
    • Spatial query processing in Euclidean Space
    • Disk-based graph representations- CCAM structure
  • Spatial query in network databases
    • Architecture
    • Spatial queries:
      • Nearest neighbor query
      • Other queries
  • Performances
  • Summary
nearest neighbor query
Nearest neighbor query
  • Incremental Euclidean Restriction (IER) algorithm: Find the first 1 nearest neighbors of location q
  • Find the Euclidean nearest neighbor n3
  • Compute the network distance:

dN(q, n3)= Compute_ND(q, n3)

  • Set dEmax = dN(q, n3)
  • Repeat the process of retrieving other nodes. To node nk ,
    • if dN(q, nk) < dN(q, n3), then set

dEmax = dN(q, nk)

    • Otherwise, return the node which has set dEmax and stop

n1

5

2

n2

n5

7

1

q

4

n3

1

5

n4

nearest neighbor query1
Nearest neighbor query
  • Incremental Euclidean Restriction (IER) algorithm:

Find the k nearest neighbors of location q

    • Has the R-tree sorted in ascending order of their network distance to q
    • If the kth node is nk, then set dEmax = dN(q, nk)
    • Repeat the process of retrieving other nodes (nk+1, nk+2, ..., nm) , for node ni,
      • if dN(q, ni) <= dEmax, then set
        • dEmax = dN(q, ni)
        • Insert ni to the queue of k nesrest neighbors
        • Remove the former kth node
      • Otherwise, return the node which has set dEmax and stop
nearest neighbor query2
Nearest neighbor query
  • In this case, according to IER algorithm, p5 will be retrieved as the last one, because it has the largest Euclidean distance to q
  • Quoted from Query Processing in Spatial Network Databases
nearest neighbor query3
Nearest neighbor query
  • Incremental Network Expansion (INE) algorithm
    • Performe the nodes checking in the order of encounting sequence
  • Initiate the Q to be (n1, n2), which covers q
  • Since (n1, n2) doesn‘t cover any entity, expand n1 with n7, and Q = <(n2,5), (n7,12)>
  • Repeate the expansion
    • Expand n2 with n4 and n3, Q = <(n4, 7), (n3, 9), (n7, 12)>
    • Here p5 is covered by segment (n2, n4), set threshold dNmax = dN(q, p5) = 6
    • Since the next one in the Q has dN(q, n4) > dNmax , the algorithm terminates, returning p5.
other queries
Other queries
  • Other queries:
    • Range query
    • Closest-Pairs
    • E-distance joins
  • Provide algorithms which process queries in euclidean space and network spaces
outline4
Outline
  • Introduction
  • Related work
    • Spatial query processing in Euclidean Space
    • Disk-based graph representations- CCAM structure
  • Spatial query in network databases
    • Architecture
    • Spatial queries:
      • Nearest neighbor query
      • Other queries
  • Performances
  • Summary
performancess
Performancess
  • Experiments comparing between Euclidean restriction (ER) and Network Expansion (NE)
  • Experimental targets:
    • Page accesses
    • CPU cost
nearest neighbor queries experiments
Nearest Neighbor Queries Experiments
  • |S|=the number of the entity set and |N|=the number of segments
  • When |S| / |N| decrease, the entities among segments are relatively sparse, which relatively increases false hits. Therefore, the amount of network computation is increased.
  • Quoted from Query Processing in Spatial Network Databases
outline5
Outline
  • Introduction
  • Related work
    • Spatial query processing in Euclidean Space
    • Disk-based graph representations- CCAM structure
  • Spatial query in network databases
    • Architecture
    • Spatial queries:
      • Nearest neighbor query
      • Other queries
  • Performances
  • Summary
relating to de3 project
Relating to DE3 project
  • Our idea:
    • Spatial network is represented by Oracle Spatial (SDO_Geometry)
    • Indexing with R-tree
    • Tracking moving objects with segment-based policy
  • We have in common:
    • Processing NN query and Range query in Euclidean space and network space
    • Using R-tree index
  • We can borrow the idea:
    • The network expansion policy
summary
Summary
  • Strong points
    • Contribution: the data structure is high efficient
    • Support more than one query
  • Weak points
    • It is not wasy to read, because there are too many brackets among lines.