query processing in spatial network databases

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Query Processing in Spatial Network Databases

presented by Hao Hong

Dimitris Papadias Jun Zhang

Hong Kong University of Science and Technology

Nikos Mamoulis University of Hong Kong

Yufei Tao City University of Hong Kong

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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

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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

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Spatial query processing in Euclidean Space Disk-based graph representations- CCAM structure

Spatial query in network databases Architecture Spatial queries:



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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

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Spatial query processing in Euclidean Space

an example

n1

n3

n4 n6

n8

E1 E2

E3

E4

n2

n5

n7

E5E6E1

E2

n9 E5 E6 E3 E4

n1 n2 n7 n8 n9 n3 n4 n5 n6

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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

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Disk-based graph representations- CCAM structure

An example

n1

n2

n3

n4

n5

A graph

n1 n4

A B-tree in order of node id

n1 n2 n3 n4 n5

Disk pages

List 1

......

List 5

List 3

......

List 4

page1

page2

5

6

2

1

4

n2 2 n4 7

7

n5 5 null

Adjacency list of n1

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Spatial query processing in Euclidean Space Disk-based graph representations- CCAM

structure Spatial query in network databases

Architecture Spatial queries:



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Architecture The above example

n1

n2

n3

n4

n5

The networkE1 E2

Network R-tree

n1 n2 n3 n4 n55

6

2

1

4

7E1

E2

Adjacency component

List 1

......

List 5

List 3

......

List 4

page1

page2

page2

Adjacency list of n1

2MBR(n1, n2)

page1 7MBR(n1, n4)

page1 5MBR(n1, n5)

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Architecture

E1 E2

Network R-tree

n1 n2 n3 n4 n5

page1

page2

listPtr

Disk pages

2MBR(n1, n2)

listPtr 7MBR(n1, n4)


Adjacency component :

Enanle fast access to Neighbor nodes

and the correspondingpolyline

... ......

... ......


pageN

pageM

polylineComponent:

Stores the endpoints

And the MBR of each segment

Polyline of n1,n2Page2

Polyline of n1,n4

Polyline of n1, n5

... ...

... ...

... ...

page1

...

...

...

pageNMBR(n1, n2)

pageNMBR(n1, n4)

pageNMBR(n1, n5)

... ...

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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

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Nearest neighbor query

Incremental Euclidean Restriction (IER) algorithm: Find the first 1 nearest neighbors of location q

n1

n2

n3

n4

n5

5

5

2

1

4

7

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

q1

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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

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3

5

24

2

61

94 6

4

n1

n2n3

n4

n5

n6

n7n8 n9

p1p2

p3

p4

p5

q

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

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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.

3

5

24

2

61

94 6

4

n1

n2n3

n4

n5

n6

n7n8 n9

p1p2

p3

p4

p5

q

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Other queries

Other queries: Range query Closest-Pairs E-distance joins

Provide algorithms which process queries in euclidean space and network spaces

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Performancess Experiments comparing between Euclidean restriction

(ER) and Network Expansion (NE) Experimental targets:

Page accesses CPU cost

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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.

0

20

40

60

80

0.1 0.5 1 2 10

R-trees

network IER

INE

IER

INE IERINE

IERINE

IERINE

cardinality ratio - |S|/|N|

Page Accesses

0

20

40

60

80

100

0.1 0.5 1 2 10

IER

INE

CPU time -msecs

cardinality ratio - |S|/|N|

Quoted from Query Processing in Spatial Network Databases

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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

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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.

query processing in spatial network databases

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