Classifying

Network

Architectures

for

Locating

Information

Sources

Technical

Report

Version

R.

Dolin

D.

Agrawal

A.

El

Abbadi

Department

of

Computer

Science

University

of

California

Santa

Barbara

CA

frad

agrawal

amrg@cs.ucsb.edu

September

Abstract

This

paper

presents

three

broad

classes

of

network

architecture

that

support

discovery

of

information

sources

Relevant

concepts

such

as

query

routing

and

the

extraction

propagation

and

retrieval

of

metadata

are

defined

Based

on

these

concepts

different

models

of

locating

and

querying

relevant

information

sources

are

presented

Finally

we

estimate

several

important

characteristics

of

these

models

and

classes

as

well

as

their

expected

scalability

Introduction

Finding

appropriate

sources

of

information

on

the

Internet

increases

in

difficulty

with

the

increasing

number

of

Internet

users

the

number

of

sources

providing

electronic

documents

the

number

of

systems

for

locating

these

documents

and

the

volume

of

available

data

An

information

source

is

any

site

in

the

network

that

provides

electronic

documents

such

as

journal

articles

books

hypertext

pages

images

files

etc.

Prior

to

digital

networks

the

number

of

potential

information

sources

was

relatively

limited

mainly

by

locality

hence

a

user

could

discover

what

was

available

by

browsing

individual

sources

In

a

widearea

network

such

as

the

Internet

information

resides

in

numerous

heterogeneous

systems

which

are

geographically

distributed

each

such

distributed

system

may

provide

many

information

sources

Individual

systems

provide

various

discovery

tools

e.g.

Archie

Veronica

Yahoo

However

users

can

get

easily

lost

in

the

vast

space

of

documents

that

are

indexed

by

these

systems

No

system-independent

location

facility

currently

exists

What

was

once

a

problem

of

limited

choice

has

become

a

problem

of

too

much

choice

The

existence

of

thousands

of

newsgroups

World-Wide

Web

-LRB-

WWW

-RRB-

sites

FTP

archives

and

digital

libraries

renders

traditional

methods

of

locating

good

information

sources

infeasible

Current

WWW

indexes

such

as

AltaVista

-LSB-

-RSB-

Lycos

-LSB-

-RSB-

and

Yahoo

-LSB-

-RSB-

which

are

designed

for

locating

information

on

the

Internet

are

limited

in

several

ways

For

example

they

are

limited

to

on-line

text

documents

and

they

give

no

control

to

the

sources

of

the

documents

to

determine

the

frequency

with

which

they

are

indexed

The

gGlOSS

text

database

discovery

system

-LSB-

-RSB-

represents

sources

by

vectors

of

term

frequencies

It

is

similar

in

this

regard

to

the

SMART

automated

text

retrieval

system

-LSB-

-RSB-

replacing

the

two-dimensional

term/document

matrix

by

a

term/collection

matrix

SMART

and

gGlOSS

are

only

two

of

several

automatic

text

retrieval

systems

Other

notable

systems

include

ConQuest

-LSB-

-RSB-

which

uses

a

static

semantic

network

based

on

dictionary

definitions

and

Latent

Semantic

Indexing

-LRB-

LSI

-RRB-

-LSB-

-RSB-

which

uses

a

dynamic

semantic

network

based

on

word

co-occurrences

within

documents

in

a

collection

Text

Diagram

Symbol

Symbol

Description

QG

ffi

Query

Generator

S

Information

Source

I

Intermediary

Table

Objects

Used

in

Model

Diagrams

Content

Routing

-LSB-

-RSB-

is

a

system

in

which

queries

get

routed

to

the

available

servers

based

on

the

expected

relevance

of

the

server

to

the

query

This

system

also

propagates

descriptions

of

server

contents

through

a

hierarchy

of

information

servers

Harvest

-LSB-

-RSB-

automatically

indexes

documents

within

a

source

and

distributes

these

indexes

It

can

be

used

to

describe

WWW

sites

newsgroups

repositories

of

technical

reports

etc.

and

includes

subsystems

for

caching

and

replicating

the

indexes

This

paper

presents

a

systematic

description

of

possible

network

architectures

that

support

the

discovery

of

information

sources

and

analyzes

their

differences

The

next

section

describes

relevant

concepts

such

as

query

routing

and

the

extraction

propagation

and

retrieval

of

metadata

Based

on

these

concepts

different

models

of

locating

and

querying

relevant

information

sources

are

presented

within

three

broad

classes

Finally

we

estimate

several

important

characteristics

of

these

models

and

classes

as

well

as

their

expected

scalability

Terminology

We

first

describe

the

terms

which

will

be

used

in

the

network

models

We

define

a

document

operationally

as

an

atomic

entity

which

can

be

searched

for

retrieved

and

viewed

though

not

necessarily

stored

as

a

single

unit

electronically

or

otherwise

A

document

is

not

required

to

be

stored

atomically

nor

is

it

necessarily

always

used

atomically

although

one

can

access

sections

of

a

document

a

document

must

be

available

as

an

atomic

entity

A

collection

refers

to

a

well

defined

set

of

documents

A

query

refers

to

a

document

specification

against

which

documents

in

a

collection

can

be

compared

for

relative

similarity

A

query

generator

is

a

system

which

electronically

creates

and

transmits

queries

they

are

represented

as

circles

ffi

in

the

model

diagrams

A

search

engine

is

an

interface

which

accepts

as

input

a

query

about

a

particular

collection

and

returns

as

output

a

query

result

which

is

a

subset

of

the

collection

which

it

has

determined

contain

documents

relevant

to

the

query

The

subset

does

not

contain

the

actual

documents

but

rather

pointers

to

and

possibly

descriptions

of

the

documents

A

single

collection

may

have

multiple

search

engines

associated

with

it

but

by

definition

a

search

engine

can

work

on

only

a

single

collection

-LRB-

i.e.

we

do

not

require

a

collection

to

be

an

input

parameter

to

a

search

engine

-RRB-

An

information

source

or

source

refers

to

a

particular

-LSB-

document

collection

search

engine

-RSB-

pair

Sources

are

represented

as

triangles

in

the

model

diagrams

We

point

out

that

collection

refers

only

to

a

set

of

documents

while

source

refers

more

generally

to

a

machine

including

its

documents

search

engine

metadata

etc.

Metadata

refers

to

the

description

of

an

object

We

make

a

distinction

between

document

metadata

and

collection

metadata

The

former

is

information

about

individual

documents

while

the

latter

is

summary

information

about

an

entire

collection

and

possibly

other

descriptive

information

about

the

machine

network

parameters

etc.

Unless

otherwise

stated

metadata

will

refer

to

collection

metadata

Therefore

a

query

result

is

just

the

document

metadata

that

would

be

returned

by

a

search

engine

given

a

particular

query

It

is

not

necessarily

returned

directly

by

a

search

engine

but

by

any

system

which

gives

the

same

document

subset

Finally

an

intermediary

drawn

as

a

small

or

large

box

in

the

model

diagrams

is

a

logical

machine

on

the

network

which

provides

a

level

of

indirection

between

a

request

for

information

and

the

original

supplier

of

the

result

usually

an

information

source

A

query

intermediary

acts

as

a

pseudo

search

engine

it

takes

queries

as

input

and

provides

query

results

as

output

However

a

query

intermediary

differs

from

a

search

engine

in

that

-RRB-

it

does

not

necessarily

have

direct

access

to

any

document

collection

and

that

-RRB-

it

may

return

query

results

for

several

sources

simultaneously

A

metadata

intermediary

accepts

a

request

for

either

document

or

collection

metadata

and

supplies

the

relevant

result

Intermediaries

may

store

information

locally

or

may

request

information

either

from

another

intermediary

or

from

a

source

A

summary

of

the

basic

design

objects

shown

in

the

model

diagrams

is

presented

in

Table

Our

network

models

describe

several

common

activities

Query

propagation

or

query

routing

is

the

process

of

passing

a

query

from

a

query

generator

to

either

a

source

or

a

query

intermediary

This

action

is

expected

to

result

in

result

retrieval

which

returns

a

query

result

to

the

query

generator

Result

merging

involves

combining

query

results

for

a

query

sent

simultaneously

to

multiple

sources

Documents

in

a

result

set

are

often

ranked

within

that

set

result

merging

can

include

rank

merging

where

the

union

of

the

multiple

result

sets

are

re-ranked

for

the

en

tire

union

Result

merging

can

also

include

duplicate

detection

in

which

an

attempt

is

made

to

detect

if

the

same

document

has

been

returned

by

multiple

sources

Metadata

extraction

denotes

the

process

of

deriving

either

document

or

collection

metadata

Document

metadata

extraction

can

take

place

either

at

a

source

or

at

an

intermediary

which

has

direct

access

to

a

source

's

documents

Collection

metadata

extraction

is

generally

done

only

directly

at

a

source

though

there

is

no

requirement

for

this

Metadata

propagation

involves

the

pushing

of

metadata

by

a

source

to

any

other

machine

that

either

stores

it

locally

for

long-term

use

or

else

uses

it

immediately

to

help

resolve

a

query

and

then

discards

it

Metadata

retrieval

involves

the

pulling

of

metadata

by

an

intermediary

or

a

query

generator

The

models

that

follow

have

examples

of

both

long-term

and

shortterm

use

of

both

document

and

collection

metadata

An

update

is

the

process

of

bringing

up-to-date

all

stored

metadata

across

the

entire

network

about

a

particular

source

Network

Models

This

section

describes

three

classes

of

network

models

with

increasing

complexity

that

encompass

the

discovery

and

querying

of

information

sources

The

first

class

of

models

uses

no

intermediate

servers

so

that

the

only

communication

is

directly

between

query

generators

and

information

sources

The

next

level

of

complexity

is

obtained

by

placing

a

single

intermediary

between

the

query

generators

and

the

sources

The

simplified

model

in

this

class

is

the

system

used

by

most

current

WWW

index

sites

The

final

level

of

complexity

is

obtained

by

allowing

for

multiple

communicating

intermediaries

The

corresponding

metadata

for

this

class

of

models

is

potentially

much

more

complex

and

carefully

distributed

The

models

described

below

are

summarized

in

Figure

Direct

Within

Class

the

Direct

models

we

describe

three

architectures

These

models

are

not

intended

to

be

realistic

for

a

large-scale

network

but

rather

exemplary

of

particular

problems

of

scalability

which

will

be

discussed

in

Section

The

Simple

Model

is

the

most

basic

model

which

contains

one

or

more

query

generators

but

only

a

single

information

source

This

model

is

shown

in

Figure

Queries

are

sent

directly

to

the

information

source

and

the

results

are

sent

back

directly

to

the

Figure

Model

Classifications

ffl

Class

Direct

-LCB-

Simple

Model

-LCB-

Remote

Model

-LCB-

Local

Model

ffl

Class

Single

Intermediary

-LCB-

Brute-Force

Model

-LCB-

STARTS

Model

ffl

Class

Multiple

Intermediaries

-LCB-

Pharos

Model

Figure

Simple

Model

Query

Users

Source

generator

of

each

query

Since

we

defined

an

information

source

as

a

unique

-LSB-

collection

search

engine

-RSB-

pair

this

model

does

not

differentiate

between

simple

one-site

sources

and

complex

multi-site

sources

We

require

only

from

the

perspective

of

the

query

generators

-RRB-

that

only

unique

query

results

are

returned

and

-RRB-

that

there

is

a

single

search

engine

query

point

for

all

of

the

-LRB-

possibly

distributed

-RRB-

data

Thus

this

model

avoids

any

problems

associated

with

result

merging

as

discussed

in

Section

The

next

model

allows

for

multiple

sources

by

simply

adding

them

in

and

not

worrying

about

which

sources

are

queried

nor

how

a

query

generator

should

merge

the

results

It

is

implicitly

assumed

that

each

query

will

be

propagated

to

all

sources

This

model

the

Remote

Model

is

depicted

in

Figure

Neither

the

Simple

Model

nor

the

Remote

Model

include

the

use

of

any

collection

metadata

The

next

model

the

Local

Model

requires

that

each

query

generator

pre-collect

sufficient

metadata

about

each

source

so

that

the

best

sources

can

be

decided

locally

at

the

generator

without

first

contacting

any

remote

Figure

Remote

Model

Query

Users

Sources

Figure

Local

Model

Query

Users

Sources

Stored

Metadata

Locally

site

To

handle

a

query

the

only

traffic

generated

in

this

model

is

that

used

to

query

the

most

relevant

sources

and

retrieve

their

results

This

model

assumes

that

there

is

a

standard

method

with

which

metadata

can

be

retrieved

and

merged

by

the

query

generators

This

model

is

shown

in

Figure

Single

Intermediary

Within

Class

the

Single

Intermediary

models

we

describe

two

architectures

the

Brute-Force

-LRB-

BF

-RRB-

Model

and

the

STARTS

Model

-LSB-

-RSB-

Both

these

models

maintain

a

single

intermediary

between

the

query

generators

and

sources

This

intermediary

handles

both

query

and

metadata

traffic

though

there

is

no

synchronization

between

its

reception

of

a

query

and

its

-LRB-

collection

-RRB-

metadata

retrieval

Although

there

may

be

several

intermediaries

on

the

network

simultaneously

in

this

class

of

models

they

do

not

communicate

with

each

other

A

query

generator

must

decide

which

intermediary

to

use

for

a

given

query

though

by

definition

no

method

is

provided

for

assisting

in

this

decision

This

uncertainty

affects

estimates

of

query

network

traffic

as

will

be

discussed

in

Section

Figure

Brute-Force

Model

Query

Users

Sources

Intermediary

Intermediary

The

simpler

of

the

two

models

described

in

this

class

the

BF

Model

is

the

system

used

by

most

if

not

all

current

WWW

index

sites

and

is

depicted

in

Figure

In

this

model

an

intermediary

performs

metadata

extraction

by

gathering

all

documents

available

at

all

sources

Current

implementations

extract

only

limited

document

metadata

to

reduce

storage

space

and

no

collection

metadata

In

principle

collection

metadata

could

be

extracted

also

This

process

is

driven

completely

by

the

intermediary

the

sources

have

no

control

over

the

frequency

of

metadata

collection

nor

over

the

type

of

metadata

extracted

Queries

are

sent

to

the

intermediary

which

uses

its

pre-collected

metadata

to

determine

which

of

the

documents

it

has

analyzed

are

relevant

and

then

returns

a

-LRB-

possibly

ranked

-RRB-

result

set

to

the

query

generator

Queries

are

not

propagated

to

the

sources

and

results

are

not

able

to

include

information

about

documents

which

have

not

been

analyzed

directly

by

the

intermediary

A

fairly

new

approach

being

discussed

among

some

of

the

major

WWW

index

vendors

is

the

STARTS

Model

-LSB-

-RSB-

shown

in

Figure

on

the

following

page

In

this

model

built

on

a

gGlOSS

-LSB-

-RSB-

framework

each

source

performs

its

own

-LRB-

collection

-RRB-

metadata

extraction

The

intermediary

then

gathers

this

metadata

rather

than

extracting

its

own

In

principle

this

procedure

allows

off-line

documents

to

be

included

since

the

intermediary

is

not

required

to

have

direct

access

to

documents

Even

though

the

intermediary

retrieves

the

metadata

in

principle

this

model

allows

the

source

to

regulate

its

update

frequency

by

including

an

expiration

date

field

in

the

metadata

Although

non-text

metadata

-LRB-

see

next

section

-RRB-

is

theoretically

possible

the

default

metadata

structure

is

based

on

lists

of

terms

which

appear

in

text

document

collections

Queries

in

the

STARTS

STARTS

Workshop

occurred

at

Stanford

University

on

August

Figure

STARTS

Model

Query

Users

Sources

Intermediary

Intermediary

Model

are

of

a

keyword

matching

nature

although

the

design

allows

for

extensions

to

this

query

structure

When

an

intermediary

receives

a

query

it

analyzes

its

pre-collected

metadata

and

chooses

a

set

of

relevant

sources

The

query

is

then

propagated

to

these

sources

in

a

standard

format

and

they

return

the

results

-LRB-

document

metadata

retrieval

-RRB-

also

in

a

standard

format

back

to

the

intermediary

The

intermediary

is

then

responsible

for

merging

the

results

from

all

the

queried

sources

and

passing

a

single

ranked

list

of

results

back

to

the

query

generator

The

major

differences

that

the

STARTS

Model

has

from

the

BF

Model

is

that

-RRB-

it

allows

-LRB-

actually

requires

-RRB-

each

source

to

extract

its

own

metadata

and

-RRB-

there

are

standard

formats

for

information

exchange

-LRB-

metadata

queries

and

results

-RRB-

The

STARTS

Model

does

not

require

that

an

intermediary

store

document

metadata

as

does

the

BF

Model

Instead

it

retrieves

document

metadata

at

query

time

including

for

example

an

abstract

where

applicable

Therefore

even

though

the

query

result

size

in

the

STARTS

Model

is

perhaps

roughly

the

same

as

in

the

BF

Model

the

query

results

are

likely

to

be

much

more

descriptive

about

the

fewer

documents

returned

As

a

result

we

expect

that

substantially

fewer

documents

in

the

STARTS

Model

than

in

the

BF

Model

are

likely

to

be

checked

for

relevance

by

having

the

query

generator

fetch

the

actual

document

and

that

the

of

the

query

result

will

be

much

higher

Multiple

Intermediaries

Within

Class

the

Multiple

Intermediaries

models

we

define

three

extensions

to

standard

metadata

and

then

bring

them

together

in

a

single

design

Pharos

The

first

extension

is

hierarchical

metadata

which

is

the

fraction

of

returned

documents

that

are

relevant

to

a

query

Figure

Pharos

Model

Query

Users

Sources

-LRB-

Local

-RRB-

Servers

Mid-Level

History

U.S.A.

China

Servers

-LRB-

Remote

-RRB-

High-Level

means

that

the

metadata

is

organized

in

a

tree-like

structure

Information

at

a

parent

node

in

the

tree

contains

some

form

of

summary

of

the

information

of

all

its

children

though

possibly

with

less

detail

The

second

extension

is

that

of

shared

metadata

Sharing

metadata

means

that

intermediaries

can

pass

metadata

between

themselves

in

an

organized

manner

Thus

depending

on

the

architecture

an

intermediary

can

collect

specific

metadata

about

many

sources

other

systems

can

then

retrieve

that

specialized

information

if

and

when

they

need

it

without

having

to

communicate

with

the

separate

sources

directly

The

last

extension

is

that

of

non-text

metadata

which

describes

non-text

aspects

of

documents

and

collections

For

example

documents

that

include

images

sounds

and

maps

may

not

be

best

described

by

words

or

terms

associated

with

them

Many

of

these

non-text

documents

as

well

as

many

text

documents

are

also

characterized

by

for

example

image

feature-vectors

geographical

coordinates

temporal

information

etc.

Given

these

metadata

extensions

we

describe

a

new

model

Pharos

shown

in

Figure

Pharos

is

described

in

detail

in

-LSB-

-RSB-

Pharos

is

designed

to

select

a

small

set

of

-LRB-

highly

relevant

-RRB-

sources

from

among

a

very

large

set

While

Pharos

separates

the

query

activity

-LRB-

query

propagation

and

result

retrieval

-RRB-

from

the

metadata

propagation

as

in

the

previous

models

it

differs

from

them

in

that

it

synchronizes

this

activity

with

the

metadata

retrieval

Metadata

is

extracted

by

the

source

as

in

the

STARTS

Model

but

here

the

metadata

is

composed

of

a

small

amount

of

non-hierarchical

information

and

a

relatively

large

amount

of

hierarchical

information

defined

by

one

or

more

information

taxonomies

The

metadata

is

then

split

into

two

levels

the

high-level

metadata

consists

of

the

non-hierarchical

information

and

the

top

portion

of

the

hierarchical

metadata

and

the

mid-level

metadata

consists

of

the

lower

portion

of

the

hierarchi

cal

metadata

There

are

two

types

of

intermediaries

high-level

servers

and

mid-level

servers

These

levels

correspond

to

the

levels

in

the

metadata

Both

types

of

intermediaries

contain

information

about

all

sources

although

multi-source

metadata

records

are

also

possible

in

this

model

The

high-level

servers

are

massively

replicated

identical

copies

and

designed

to

reside

locally

similar

in

nature

to

USENET

news

servers

The

mid-level

servers

however

are

only

sparsely

replicated

-LRB-

if

at

all

-RRB-

and

each

contains

unique

topic-based

metadata

about

all

sources

-LRB-

again

multi-source

records

are

possible

-RRB-

A

source

propagates

its

metadata

as

needed

to

the

relevant

intermediaries

Because

Pharos

works

with

any

type

of

information

taxonomy

a

source

can

extract

non-text

information

about

its

collection

and

incorporate

it

as

a

standard

component

of

its

metadata

When

a

query

is

generated

there

is

an

iterative

series

of

metadata

retrieval

The

query

is

first

matched

against

the

local

high-level

metadata

and

the

sources

are

filtered

to

a

greatly

reduced

subset

-LRB-

from

say

potential

sources

available

on

the

network

to

say

relevant

sources

for

the

specific

query

-RRB-

The

query

generator

then

requests

more

metadata

about

this

subset

from

the

relevant

topic-based

mid-level

servers

situated

remotely

on

the

network

These

servers

supply

greater

detailed

information

about

this

filtered

list

of

sources

thus

further

reducing

the

number

of

potentially

relevant

sources

-LRB-

say

from

to

-RRB-

Once

a

small

set

of

sources

is

selected

Pharos

assumes

that

the

sources

are

then

queried

directly

that

is

there

are

no

query

intermediaries

in

Pharos

as

there

are

in

the

STARTS

Model

However

the

equivalent

functionality

for

query

result

merging

could

be

built

into

the

query

generator

's

user

interface

There

is

nothing

about

having

multiple

intermediaries

that

precludes

such

activity

While

Pharos

does

not

directly

address

this

issue

keyword

text-based

results

can

be

merged

using

the

STARTS

Model

methodology

For

other

types

of

documents

result

merging

remains

a

more

difficult

problem

Model

Comparison

Some

of

the

criteria

under

which

we

might

compare

these

different

models

and

classes

especially

from

an

implementation

point

of

view

are

scalability

flexibility

design

complexity

and

evaluation

methodology

Increases

in

design

complexity

should

be

warranted

by

corresponding

increases

in

either

scalabil

servers

are

considered

to

be

the

sources

themselves

ity

or

flexibility

The

performance

of

these

models

depends

on

factors

such

as

the

number

of

queries

the

number

of

sources

and

the

size

of

a

collection

We

attempt

to

estimate

several

important

characteristics

of

each

model

as

well

as

the

three

classes

in

general

We

have

selected

several

model

parameters

and

estimated

their

corresponding

order

of

magnitude

values

We

then

use

these

parameters

to

derive

equations

for

network

traffic

storage

requirements

and

the

number

of

accesses

Next

we

use

the

sample

values

to

check

that

these

equations

are

realistic

and

to

identify

potential

problems

Finally

we

discuss

how

these

characteristics

are

likely

to

change

with

the

future

growth

of

the

Internet

Model

Parameters

Table

on

the

following

page

lists

all

model

parameters

used

in

our

derivations

All

values

in

this

and

the

following

tables

are

written

in

-LRB-

x

-RRB-

We

first

list

the

values

used

to

describe

quantities

such

as

the

number

of

documents

in

a

collection

and

the

number

of

sources

Next

we

list

the

values

used

to

describe

in

bytes

quantities

such

as

the

size

of

a

document

and

the

size

of

a

query

result

This

table

includes

estimates

of

what

might

be

considered

a

parameter

's

small

large

and

typical

order

of

magnitude

For

example

ND

the

number

of

documents

in

a

single

collection

is

estimated

to

range

from

to

and

a

typical

collection

is

estimated

to

contain

documents

Several

of

these

parameters

are

specialized

and

need

some

further

explanation

In

Pharos

queries

are

based

on

one

or

more

topics

chosen

from

several

information

classifications

and

the

number

of

topics

chosen

per

query

NQI

affects

how

many

mid-level

intermediaries

are

involved

in

handling

a

query

one

intermediary

per

topic

We

differentiate

SDT

the

size

of

an

ASCII

text

document

from

SD

the

size

of

a

non-specific

document

because

architectures

such

as

the

BF

and

STARTS

Model

are

designed

for

text

documents

ASCII

documents

tend

to

be

smaller

than

for

example

images

All

sizes

in

Table

after

SR

are

related

to

metadata

It

is

difficult

to

estimate

these

sizes

as

an

example

we

derive

two

of

them

For

text-based

vector

analysis

-LSB-

-RSB-

the

size

SCTS

of

the

full

collection

metadata

at

a

source

is

a

function

of

the

number

of

unique

words

t

in

its

collection

which

is

generally

a

function

of

the

total

number

of

words

in

the

collection

N

For

t

f

-LRB-

N

-RRB-

clearly

f

-LRB-

N

-RRB-

is

greater

than

Annual

Report

of

the

Library

of

Congress

lists

approximately

million

items

in

its

collection

INSPEC

contains

well

over

a

million

articles

Symbol

Small

Large

Typical

Description

ND

Number

of

documents

in

a

single

collection

NRD

Number

of

relevant

documents

from

a

single

collection

NS

Number

of

sources

NBS

Number

of

best

sources

NRS

Number

of

relevant

sources

-LRB-

i.e.

sources

with

any

relevant

documents

-RRB-

assumed

to

be

NS

NQG

Number

of

query

generators

NII

Number

of

independent

intermediaries

-LRB-

Class

-RRB-

NMLS

Number

of

unique

mid-level

servers

-LRB-

Pharos

-RRB-

NIM

Number

of

duplicate

mid-level

servers

per

topic

-LRB-

Pharos

-RRB-

NHLS

Number

of

high-level

servers

-LRB-

Pharos

-RRB-

NHLF

Number

of

sources

selected

by

high-level

filter

-LRB-

Pharos

-RRB-

NQI

Number

of

taxonomy

nodes

in

a

Pharos

query

SD

Size

of

a

non-specific

document

SDT

Size

of

a

text

-LRB-

ASCII

-RRB-

document

SC

Size

of

a

collection

of

non-specific

documents

ND

SD

SCT

Size

of

a

collection

of

text

-LRB-

ASCII

-RRB-

documents

ND

SDT

SQ

Size

of

a

query

SM

Size

of

a

query

result

-LRB-

returned

metadata

record

-RRB-

for

a

single

document

-LRB-

e.g.

-LSB-

title

author

publication

date

keywords

abstract

-RSB-

-RRB-

SR

Size

of

a

query

result

-LRB-

all

relevant

documents

-RRB-

from

a

single

source

NRD

SM

SCTS

Size

of

full

-LRB-

text

-RRB-

collection

metadata

at

a

source

-LRB-

STARTS

-RRB-

see

Equations

-LCB-

SCTI

Size

of

full

collection

metadata

for

all

sources

at

an

intermediary

-LRB-

STARTS

-RRB-

see

Equation

STN

Size

of

information

for

each

node

in

a

taxonomy

-LRB-

Pharos

-RRB-

SCPS

Size

of

full

collection

metadata

at

a

source

-LRB-

Pharos

-RRB-

STN

-LRB-

nodes/taxonomy

-RRB-

-LRB-

taxonomy/source

-RRB-

SHPS

Size

of

full

collection

high-level

metadata

at

a

source

-LRB-

Pharos

-RRB-

extracted

from

nodes

of

the

taxonomy

trees

SMPS

Size

of

full

collection

mid-level

metadata

at

a

source

-LRB-

Pharos

-RRB-

extracted

from

nodes

of

the

taxonomy

trees

SHLI

Size

of

metadata

at

a

high-level

intermediary

for

all

sources

-LRB-

Pharos

-RRB-

SHPS

NS

SMLI

Size

of

metadata

at

a

mid-level

intermediary

for

all

sources

-LRB-

Pharos

-RRB-

SMPS

NS

Table

Model

Parameters

-LRB-

all

values

are

-LRB-

x

-RRB-

sizes

are

in

bytes

-RRB-

log

-LRB-

N

-RRB-

and

less

than

N

Salton

-LSB-

-RSB-

gives

t

kN

for

constants

k

and

k

and

We

take

k

and

Next

we

must

estimate

N

as

a

function

of

SCT

the

size

of

a

collection

of

ASCII

text

documents

Salton

points

out

that

while

average

word

length

for

distinct

English

words

is

characters

the

average

word

length

in

ordinary

English

text

-LRB-

with

many

repeated

words

-RRB-

is

characters

Since

we

are

not

assuming

ordinary

English

text

necessarily

-LRB-

or

even

English

let

alone

text

-RRB-

and

we

want

to

allow

for

multi-word

terms

we

take

the

average

number

of

bytes

per

term

L

as

Finally

assuming

that

each

term

requires

at

least

three

numbers

in

the

we

derive

the

following

estimate

SCTS

-LRB-

L

-RRB-

t

-LRB-

-RRB-

ss

-LSB-

-LRB-

-LRB-

L

-RRB-

k

-RRB-

L

-RSB-

-LRB-

SCT

-RRB-

-LRB-

-RRB-

ss

-LRB-

SCT

-RRB-

-LRB-

-RRB-

with

t

k

N

and

N

SCT

L

An

intermediary

is

required

to

maintain

this

metadata

for

each

source

regardless

of

whether

the

terms

from

a

source

have

already

appeared

in

an

intermediary

's

global

list

of

unique

terms

Thus

SCT

I

ss

SCTS

NS

-LRB-

-RRB-

where

SCT

I

is

the

size

of

metadata

at

an

intermediary

and

NS

is

the

number

of

sources

from

which

the

intermediary

has

collected

information

Analysis

Estimates

for

several

characteristics

of

each

model

are

given

in

Estimate

Tables

-LCB-

where

all

expressions

are

derived

from

the

model

parameters

described

in

the

previous

section

Values

are

derived

by

applying

the

constant

typical

values

in

Table

to

the

expressions

in

each

Estimate

Table

Any

column

which

is

not

applicable

to

the

corresponding

model

is

marked

such

is

the

case

for

example

of

metadata

quantities

for

the

Simple

Model

which

has

no

metadata

This

is

different

from

columns

which

potentially

apply

to

a

model

but

for

which

the

particular

model

has

a

zero

quantity

marked

Table

on

the

next

page

gives

estimates

for

remote

network

traffic

generated

by

a

single

query

and

by

a

single

update

We

assume

that

network

traffic

is

sent

efficiently

-LRB-

e.g.

text

compression

is

-RRB-

but

this

does

not

greatly

affect

order-of-magnitude

estimates

metadata

may

require

for

example

the

number

of

documents

in

which

each

term

occurs

the

total

number

of

occurrences

of

the

term

in

the

entire

collection

fields

in

which

the

word

occurs

etc.

We

specify

remote

traffic

because

we

do

not

include

in

these

estimates

any

traffic

assumed

to

travel

between

machines

within

the

same

local

area

This

excludes

for

example

traffic

in

the

Pharos

Model

between

a

query

generator

and

its

corresponding

local

high-level

metadata

server

Table

gives

estimates

for

long-term

metadata

storage

requirements

Metadata

storage

is

required

at

the

query

generator

in

the

Local

Model

only

However

Pharos

stores

some

metadata

at

high-level

servers

within

a

query

generator

's

local

area

there

are

a

total

of

NHLS

such

servers

and

this

storage

requirement

is

listed

as

residing

at

the

query

generator

In

our

numerical

estimates

we

assume

that

each

Class

intermediary

retrieve

metadata

from

every

source

In

reality

some

intermediaries

collect

more

than

others

and

as

a

result

many

queries

are

propagated

to

multiple

intermediaries

by

users

trying

to

find

as

good

information

as

is

practical

The

amount

of

duplicate

query

generation

is

difficult

to

estimate

and

we

do

not

attempt

to

do

so

Tables

and

give

the

total

number

of

accesses

generated

assuming

that

each

query

generator

issues

a

single

query

and

each

source

issues

a

single

update

respectively

Thus

the

total

number

of

queries

and

updates

are

determined

by

the

number

of

query

generators

NQG

and

sources

NS

respectively

As

an

example

we

derive

the

Single

Query

Expression

for

the

STARTS

Model

in

Table

The

query

of

size

SQ

is

first

propagated

to

the

intermediary

which

matches

it

against

its

metadata

and

selects

the

best

sources

to

query

directly

These

NBS

sources

are

then

handed

the

query

So

far

the

query

has

generated

SQ

-LRB-

NBS

-RRB-

bytes

of

remote

network

traffic

The

next

action

is

the

result

retrieval

process

Each

queried

source

will

send

back

its

own

query

result

assumed

to

be

of

size

SR.

The

intermediary

in

STARTS

will

perform

only

limited

result

merging

rank

merging

but

no

duplicate

detection

Thus

the

same

amount

of

traffic

sent

to

the

intermediary

is

bundled

together

and

re-sent

to

the

query

generator

for

a

total

result

retrieval

of

-LRB-

SR?NBS

-RRB-

bytes

While

one

might

consider

the

factor

of

to

be

superfluous

it

is

negligible

in

the

order-of-magnitude

estimate

anyway

Thus

the

total

amount

of

traffic

generated

by

a

single

query

in

the

STARTS

Model

is

-LRB-

SQ

-LRB-

NBS

-RRB-

-RRB-

-LRB-

SR

NBS

-RRB-

bytes

as

shown

in

the

table

In

fact

in

all

the

models

traffic

caused

by

queries

is

dominated

by

multiplicative

factors

of

SR

with

the

best

being

SR

NBS

in

STARTS

and

Pharos

Single

Query

Single

Update

Typical

Typical

Model

Expression

Value

Expression

Value

Comments

Simple

SQ

SR

No

metadata

single-source

query

only

Remote

NS

-LRB-

SQ

SR

-RRB-

No

metadata

Local

NBS

-LRB-

SQ

SR

-RRB-

NQG

SCTS

BF

SQ

-LRB-

NRS

SR

-RRB-

NII

SCT

Query

result

assumes

no

duplicate

detection

STARTS

-LRB-

SQ

-LRB-

NBS

-RRB-

-RRB-

-LRB-

SR

NBS

-RRB-

NII

SCTS

Pharos

-LRB-

NHLF

NQI

STN

-RRB-

-LRB-

NBS

-LRB-

SQ

SR

-RRB-

-RRB-

-LRB-

NHLS

SHPS

-RRB-

-LRB-

NIM

SMPS

-RRB-

Table

Estimates

of

Remote

Network

Traffic

-LRB-

in

bytes

all

values

are

-LRB-

x

-RRB-

-RRB-

Combined

Typical

Values

Query

Combined

Average

per

Model

Generators

Intermediaries

Storage

Site

Total

Comments

Simple

No

metadata

single-source

query

only

Remote

No

metadata

Local

NQG

SCTI

Assumes

STARTS-style

metadata

-LRB-

i.e.

text

only

-RRB-

BF

NII

-LRB-

ND

NS

SM

-RRB-

Assumes

smaller

SM

STARTS

NII

SCTI

Pharos

NHLS

SHLI

NIM

SMLI

Table

Estimates

of

Metadata

Storage

Requirements

-LRB-

in

bytes

all

values

are

-LRB-

x

-RRB-

-RRB-

Combined

Typical

Values

-LRB-

Remote

-RRB-

Combined

Average

per

Model

Intermediaries

Sources

Intermediary

Source

Total

Comments

Simple

NQG

Single-source

query

only

Remote

NQG

NS

Local

NQG

NBS

BF

NQG

-LRB-

NQG

NRS

NRD

-RRB-

Assumes

relevance

check

of

top

documents

STARTS

NQG

NQG

NBS

Pharos

NQI

NQG

NQG

NBS

Table

Estimates

of

Query

Accesses

-LRB-

one

query

per

generator

all

values

are

-LRB-

x

-RRB-

-RRB-

Combined

Typical

Values

-LRB-

Remote

-RRB-

Average

per

Model

Intermediaries

Intermediary

Total

Comments

Simple

No

metadata

Remote

No

metadata

Local

NQG

NS

Query

generator

is

intermediary

BF

ND

NII

NS

STARTS

NII

NS

Pharos

-LRB-

NHLS

NS

-RRB-

-LRB-

NMLS

NIM

NS

-RRB-

Table

Estimates

of

Update

Accesses

-LRB-

one

update

per

source

all

values

are

-LRB-

x

-RRB-

-RRB-

Scalability

Estimates

We

first

discuss

our

estimates

assuming

the

parameters

take

on

values

that

might

be

currently

considered

typical

as

was

listed

in

Table

After

that

we

increase

three

key

values

and

re-compare

some

of

the

models

Typical

Current

Values

We

intuitively

expect

that

Class

should

show

the

greatest

scalability

problems

Each

model

in

this

class

breaks

down

in

a

different

way

Clearly

the

Simple

Model

is

not

feasible

for

many

technical

-LRB-

let

alone

political

-RRB-

reasons

If

the

characteristics

we

are

estimating

are

the

most

relevant

ones

we

expect

that

some

of

these

reasons

will

show

up

in

one

or

more

of

the

Estimate

Tables

-LCB-

The

Simple

Model

does

not

seem

to

show

a

problem

in

the

amount

of

remote

network

traffic

shown

in

Table

This

is

unrealistic

because

for

example

if

we

assume

that

all

documents

on

the

network

were

placed

at

one

monolithic

source

the

query

result

size

would

likely

be

substantially

larger

than

for

more

typical

sources

In

Table

though

we

assume

the

same

result

size

for

all

queries

Table

however

shows

that

sources

in

both

the

Simple

and

Remote

models

receive

orders

of

magnitude

more

query

accesses

than

in

the

other

models

because

each

query

is

propagated

to

every

source

The

last

model

in

this

class

the

Local

Model

shows

problems

with

for

example

the

amount

of

traffic

needed

to

perform

a

single

update

The

model

also

does

poorly

in

terms

of

storage

and

total

update

accesses

Total

storage

is

a

problem

because

the

model

must

assume

that

each

query

generator

has

its

own

copy

of

all

metadata

in

order

to

be

in

Class

The

models

discussed

in

Class

are

the

closest

to

present

working

systems

It

is

important

to

distinguish

behavior

evidenced

in

a

particular

model

from

behavior

inherent

in

the

class

Both

BF

and

STARTS

for

example

have

high

storage

require

ments

as

seen

in

Table

However

this

is

a

result

of

the

fact

that

the

size

of

the

collection

metadata

used

in

these

models

is

a

function

of

the

size

of

the

collection

Pharos

uses

a

classification-based

metadata

structure

whose

size

is

independent

of

the

collection

size

and

thus

yields

smaller

storage

values

Such

a

metadata

structure

is

not

inconsistent

with

Class

The

BF

Model

is

particularly

problematic

in

the

number

of

update

accesses

shown

in

Table

This

is

because

the

model

requires

that

the

intermediaries

separately

fetch

each

document

from

a

source

rather

than

fetching

a

single

albeit

large

collection

metadata

record

as

in

the

STARTS

Model

STARTS

achieves

the

reasonable

goal

that

each

intermediary

is

accessed

no

more

than

once

per

update

If

the

BF

Model

does

not

rank

the

documents

in

its

result

set

users

must

fetch

many

documents

in

order

to

determine

their

relevancy

Even

with

a

ranked

list

however

a

lack

of

document

duplicate

detection

would

require

needless

document

fetches

As

mentioned

in

Section

we

expect

that

STARTS

provides

better

precision

and

more

document

metadata

than

BF

and

therefore

results

in

even

less

document

fetches

We

account

for

this

in

our

estimates

by

assuming

that

the

top

of

the

documents

returned

by

an

intermediary

in

the

BF

Model

is

checked

for

relevance

and

therefore

included

in

the

query

access

count

in

Table

One

of

the

advantages

of

Class

in

general

is

that

it

allows

intermediary

metadata

to

be

less

than

linear

with

respect

to

the

number

of

sources

this

is

not

possible

in

Class

or

Several

resource

discovery

models

-LSB-

-RSB-

including

Pharos

allow

intermediaries

to

collect

information

from

other

intermediaries

including

metadata

describing

a

collection

of

collections

rather

than

just

a

collection

of

documents

An

appropriate

network

hierarchy

of

intermediaries

could

arbitrarily

reduce

the

size

of

the

metadata

at

the

cost

of

loss

of

discrimination

between

different

sources

Furthermore

grouping

sources

behind

par

Figure

Scalability

Issues

ffl

Increasing

Degrades

Performance

-LCB-

Number

of

Users

-LCB-

Number

of

Sources

-LCB-

Data

Volume

ffl

Increasing

Improves

Performance

-LCB-

Network

Bandwidth

-LCB-

Storage

Capacity

-LCB-

Processing

Power

ffl

Constant

Design

Criteria

-LCB-

Query

Response

Time

-LCB-

Query

Result

Size

ticular

intermediaries

reduces

the

number

of

update

accesses

per

intermediary

at

the

cost

of

maintaining

more

of

them

The

only

Class

model

we

have

included

is

Pharos

Pharos

performs

well

in

the

estimates

for

several

reasons

Network

traffic

is

minimized

for

updates

for

the

same

reason

that

storage

is

minimized

as

stated

the

metadata

size

is

independent

of

the

collection

sizes

Pharos

stratifies

metadata

within

a

collection

Because

of

this

layering

it

can

send

pieces

of

metadata

selectively

to

different

intermediaries

this

allows

a

large

number

of

sources

to

be

coarsely

described

in

a

compact

way

This

type

of

metadata

splitting

is

possible

only

if

the

information

itself

is

hierarchically

structured

It

can

be

utilized

only

within

Class

since

its

benefit

arises

from

having

intermediaries

hierarchically

arranged

in

a

way

that

matches

the

hierarchy

within

which

a

collection

is

classified

Typical

Future

Values

We

have

chosen

typical

values

for

our

estimates

based

more

or

less

on

the

current

-LRB-

-RRB-

Internet

environment

It

is

generally

expected

that

several

parameters

will

experience

fairly

rapid

growth

over

the

next

few

years

some

degrading

system

performance

and

others

improving

it

as

shown

in

Figure

However

we

expect

that

factors

relating

to

computerhuman

interaction

can

not

drastically

change

if

a

resource

discovery

system

is

to

continue

to

be

useful

In

particular

query

response

time

and

the

result

size

need

to

remain

fairly

constant

While

we

do

not

attempt

here

to

directly

model

query

response

time

it

is

likely

to

be

greatly

affected

by

factors

such

as

the

network

traffic

and

number

of

accesses

required

by

each

model

The

query

result

size

is

directly

related

to

the

amount

of

network

traffic

generated

by

a

single

query

as

shown

in

Table

Therefore

it

is

important

to

recognize

which

models

are

likely

to

experience

future

scalability

problems

with

these

values

based

on

parameter

dependencies

We

ignore

the

Class

models

in

this

section

since

they

were

already

problematic

with

the

previous

parameter

values

First

we

change

the

three

model

parameters

which

most

closely

relate

to

the

number

of

users

the

number

of

sources

and

the

data

volume

NQG

NS

and

ND

respectively

We

increase

the

first

two

by

two

orders

of

magnitude

and

the

third

by

one

yielding

approximately

query

generators

sources

and

an

average

of

documents

per

collection

These

changes

require

changes

in

seven

other

parameters

NRS

SC

SCT

SCTS

SCT

I

SHLI

and

SMLI

We

now

allow

for

two

cases

of

the

models

to

handle

these

increases

In

the

first

Case

we

keep

the

number

of

intermediaries

the

same

in

the

second

Case

we

increase

them

Increasing

the

number

of

intermediaries

in

Class

is

a

simple

matter

of

increasing

NII

which

we

do

by

two

orders

of

magnitude

For

Pharos

we

have

two

coordinated

sets

of

intermediaries

The

mid-level

servers

are

the

most

similar

to

the

intermediaries

in

the

other

models

and

therefore

for

Case

we

increase

NIM

by

two

orders

of

magnitude

The

high-level

servers

however

act

more

like

USENET

news

servers

and

are

expected

to

serve

a

local

area

network

Moreover

any

replication

of

this

type

of

service

within

a

local

area

is

irrelevant

to

our

model

which

is

looking

only

at

remote

traffic

Therefore

we

increase

the

number

of

high-level

servers

NHLS

only

one

order

of

magnitude

that

is

we

roughly

expect

that

the

increase

in

the

number

of

high-level

servers

grows

approximately

as

the

square

root

of

the

growth

of

the

number

of

users

The

results

are

shown

in

Table

This

table

shows

the

re-computed

estimates

of

all

values

in

Tables

-LCB-

as

a

result

of

the

increases

in

NQG

NS

and

ND

For

each

model

the

column

labeled

Original

shows

the

values

from

the

original

tables

The

column

labeled

Case

shows

the

Case

values

where

the

number

of

intermediaries

is

left

as

it

was

The

column

labeled

Case

shows

the

values

for

which

the

number

of

intermediaries

was

increased

as

previously

discussed

Certain

scalability

problems

become

evident

in

this

table

For

example

because

the

metadata

in

BF

and

STARTS

grows

with

the

size

of

the

collection

as

well

as

with

the

number

of

intermediaries

the

amount

of

traffic

generated

by

a

single

update

grows

by

as

much

as

three

orders

of

magnitude

In

Pharos

the

increase

is

dominated

by

the

extra

number

of

high

BF

STARTS

Pharos

New

Values

New

Values

New

Values

Estimate

Original

Case

Case

Original

Case

Case

Original

Case

Case

Network

Traffic

-LRB-

Table

-RRB-

Query

Update

Metadata

Storage

-LRB-

Table

-RRB-

Avg

Total

Query

Accesses

-LRB-

Table

-RRB-

Avg

Int

Avg

Src

Total

Update

Accesses

-LRB-

Table

-RRB-

Avg

Int

Total

Table

Estimates

for

Large

Parameters

-LRB-

all

values

are

-LRB-

x

-RRB-

-RRB-

level

servers

and

so

the

result

is

much

smaller

only

one

order

of

magnitude

Pharos

also

scales

well

with

respect

to

storage

this

is

not

only

because

as

before

the

metadata

size

per

source

is

constant

but

also

because

only

a

portion

of

it

is

sent

to

any

particular

intermediary

Finally

STARTS

scales

the

best

for

the

number

of

update

accesses

since

it

requires

only

one

access

between

each

source

and

intermediary

and

has

relatively

few

intermediaries

Conclusion

We

have

presented

a

classification

of

network

architectures

for

locating

information

sources

These

models

have

been

grouped

into

three

broad

classes

Direct

Single

Intermediary

and

Multiple

Intermediaries

We

discussed

the

relevant

parameters

for

these

models

derived

estimates

for

several

of

their

characteristics

and

compared

them

based

on

these

estimates

Each

class

has

been

shown

to

have

certain

scalability

characteristics

for

all

its

models

We

believe

that

each

increase

in

design

complexity

among

the

classes

and

models

is

warranted

by

the

corresponding

increase

in

scalability

In

general

the

estimates

we

derived

indicate

the

need

for

collection

metadata

whose

size

is

not

a

function

of

the

size

of

the

collection

Moreover

utilizing

multiple

intermediary

models

that

rely

on

metadata

summaries

can

avoid

a

linear

growth

of

metadata

size

with

respect

to

the

number

of

sources

Limiting

the

growth

of

collection

metadata

in

these

two

ways

should

more

easily

accommodate

the

expected

expansion

of

the

Internet

More

work

needs

to

be

done

in

better

establishing

which

scalability

factors

relate

to

the

general

classes

and

in

further

classifying

the

architectures

in

more

detail

including

for

example

metadata

structure

In

the

future

we

hope

to

compare

these

models

in

terms

of

flexibility

such

as

query

methodology

data

diversity

source

selection

criteria

and

so

on

We

are

currently

working

on

simulating

these

systems

in

order

to

more

realistically

compare

them

This

work

includes

developing

an

evaluation

methodology

which

is

consistent

between

all

models

given

the

different

query

methodologies

and

results

between

them

References

-LSB-

-RSB-

AltaVista

-LSB-

-RSB-

Berry

M.W.

Dumais

S.

Using

Linear

Algebra

for

Intelligent

Information

Retrieval

SIAM

Review

December

and

-LSB-

-RSB-

Bowman

C.M.

Danzig

P.B.

Hardy

D.R.

Manber

U.

and

others

The

Harvest

Information

Discovery

and

Access

System

Proceedings

of

the

Second

International

World-Wide

Web

Conference

pp.

Chicago

Illinois

October

and

-LSB-

-RSB-

ConQuest

Software

Inc.

ConQuest

Guide

for

Developers

-LRB-

Proprietary

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Columbia

MD

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chitecture

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H.

Paepcke

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G.

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Gifford

D.

Content

Routing

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Distributed

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the

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Extending

Database

Technology

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Yahoo

FAQ

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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O 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