Spatial Joins Using Seeded Trees - UCR CS

Spatial

Joins

Ming-Ling Electrical

Lo

Using

and

Engineering

Seeded Trees* V. Ravishankar

Chinya

and Computer

University

of Michigan-Ann

1301 Beal Avenue, mingling,

Ann

ravi@eecs

is

methods for

not

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the

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for

paper,

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method trees

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data

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construction

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be replaced

by smsller

seeded

trees

icantly

in terms

growth

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that

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databases

creasing query

out

other

to

accesses. methods

penalties

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using

tial

selection,

spatial find

all

spatial

a predefine

find

in

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algorithms

into

incurred

between

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method

the

objects

access

cilitate

such

operations

Most

research

on

operation.

are

window

contained area,

overlapping

spatial

years.

search

operations

Many

in-

and

methods

have

Examples queries,

within point

a particular been

on the spa-

or

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in space.

devised

to fa-

by the Consortium Networking.

for

up both

the

SIGMOD 94- 5/94 Minneapolis, Minnesota, USA @ 1994 ACM 0-89791 -639-5/94/0005..$3.50

209

similar

method

[LH92]

has

range

space

B+

tree. two

Several

spatial

Joining

database

algorithm

using

can

as some

tree-like

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level

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n must to

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such

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by their indices

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[Gun93]

of tree-like

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information out,

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applicability

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using

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queries.

can then

organized

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at at join

[NHS84]

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which

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grid-files

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elements,

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breadth-first

Permission to co without fee all or part of this material is granted provid INl t at the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and I& date appear, and notice is given that copying is by permission of the Association of Computing Machinery. To copy otherwise, or to republish, requires a fee andlor specific permission.

assumes

based

builds data

accelerated

access

them.

those

be

indices.

method for two

as

generally

tree-like

sets are known

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for joins

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data

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main

for

speed

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[Sam90].

*This work was supported in part International Earth Science Information

time.

associated

[Rot91]

on the

Spatial

indices,

based

[Va187]

time

relevant

have

intersect-

queries, point

these trades

as the

1.1

of

index

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operations

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on join

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except

and

relatively

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Introduction

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ing

that

studies

outperform

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tree

disk accesses during

performance

1

edu

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MI 48109

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Arbor,

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SRF87]

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atively

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[BKS93]

et al.

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R-trees

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tions ations.

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Section

6 concludes

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6:

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tested

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to guide

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clustered,

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levels

factor

buffer

cannot

into

resulting

of spatial

seeding

trees,

addresses

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characteristics

algorithm

construction

a spatial

index

method

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are used

seed

methods

be very

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or where

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techniques

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work,

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input

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derived

joins,

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or (2) one or both

scenario,

scenario,

in both

the by

can use the tree

for

of the

one-seeded-tree

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processed

efficient

occur:

enough

of non-spatial

dynamically

selectivity

the

spatial

on

to realize

However,

that

seeded

The

using

of complicated

closely

in the join,

results

that

is more

case if the

other

are related

join

situations

results

the

using

However,

a spatial

are

including

R-trees

method

R-tree.

the

input and

join

perform

sets

operations

pre-computed

tree

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necessary

seeded the

the

will

situations

help

and

Such

if necessary,

and

spatial

with

method

the

Discussion

a seeded

measizes,

Conclusions

In

1.0.

that

join

for

levels,

nodes

called

We have

has

future

is no greater

constructed

=

equals

tree

of seed

join

5

noting after

method

seeded

553 581

IID.sII

rectangles

within

be retained

5772 5872

100K,

Addressing

It is worth

rd

STJ1-3F STJ2-3F

work

these

costs

Wr

-lb

little

the

in

as the

based

way

area.

find

quantitative

sets.

best

in this

the

to trees

such

databases

tests)

rd

total

data the

and

the common

seeded

is finding

sets using

[OR93],

necessary

operations

input

choosing

Relatively

is

solution

data

sampling

characteristics,

of spatial

Another the

for building

construct

issue

the

0.8.

data

research to

related

to predict

query.

area. from

as a basis

way

outcomes

map

as spatial

More

closely

sures

the

information

use such

RI’,J STJ1-2N STJ2-2N

I

divide

to time

spatially

seeded

tree

organization.

set of seed levels

artificially

1If a seeded

constructed

any pre-computed a set of seed levels

original

R-tree.

used,

whose

original

219

spatial

tree is to be used in spatial selections after the join terminates, seed level tiltering should not be

as the set of data data.

indexed

by the tree will

be a subset

of the

CPU

costs

lowest not

for

the

among

seeded

all

tree

methods

method

are

seed

level

when

always

[Ore91]

the

filtering

and

act ivat ed. Seed

when

level the

reduce

data

CPU

costs

by

CPU

[Rot91]

However,

when

If the

can

the

a join,

this

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