Discovering

and

Ranking

Web

Services

with

BASIL

A

Personalized

Approach

with

Biased

Focus

In

this

paper

we

present

a

personalized

web

service

discovery

and

ranking

technique

for

discovering

and

ranking

relevant

data-intensive

web

services

Our

first

prototype

called

BASIL

supports

a

personalized

view

of

data-intensive

web

services

through

source-biased

focus

BASIL

provides

service

discovery

and

ranking

through

source-biased

probing

and

source-biased

relevance

metrics

Concretely

the

BASIL

approach

has

three

unique

features

-LRB-

-RRB-

It

is

able

to

determine

in

very

few

interactions

whether

a

target

service

is

relevant

to

the

given

source

service

by

probing

the

target

with

very

precise

probes

-LRB-

-RRB-

It

can

evaluate

and

rank

the

relevant

services

discovered

based

on

a

set

of

source-biased

relevance

metrics

and

-LRB-

-RRB-

It

can

identify

interesting

types

of

relationships

for

each

source

service

with

respect

to

other

discovered

services

which

can

be

used

as

value-added

metadata

for

each

service

We

also

introduce

a

performance

optimization

technique

called

source-biased

probing

with

focal

terms

to

further

improve

the

effectiveness

of

the

basic

source-biased

service

discovery

algorithm

The

paper

concludes

with

a

set

of

initial

experiments

showing

the

effectiveness

of

the

BASIL

system

H.

-LSB-

Online

Information

Services

-RSB-

Web-based

services

Algorithms

Experimentation

Most

web

services

today

are

web-enabled

applications

that

can

be

accessed

and

invoked

using

a

messaging

system

typically

relying

on

standards

such

as

XML

WSDL

and

SOAP

-LSB-

-RSB-

Many

companies

have

latched

onto

the

web

services

mantra

including

major

software

developers

business

exchanges

eCom-merce

sites

and

search

engines

-LSB-

-RSB-

A

large

and

growing

portion

of

the

web

services

today

can

be

categorized

as

data-intensive

web

services

This

research

is

partially

supported

by

NSF

CNS

CCR

NSF

ITR

DoE

SciDAC

DARPA

CERCS

Research

Grant

IBM

Faculty

Award

IBM

SUR

grant

HP

Equipment

Grant

and

LLNL

LDRD

Permission

to

make

digital

or

hard

copies

of

all

or

part

of

this

work

for

personal

or

classroom

use

is

granted

without

fee

provided

that

copies

are

not

made

or

distributed

for

profit

or

commercial

advantage

and

that

copies

bear

this

notice

and

the

full

citation

on

the

first

page

To

copy

otherwise

to

republish

to

post

on

servers

or

to

redistribute

to

lists

requires

prior

specific

permission

and/or

a

fee

ICSOC

November

New

York

New

York

USA

Copyright

ACM

Data-intensive

web

services

provide

access

to

huge

and

growing

data

stores

and

support

tools

for

searching

manipulating

and

analyzing

those

data

stores

For

example

both

Amazon

-LSB-

-RSB-

and

Google

-LSB-

-RSB-

now

provide

XML

and

SOAP-based

web

service

interfaces

to

their

underlying

data

repositories

with

support

for

advanced

search

operators

over

collectively

billions

of

items

In

the

life

sciences

domain

many

bioinformatics

services

are

transitioning

from

human-in-the-loop

web

interfaces

to

the

web

services

model

-LSB-

-RSB-

providing

direct

access

to

unprecedented

amounts

of

raw

data

and

specialized

research

tools

to

provide

high-level

analysis

and

search

over

these

data

services

With

the

increasing

visibility

of

web

services

and

the

Service-Oriented

Computing

paradigm

-LSB-

-RSB-

there

is

a

growing

need

for

efficient

mechanisms

for

discovering

and

ranking

services

Effective

mechanisms

for

web

service

discovery

and

ranking

are

critical

for

organizations

to

take

advantage

of

the

tremendous

opportunities

offered

by

web

services

to

engage

in

business

collaborations

and

service

compositions

to

identify

potential

service

partners

and

to

understand

service

competitors

and

increase

the

competitive

edge

of

their

service

offerings

Current

web

service

discovery

techniques

can

be

classified

into

two

types

categorization-based

discovery

and

personalized

relevance-based

discovery

The

former

discovers

web

services

by

clustering

and

categorizing

a

collection

of

web

services

into

different

groups

based

on

certain

common

properties

of

the

services

Most

of

the

existing

UDDI

-LSB-

-RSB-

registry-based

service

discovery

methods

are

of

this

type

They

typically

discover

relevant

services

by

querying

metadata

maintained

in

the

common

registries

-LRB-

like

the

ones

offered

by

Microsoft

-LSB-

-RSB-

and

IBM

-LSB-

-RSB-

-RRB-

A

typical

question

is

Which

bioinformatics

web

services

offer

BLAST

capability

or

Which

commercial

services

offer

on-line

auctions

The

second

type

of

discovery

mechanisms

uses

personalized

relevance

reasoning

and

support

questions

such

as

Which

services

offer

the

same

type

of

content

as

NCBI

and

Find

the

top-ten

web

services

that

offer

more

coverage

than

the

BLAST

services

at

NCBI

These

two

types

of

service

discovery

techniques

offer

different

focus

and

complementary

capabilities

Consider

the

following

examples

A

bioinformatics

researcher

may

be

interested

in

finding

all

services

similar

to

NCBI

's

BLAST

service

for

searching

DNA

and

protein

sequence

libraries

-LSB-

-RSB-

Current

service

registries

may

provide

pointers

to

other

BLAST

services

but

they

do

not

describe

how

these

other

sites

relate

specifically

to

NCBI

's

BLAST

service

Which

services

provide

the

most

similar

coverage

with

respect

to

NCBI

-LRB-

e.g.

of

similar

proteins

or

organisms

-RRB-

Which

services

are

complementary

in

their

coverage

-LRB-

e.g.

of

other

sequence

libraries

-RRB-

How

best

should

the

BLAST

services

be

ranked

relative

to

the

NCBI

service

A

health

science

researcher

familiar

with

the

PubMed

ical

literature

service

may

be

interested

in

discovering

other

related

medical

digital

library

services

Given

his

prior

knowledge

with

PubMed

he

may

want

to

ask

certain

personalized

-LRB-

source-biased

-RRB-

discovery

requests

which

are

not

supported

by

the

conventional

service-registry-based

discovery

model

Examples

include

Find

and

rank

all

PubMed-related

medical

literature

sites

Which

services

have

more

general

coverage

than

PubMed

Which

medical

literature

services

are

more

specialized

than

PubMed

These

examples

highlight

two

fundamental

differences

between

the

categorization-based

and

the

personalization-based

discovery

model

-LRB-

-RRB-

Categorization

of

web

services

based

on

general

descriptions

maintained

at

the

service

registries

are

insufficient

and

inadequate

when

a

user

is

interested

in

discovery

based

on

a

particular

service

-LRB-

or

a

set

of

services

-RRB-

with

which

she

has

prior

experience

or

knowledge

-LRB-

e.g.

NCBI

BLAST

or

PubMed

-RRB-

and

-LRB-

-RRB-

There

is

a

need

for

service

ranking

metrics

that

capture

the

relative

scope

and

coverage

of

the

data

offered

with

respect

to

a

previously

known

service

Although

these

two

types

of

discovery

mechanisms

are

complementary

most

existing

proposals

on

web

service

discovery

fall

into

the

first

type

Surprisingly

there

are

to

our

knowledge

no

effective

means

to

provide

such

personalized

and

biased

discovery

and

ranking

support

without

relying

on

significant

human

intervention

In

this

paper

we

present

algorithms

for

discovering

and

ranking

relevant

data-intensive

web

services

Our

first

prototype

called

BASIL

supports

a

personalized

view

of

web

services

through

source-biased

probing

and

source-biased

relevance

detection

and

ranking

metrics

Concretely

our

approach

is

capable

of

discovering

and

ranking

web

services

by

focusing

on

the

nature

and

degree

of

the

data

relevance

of

the

source

service

to

others

Given

a

service

like

NCBI

's

BLAST

called

the

source

the

BASIL

source-biased

probing

technique

leverages

the

summary

information

of

the

source

to

generate

a

series

of

biased

probes

to

other

services

called

the

targets

This

source-biased

probing

allows

us

to

determine

whether

a

target

service

is

relevant

to

the

source

by

probing

the

target

with

very

few

focused

probes

We

introduce

the

biased

focus

metric

to

discover

and

rank

highly

relevant

data

services

and

measure

relevance

between

services

Our

initial

results

on

both

simulation

and

web

experiments

show

that

the

BASIL

system

supports

efficient

discovery

and

ranking

of

data-intensive

web

services

We

consider

a

universe

of

discourse

W

consisting

of

D

data-intensive

web

services

W

-LCB-

S

S

S

D

-RCB-

where

each

service

produces

one

or

more

XML

documents

in

response

to

a

particular

service

request

Hence

we

describe

each

web

service

S

i

as

a

set

of

M

i

documents

S

i

-LCB-

doc

doc

doc

M

i

-RCB-

For

example

the

documents

corresponding

to

the

NCBI

BLAST

service

would

consist

of

genetic

sequences

and

documentation

generated

in

response

to

a

service

requests

Similarly

the

documents

corresponding

to

PubMed

would

consist

of

the

set

of

medical

journal

articles

in

the

PubMed

data

repository

There

are

N

terms

-LRB-

t

t

t

N

-RRB-

in

the

universe

of

discourse

W

including

both

the

tags

and

content

of

the

XML

documents

where

common

stopwords

-LRB-

like

a

the

and

so

on

-RRB-

have

been

eliminated

Optionally

the

set

of

N

terms

may

be

further

refined

by

stemming

-LSB-

-RSB-

to

remove

prefixes

and

suffixes

Adopting

a

vector-space

model

-LSB-

-RSB-

of

the

service

data

repository

we

describe

each

service

S

i

as

a

vector

consisting

of

the

terms

in

the

service

along

with

a

corresponding

weight

Summary

-LRB-

S

i

-RRB-

-LCB-

-LRB-

t

w

i

-RRB-

-LRB-

t

w

i

-RRB-

-LRB-

t

N

w

iN

-RRB-

-RCB-

BASIL

BiAsed

Service

dIscovery

aLgorithm

A

term

that

does

not

occur

in

any

documents

served

by

a

service

S

i

will

have

weight

Typically

for

any

particular

service

S

i

only

a

fraction

of

the

N

terms

will

have

non-zero

weight

We

refer

to

the

number

of

non-zero

weighted

terms

in

S

i

as

N

i

We

call

the

vector

Summary

-LRB-

S

i

-RRB-

a

service

summary

for

the

data-intensive

web

service

S

i

A

service

summary

is

a

single

aggregate

vector

that

summarizes

the

overall

distribution

of

terms

in

the

set

of

documents

produced

by

the

service

In

this

first

prototype

of

BASIL

we

rely

on

a

bag-of-words

model

that

is

indifferent

to

the

structure

inherent

in

the

XML

documents

As

we

demonstrate

in

the

experiments

section

this

bag-of-words

approach

is

quite

powerful

without

the

added

burden

of

structural

comparisons

We

anticipate

augmenting

future

versions

of

BASIL

to

incorporate

structural

components

-LRB-

to

support

schema

matching

leverage

existing

ontologies

etc.

-RRB-

To

find

Summary

-LRB-

S

i

-RRB-

we

must

first

represent

each

document

doc

j

-LRB-

j

M

-RRB-

as

a

vector

of

terms

and

the

frequency

of

each

term

in

the

document

doc

j

-LCB-

-LRB-

t

f

req

j

-RRB-

-LRB-

t

f

req

j

-RRB-

-LRB-

t

N

f

req

jN

-RRB-

-RCB-

where

f

req

jk

is

the

frequency

of

occurrence

of

term

t

k

in

document

j

The

initial

weight

for

each

term

may

be

based

on

the

raw

frequency

of

the

term

in

the

document

and

it

can

be

refined

using

alternative

occurrence-based

metrics

like

the

normalized

frequency

of

the

term

and

the

term-frequency

inverse

document-frequency

-LRB-

TFIDF

-RRB-

weight

TFIDF

weights

the

terms

in

each

document

vector

based

on

the

characteristics

of

all

documents

in

the

set

of

documents

Given

a

particular

encoding

for

each

document

we

may

generate

the

overall

service

summary

in

a

number

of

ways

Initially

the

weight

for

each

term

in

the

service

summary

may

be

based

on

the

overall

frequency

of

the

term

across

all

the

documents

in

the

service

-LRB-

called

the

service

frequency

or

servFreq

-RRB-

w

ik

servF

req

ik

M

j

f

req

jk

Alternatively

we

can

also

define

the

weight

for

each

term

based

on

the

number

of

documents

in

which

each

term

occurs

-LRB-

called

the

document

count

frequency

or

docCount

-RRB-

Once

we

have

chosen

our

service

model

to

effectively

compare

two

data-intensive

web

services

and

determine

the

relevance

of

one

service

to

another

we

need

two

technical

components

-LRB-

-RRB-

a

technique

for

generating

a

service

summary

and

-LRB-

-RRB-

a

metric

for

measuring

the

relevance

between

the

two

Estimating

Service

Summaries

Ideally

we

would

have

access

to

the

complete

set

of

documents

belonging

to

a

data-intensive

web

service

We

call

a

service

summary

for

S

i

built

on

these

documents

an

actual

service

summary

or

ASummary

-LRB-

S

i

-RRB-

However

the

enormous

size

of

the

underlying

repositories

for

many

data-intensive

web

services

coupled

with

the

non-trivial

costs

of

collecting

documents

-LRB-

through

repeated

service

requests

and

individual

document

transfer

-RRB-

make

it

unreasonable

to

generate

an

actual

service

summary

for

every

service

available

As

a

result

previous

researchers

in

the

context

of

distributed

databases

have

introduced

several

probing

techniques

for

generating

representative

summaries

based

on

small

samples

of

a

document-based

collections

-LSB-

-RSB-

We

call

such

a

representative

summary

an

estimated

service

summary

or

ESummary

-LRB-

S

i

-RRB-

ESummary

-LRB-

S

i

-RRB-

-LCB-

-LRB-

t

w

i

-RRB-

-LRB-

t

w

i

-RRB-

-LRB-

t

N

w

iN

-RRB-

-RCB-

The

number

of

occurring

terms

-LRB-

i.e.

those

terms

that

have

non-zero

weight

-RRB-

in

the

estimated

summary

is

denoted

by

N

i

Typically

N

i

will

be

much

less

than

the

number

of

non-zero

weighted

terms

N

i

in

the

actual

service

summary

since

only

a

fraction

of

the

total

documents

in

a

service

will

be

examined

The

goal

of

a

prober

is

typically

to

find

ESummary

-LRB-

S

i

-RRB-

such

that

the

relative

distribution

of

terms

closely

matches

the

distribution

of

terms

in

ASummary

-LRB-

S

i

-RRB-

even

though

only

a

fraction

of

the

total

service

documents

will

be

examined

Current

probing

techniques

for

estimating

service

summaries

aim

at

estimating

the

overall

summary

of

the

data

served

by

a

web

service

We

classify

them

into

two

categories

random

sampling

and

query-based

sampling

Random

Sampling

N

o

Bias

If

we

had

unfettered

access

to

a

data-intensive

web

service

we

could

randomly

select

terms

from

the

service

to

generate

the

estimated

service

summary

ESummary

-LRB-

S

i

-RRB-

Barring

that

we

could

randomly

select

documents

with

which

to

base

the

estimated

service

summary

We

will

call

such

a

random

selection

mechanism

an

unbiased

prober

since

all

terms

-LRB-

or

documents

-RRB-

are

equally

likely

to

be

selected

In

practice

an

unbiased

prober

is

unrealistic

since

most

services

only

provide

a

request-response

mechanism

for

extracting

documents

Query-based

Sampling

Query

Bias

As

a

good

approximation

to

unbiased

probing

Callan

et

al.

-LSB-

-RSB-

have

introduced

a

query-based

sampling

technique

for

generating

accurate

estimates

of

document-based

collections

by

examining

only

a

fraction

of

the

total

documents

The

Callan

technique

has

been

shown

to

provide

accurate

estimates

using

very

few

documents

-LRB-

e.g.

several

hundred

-RRB-

Adapting

the

Callan

technique

to

the

web

services

context

requires

repeat-edly

requesting

documents

from

a

service

using

a

limited

set

of

service

requests

Since

the

documents

extracted

are

not

chosen

randomly

but

are

biased

by

the

service

request

mechanism

through

the

ranking

of

returned

documents

and

by

providing

incomplete

access

to

the

entire

data

service

repository

we

say

that

the

Callan

technique

displays

query

bias

There

are

several

ways

to

define

the

limited

set

of

queries

including

random

selection

from

a

general

dictionary

and

random

selection

augmented

by

terms

drawn

from

the

extracted

documents

from

the

service

In

the

rest

of

the

paper

when

we

refer

to

an

estimated

service

summary

ESummary

-LRB-

S

i

-RRB-

we

mean

one

that

has

been

produced

by

a

query-biased

prober

Comparing

Service

Summaries

In

order

to

determine

the

relevance

of

one

service

S

i

to

another

service

S

j

and

to

assess

the

nature

of

their

relationship

we

require

an

appropriate

relevance

metric

There

are

a

number

of

possible

relevance

metrics

to

compare

two

service

summaries

A

fairly

simple

and

straightforward

approach

is

based

on

a

count

of

the

number

of

common

terms

in

the

two

services

S

i

and

S

j

rel

-LRB-

S

i

S

j

-RRB-

ESummary

-LRB-

S

i

-RRB-

ESummary

-LRB-

S

j

-RRB-

max

-LRB-

ESummary

-LRB-

S

j

-RRB-

ESummary

-LRB-

S

i

-RRB-

-RRB-

Two

services

with

exactly

the

same

terms

represented

in

their

estimated

summaries

will

have

rel

-LRB-

S

i

S

j

-RRB-

indicating

the

highest

possible

degree

of

relevance

Conversely

two

services

with

no

terms

in

common

will

have

rel

-LRB-

S

i

S

j

-RRB-

indicating

the

lowest

possible

degree

of

relevance

We

now

use

an

example

to

illustrate

why

the

existing

service

summary

estimation

techniques

are

inadequate

for

effectively

discovering

relevant

services

especially

in

terms

of

the

data

coverage

of

one

-LRB-

target

-RRB-

in

the

context

of

the

other

-LRB-

source

-RRB-

Example

We

collected

fifty

documents

from

the

Google

web

service

the

PubMed

web

service

and

ESPN

's

search

site

respectively

using

a

query-based

sampling

technique

for

service

summary

estimation

Using

the

service

summaries

constructed

we

find

that

rel

-LRB-

Google

P

ubM

ed

-RRB-

and

rel

-LRB-

ESP

N

P

ubM

ed

-RRB-

In

both

cases

the

service

summaries

share

very

few

terms

in

common

and

hence

both

Google

and

ESPN

appear

to

be

irrelevant

with

respect

to

PubMed

even

though

Google

provides

considerable

health-related

content

Based

on

these

figures

we

could

incorrectly

conclude

that

-LRB-

-RRB-

Google

is

irrelevant

to

PubMed

and

-LRB-

-RRB-

Relatively

speaking

ESPN

is

more

relevant

to

PubMed

than

Google

This

example

underlines

two

critical

problems

with

current

techniques

for

probing

and

comparing

service

summaries

First

current

service

summary

estimation

techniques

are

concerned

with

generating

overall

-LRB-

or

global

-RRB-

summaries

of

the

underlying

data

repositories

The

goal

is

to

generate

essentially

an

unbiased

estimate

of

the

actual

service

summary

Second

the

current

relevance

comparison

metric

fails

to

serve

as

a

valuable

ranking

metric

or

indicator

of

interesting

relationships

between

target

services

in

terms

of

the

data

coverage

of

a

target

service

with

respect

to

the

source

Bearing

these

issues

in

mind

we

now

introduce

BASIL

an

efficient

web

service

discovery

and

ranking

prototype

that

relies

on

a

biased

perspective

of

services

rather

than

on

a

single

global

perspective

BASIL

relies

on

three

fundamental

steps

-LRB-

-RRB-

source-biased

probing

for

web

service

discovery

-LRB-

-RRB-

evaluation

and

ranking

of

discovered

services

with

the

biased

focus

metric

and

-LRB-

-RRB-

leveraging

the

biased

perspective

of

service

sources

and

targets

to

discover

interesting

relationships

Source-Biased

Probing

Given

a

data-intensive

web

service

the

source

the

source-biased

probing

technique

leverages

the

summary

information

of

the

source

to

generate

a

series

of

biased

probes

for

analyzing

another

service

the

target

This

source-biased

probing

allows

us

to

determine

in

very

few

interactions

whether

a

target

service

is

relevant

to

the

source

by

probing

the

target

with

focused

probes

To

help

differentiate

the

source-biased

approach

from

others

discussed

in

Section

in

this

section

we

use

to

denote

the

source

service

and

to

denote

the

target

service

instead

of

S

i

and

S

j

Given

two

services

and

the

output

of

the

source-biased

probing

is

a

subjective

service

summary

for

that

is

biased

towards

We

define

the

source-biased

summary

of

the

target

service

denoted

by

ESummary

-LRB-

-RRB-

as

follows

ESummary

-LRB-

-RRB-

-LCB-

-LRB-

t

w

-RRB-

-LRB-

t

w

-RRB-

-LRB-

t

N

w

N

-RRB-

-RCB-

N

is

the

total

number

of

terms

used

in

analyzing

the

set

of

data-intensive

web

services

w

i

-LRB-

i

N

-RRB-

is

the

weight

of

term

t

i

defined

using

one

of

the

weight

function

introduced

in

Section

To

distinguish

the

term

weight

w

j

from

the

corresponding

term

weight

in

the

biased

target

summary

we

denote

the

bias

by

w

j

It

is

important

to

note

that

typically

the

inequality

w

j

w

j

does

hold

Concretely

the

source-biased

probing

algorithm

generates

a

source-biased

summary

for

a

target

as

follows

It

uses

the

estimated

service

summary

of

the

source

denoted

by

ESummary

-LRB-

-RRB-

as

a

dictionary

of

candidate

probe

terms

and

sends

a

series

of

query

requests

parameterized

by

probe

terms

selected

from

ESummary

-LRB-

-RRB-

to

the

target

service

for

each

probe

term

it

retrieves

the

top

m

matched

documents

from

generates

summary

terms

and

updates

ESummary

-LRB-

-RRB-

This

process

repeats

until

a

stopping

condition

is

met

Figure

illustrates

the

source-biased

probing

process

Note

that

in

this

first

prototype

of

BASIL

the

service

requests

are

constrained

to

keyword-based

probes

Note

that

the

source-biased

approach

can

also

be

applied

to

UDDI-directory-based

discovery

by

restricting

SourceBiasedProbing

-LRB-

Source

Target

-RRB-

For

target

service

initialize

ESummary

-LRB-

-RRB-

repeat

Invoke

the

probe

term

selection

algorithm

to

select

a

one-term

query

probe

q

from

the

source

of

bias

ESummary

-LRB-

-RRB-

Send

the

query

q

to

the

target

service

Retrieve

the

top-m

documents

from

Update

ESummary

-LRB-

-RRB-

with

the

terms

and

frequencies

from

the

top-m

documents

until

Stop

probing

condition

is

met

return

ESummary

-LRB-

-RRB-

Figure

Source-Biased

Probing

Algorithm

the

source

summary

to

be

generated

from

the

meta-data

description

maintained

at

the

registries

rather

than

performing

the

source-biased

probing

directly

However

the

quality

of

the

discovery

results

will

be

lower

due

to

the

lack

of

richness

in

the

metadata

maintained

at

the

service

registries

for

many

services

Now

we

use

a

simple

example

to

illustrate

the

power

of

source-biased

probing

For

presentation

brevity

we

are

considering

a

simplistic

world

of

only

very

few

terms

per

service

summary

In

reality

each

service

summary

would

consist

of

orders

of

magnitude

more

terms

Example

Suppose

that

our

goal

is

to

understand

the

relevance

of

Google

to

PubMed

Suppose

ESummary

-LRB-

P

ubMed

-RRB-

-LCB-

arthritis

bacteria

cancer

-RCB-

-LRB-

where

for

simplicity

we

have

dropped

the

term

weights

from

the

summary

-RRB-

Again

for

simplicity

suppose

that

Google

provides

access

to

only

three

types

of

information

health

animals

and

cars

ASummary

-LRB-

Google

-RRB-

-LCB-

arthritis

bacteria

cancer

dog

elephant

frog

garage

helmet

indycar

-RCB-

An

unbiased

prober

could

result

in

ESummary

-LRB-

Google

-RRB-

-LCB-

arthritis

frog

helmet

-RCB-

whereas

a

source-biased

prober

could

result

in

ESummary

P

ubM

ed

-LRB-

Google

-RRB-

-LCB-

arthritis

bacteria

cancer

-RCB-

This

simple

example

illustrates

the

essence

of

the

source-biased

probing

and

how

it

accentuates

the

commonality

between

the

two

services

The

performance

and

effectiveness

of

the

source-biased

probing

algorithm

depends

upon

a

number

of

factors

including

the

selection

criterion

used

for

choosing

source-specific

candidate

probe

terms

and

the

type

of

stop

condition

used

to

terminate

the

probing

process

Mechanisms

to

Select

Probe

Terms

There

are

several

possible

ways

to

select

the

probes

based

on

the

statistics

stored

with

each

service

summary

including

uniform

random

selection

and

selection

based

on

top-weighted

terms

In

general

the

selection

criterion

will

recommend

a

query

term

drawn

from

the

set

N

of

all

non-zero

weighted

terms

in

the

unbiased

source

summary

ESummary

-LRB-

-RRB-

Uniform

Random

Selection

In

this

simplest

of

selection

techniques

each

term

that

occurs

in

ESummary

-LRB-

-RRB-

has

an

equal

probability

of

being

selected

i.e.

P

rob

-LRB-

selecting

term

j

-RRB-

N

Weight-Based

Selection

Rather

than

randomly

selecting

query

terms

we

could

instead

rely

on

a

ranking

of

the

terms

by

one

of

the

statistics

that

are

recorded

with

each

service

summary

For

example

all

terms

in

ESummary

-LRB-

-RRB-

could

be

ranked

according

to

the

weight

of

each

term

Terms

would

then

be

selected

in

descending

order

of

weight

Depending

on

the

type

of

weight

cataloged

-LRB-

e.g.

servF

req

docCount

etc.

-RRB-

several

flavors

of

weight-based

selection

may

be

considered

Different

Types

of

Stop

Probing

Conditions

The

stop

probing

condition

is

the

second

critical

component

in

the

source-biased

probing

algorithm

We

consider

four

different

types

of

conditions

that

might

be

used

in

practice

Number

of

Queries

After

some

fixed

number

of

query

probes

-LRB-

M

axP

robes

-RRB-

end

the

probing

This

condition

is

agnostic

to

the

number

of

documents

that

are

examined

for

each

service

Documents

Returned

In

contrast

to

the

first

technique

the

second

condition

considers

not

the

number

of

queries

but

the

total

number

of

documents

-LRB-

M

axDocs

-RRB-

returned

by

the

service

Since

some

queries

may

return

no

documents

this

stopping

condition

will

require

more

query

probes

than

the

first

alternative

when

M

axP

robes

M

axDocs

Document

Thresholding

Rather

than

treating

each

document

the

same

this

third

alternative

applies

a

threshold

value

to

each

document

to

determine

if

it

should

be

counted

toward

M

axDocs

For

each

document

we

may

calculate

the

relevance

of

the

document

to

the

source

of

bias

ESummary

-LRB-

-RRB-

If

the

document

relevance

is

greater

than

some

threshold

value

then

the

document

is

counted

Otherwise

the

document

is

discarded

Steady-State

Rather

than

relying

on

a

count

of

queries

or

documents

this

final

stopping

condition

alternative

instead

relies

on

the

estimated

summary

reaching

a

steady-state

After

each

probe

we

calculate

the

difference

between

the

new

value

of

ESummary

-LRB-

-RRB-

and

the

old

value

If

the

difference

-LRB-

which

may

be

calculated

in

a

number

of

ways

-RRB-

is

less

than

some

small

value

then

we

consider

the

summary

stable

and

stop

the

probing

Due

to

the

space

limitation

we

refer

readers

to

our

technical

report

-LSB-

-RSB-

for

detailed

experiments

on

the

impact

of

these

two

parameters

Evaluating

and

Ranking

Services

Given

a

source

and

a

target

service

once

we

generate

the

source-biased

summary

for

the

target

service

we

need

an

efficient

mechanism

to

evaluate

the

source-biased

relevance

of

a

target

service

with

respect

to

the

source

Once

a

set

of

target

services

have

been

evaluated

with

the

source-biased

relevance

metric

we

can

then

rank

the

target

services

with

respect

to

the

source

of

bias

We

begin

by

discussing

the

necessary

components

of

the

source-biased

metric

Let

denote

a

source

service

modeled

by

an

estimated

summary

and

denote

a

target

service

with

a

biased

summary

and

let

f

ocus

-LRB-

-RRB-

denote

the

source-biased

focus

measure

We

define

f

ocus

-LRB-

-RRB-

to

be

a

measure

of

the

topical

focus

of

the

target

service

with

respect

to

the

source

of

bias

The

focus

metric

ranges

from

to

with

lower

values

indicating

less

focus

and

higher

values

indicating

more

focus

In

general

f

ocus

is

not

a

symmetric

relation

We

may

describe

any

two

data-intensive

web

services

and

with

the

focus

in

terms

of

by

f

ocus

-LRB-

-RRB-

or

in

terms

of

by

f

ocus

-LRB-

-RRB-

We

propose

to

use

the

well-known

cosine

similarity

-LRB-

or

normalized

inner

product

-RRB-

to

approximate

the

source-biased

focus

measure

We

define

the

cosine-based

focus

as

follows

Cosine

f

ocus

-LRB-

-RRB-

N

k

w

k

w

k

N

k

-LRB-

w

k

-RRB-

N

k

-LRB-

w

k

-RRB-

where

w

k

is

the

weight

for

term

k

in

ESummary

-LRB-

-RRB-

and

w

k

is

the

biased

weight

for

term

k

in

ESummary

-LRB-

-RRB-

The

cosine

ranges

from

to

with

higher

scores

indicating

a

higher

degree

of

similarity

In

contrast

the

cosine

between

orthogonal

vectors

is

indicating

that

they

are

completely

dissimilar

The

cosine

measures

the

angle

between

two

vectors

regardless

of

the

length

of

each

vector

Intuitively

the

cosine-based

biased

focus

is

appealing

since

it

reasonably

captures

the

relevance

between

two

data-intensive

web

services

Ranking

Relevant

Services

Given

the

biased

focus

measure

we

may

probe

a

group

of

get

services

to

identify

the

most

relevant

services

to

the

source

of

bias

For

a

single

source

of

bias

S

from

our

universe

of

discourse

W

we

may

evaluate

multiple

target

services

S

S

S

d

For

each

target

service

we

may

evaluate

the

appropriate

focus

measure

for

each

source-target

pair

-LRB-

i.e.

f

ocus

S

-LRB-

S

-RRB-

f

ocus

S

-LRB-

S

-RRB-

etc.

-RRB-

We

may

then

rank

the

target

services

in

descending

order

in

terms

of

their

source-biased

focus

with

respect

to

S

As

we

will

show

in

our

experiments

section

source-biased

probing

results

in

the

identification

of

relevant

services

that

existing

approaches

may

overlook

We

also

show

that

source-biased

probing

can

generate

source-biased

summaries

of

good

quality

using

far

fewer

documents

than

existing

approaches

placing

significantly

less

burden

on

the

target

services

Identifying

Interesting

Relationships

The

critical

third

component

of

the

BASIL

system

consists

of

the

techniques

for

exploiting

and

understanding

interesting

relationships

between

services

using

a

source-biased

lens

By

analyzing

the

nature

of

the

relationships

between

data-intensive

web

services

we

will

provide

support

for

understanding

the

relative

scope

and

coverage

of

one

service

with

respect

to

another

The

source-biased

probing

framework

and

biased

focus

measure

provide

the

flexible

building

blocks

for

automated

identification

of

interesting

relationships

between

services

especially

since

the

framework

promotes

an

asymmetric

source-biased

view

for

any

two

services

Our

relationship

discovery

module

creates

a

flexible

organization

of

services

where

each

service

is

annotated

with

a

list

of

relationship

sets

The

two

typical

relationship

types

we

have

identified

are

similarity-based

and

hierarchical-based

Similarity-Based

Relationships

Given

the

universe

of

discourse

W

-LCB-

S

S

S

D

-RCB-

we

identify

three

similarity-based

relationship

sets

for

a

particular

service

S

i

These

relationship

sets

are

defined

in

terms

of

threshold

values

high

and

low

where

low

high

equivalent

The

first

relationship

says

that

if

both

focus

S

i

-LRB-

S

j

-RRB-

high

and

f

ocus

S

j

-LRB-

S

i

-RRB-

high

hold

then

we

may

conclude

that

S

i

is

sufficiently

focused

on

S

j

and

S

j

is

sufficiently

focused

on

S

i

Hence

the

two

services

are

approximately

the

same

in

terms

of

their

data

coverage

We

call

this

approximate

equality

equivalence

It

indicates

that

the

equivalence

is

not

absolute

but

is

a

function

of

the

parameter

high

Formally

equivalent

-LRB-

S

i

-RRB-

-LCB-

S

j

W

focus

S

i

-LRB-

S

j

-RRB-

high

f

ocus

S

j

-LRB-

S

i

-RRB-

high

-RCB-

complement

If

both

focus

S

i

-LRB-

S

j

-RRB-

low

and

f

ocus

S

j

-LRB-

S

i

-RRB-

low

hold

then

we

can

conclude

that

S

i

and

S

j

are

sufficiently

concerned

with

different

topics

since

neither

one

is

very

focused

on

the

other

We

annotate

this

approximate

complementary

nature

with

the

prefix

Formally

complement

-LRB-

S

i

-RRB-

-LCB-

S

j

W

focus

S

i

-LRB-

S

j

-RRB-

low

focus

S

j

-LRB-

S

i

-RRB-

low

-RCB-

overlap

When

two

services

S

i

and

S

j

are

neither

equivalent

nor

complementary

we

say

that

the

two

services

overlap

Formally

overlap

-LRB-

S

i

-RRB-

-LCB-

S

j

W

S

j

complement

-LRB-

S

i

-RRB-

S

j

equivalent

-LRB-

S

i

-RRB-

-RCB-

Hierarchical

Relationships

In

addition

to

similarity-based

relationship

sets

we

also

define

hierarchical

relationship

sets

by

measuring

the

relative

coverage

of

target

services

in

W

with

respect

to

a

particular

text

service

S

i

-LRB-

source

-RRB-

These

hierarchical

relationship

sets

are

defined

in

terms

of

a

parameter

dif

f

where

dif

f

superset

If

focus

S

i

-LRB-

S

j

-RRB-

focus

S

j

-LRB-

S

i

-RRB-

dif

f

then

a

relatively

significant

portion

of

S

i

is

contained

in

S

j

indicating

that

S

j

has

a

superset

relationship

with

S

j

We

use

the

prefix

to

indicate

that

S

j

is

not

a

strict

superset

of

S

i

but

rather

that

the

relationship

is

parameterized

by

dif

f

Formally

superset

-LRB-

S

i

-RRB-

-LCB-

S

j

W

focus

S

i

-LRB-

S

j

-RRB-

focus

S

j

-LRB-

S

i

-RRB-

dif

f

-RCB-

subset

Conversely

If

focus

S

j

-LRB-

S

i

-RRB-

focus

S

i

-LRB-

S

j

-RRB-

dif

f

then

a

relatively

significant

portion

of

S

j

is

contained

in

S

i

indicating

that

S

j

has

a

subset

relationship

with

S

i

Similarly

S

j

is

not

a

strict

subset

of

S

i

but

rather

the

relationship

is

parameterized

by

dif

f

Formally

subset

-LRB-

S

i

-RRB-

-LCB-

S

j

W

focus

S

j

-LRB-

S

i

-RRB-

focus

S

i

-LRB-

S

j

-RRB-

dif

f

-RCB-

We

note

that

the

determination

of

the

appropriate

values

is

critical

for

the

correct

assignation

of

services

to

each

relationship

set

In

our

experiments

section

we

illustrate

how

these

relationship

sets

may

be

created

but

for

now

we

leave

the

optimization

of

values

as

future

work

Using

Relationship

Sets

Both

similarity

based

and

hierarchy-based

inter-service

relationships

can

be

generated

automati-cally

and

used

as

metadata

annotation

to

each

of

the

services

These

source-biased

relevance

data

provide

a

flexible

foundation

for

relationship

analysis

among

services

For

any

service

S

i

we

need

only

consult

the

appropriate

relationship

set

The

three

similarity-based

relationship

sets

provide

the

basis

for

answering

queries

of

the

form

What

other

services

are

most

like

X

Somewhat

like

X

Or

complementary

to

X

The

two

hierarchical-based

sets

provide

the

basis

for

answering

queries

of

the

form

What

other

services

are

more

general

than

X

Or

more

specialized

than

X

In

addition

these

relationship

sets

are

useful

for

routing

service

requests

to

the

appropriate

services

For

example

a

user

interested

in

BLAST

data

may

choose

to

use

both

NCBI

's

BLAST

service

and

all

of

the

services

that

have

a

equivalence

relationship

with

NCBI

BLAST

Alternatively

a

user

interested

in

maximizing

coverage

of

multiple

topically-distinct

services

may

choose

to

query

both

the

source

service

she

knows

about

and

any

members

in

the

complementary

set

of

the

source

service

The

hierarchical

relationship

sets

are

particularly

helpful

in

cases

where

a

user

may

refine

a

service

request

to

more

specialized

services

or

alternatively

may

choose

to

generalize

the

scope

of

the

service

request

by

considering

services

further

up

the

hierarchy

to

get

more

matching

answers

One

of

the

critical

parameters

to

the

success

of

BASIL

's

source-biased

probing

is

the

choice

of

probe

terms

from

the

source

of

bias

We

have

discussed

several

selection

techniques

as

well

as

different

ways

to

define

stop-probing

conditions

In

this

section

we

introduce

a

refinement

over

these

simple

selection

techniques

whereby

the

source

summary

is

segmented

into

k

groups

of

co-occurring

terms

The

main

idea

is

to

it-eratively

select

one

term

from

each

of

the

k

groups

to

probe

the

target

We

call

these

terms

the

focal

terms

of

the

corresponding

group

When

used

in

conjunction

with

the

general

source-biased

probing

algorithm

we

have

an

enhanced

version

called

source-biased

probing

with

focal

terms

A

unique

advantage

of

using

focal

terms

is

that

the

biased

summaries

of

target

services

can

be

generated

in

fewer

queries

with

higher

quality

Focal

Terms

and

Focal

Term

Groups

Let

denote

a

source

service

with

its

unbiased

service

summary

ESummary

We

denote

the

set

of

terms

with

non-zero

weight

in

ESummary

-LRB-

i.e.

the

terms

that

actually

occur

in

the

service

-RRB-

as

T

erms

-LRB-

-RRB-

where

T

erms

-LRB-

-RRB-

consists

of

n

terms

t

t

t

n

A

focal

term

group

is

a

subset

of

terms

in

the

set

T

erms

-LRB-

-RRB-

that

co-occur

in

the

documents

of

We

denote

a

focal

term

Table

Example

Focal

Terms

for

PubMed

care

education

family

management

brain

gene

protein

nucleotide

clinical

noteworthy

taxonomy

experimental

molecular

therapy

aids

evidence

research

winter

group

i

as

F

T

erms

i

The

main

idea

behind

source-biased

probing

with

focal

terms

is

to

partition

the

set

T

erms

-LRB-

-RRB-

into

k

disjoint

term

groups

such

that

the

terms

within

each

term

group

co-occur

in

documents

of

more

frequently

than

they

do

with

terms

from

other

term

groups

Formally

we

need

an

algorithm

that

can

find

a

partition

of

T

erms

-LRB-

-RRB-

into

k

focal

term

groups

T

erms

-LRB-

-RRB-

-LCB-

F

T

erms

F

T

erms

i

F

T

erms

k

k

i

F

T

erms

i

-LCB-

t

t

n

-RCB-

and

F

T

erms

i

F

T

erms

j

-RCB-

In

Table

we

show

an

example

of

five

focal

term

groups

for

a

collection

of

PubMed

documents

Note

that

k

is

intended

to

be

very

small

since

the

focal

term

groups

are

meant

to

be

very

coarse

Given

k

focal

term

groups

by

selecting

a

focal

term

from

each

term

group

F

T

erms

i

as

a

probing

query

we

hope

to

retrieve

documents

that

also

contain

many

of

the

other

words

in

that

focal

term

group

For

example

suppose

we

are

using

a

frequency-based

measure

for

query

probe

selection

from

PubMed

The

top

four

query

terms

may

be

brain

gene

protein

and

nucleotide

Suppose

these

four

terms

tend

to

co-occur

with

each

other

as

indicated

in

Table

By

sending

the

first

query

brain

to

a

target

service

we

could

reasonably

expect

to

find

the

other

three

terms

since

our

analysis

of

the

source

indicates

that

these

four

terms

tend

to

co-occur

A

naive

source-biased

prober

would

ignore

this

co-occurrence

information

and

instead

send

the

other

three

queries

gene

protein

and

nucleotide

even

though

we

might

reasonably

expect

for

those

queries

to

generate

documents

similar

to

those

generated

by

the

first

query

brain

In

essence

we

will

have

used

four

queries

when

a

single

query

would

have

sufficed

at

adequately

exploring

the

term

space

of

the

target

It

is

important

to

note

that

unlike

previous

research

in

grouping

terms

for

query-expansion

-LSB-

-RSB-

or

finding

similar

terms

-LSB-

-RSB-

our

goal

is

not

to

find

close

semantic

relationships

between

terms

but

rather

to

find

very

coarse

co-occurrence

associations

among

terms

to

support

a

more

efficient

and

effective

biased

service

summary

estimation

For

example

though

we

may

discover

that

brain

and

protein

tend

to

co-occur

we

do

not

claim

that

there

is

a

close

semantic

relationship

between

the

two

terms

Finding

Focal

Terms

In

this

section

we

discuss

how

we

may

adapt

a

popular

clustering

technique

to

the

problem

of

focal

term

discovery

Recall

that

in

Section

we

view

a

service

S

i

as

a

set

of

documents

each

of

which

is

described

by

a

vector

of

terms

and

weights

We

now

invert

our

view

of

a

service

using

the

same

set

of

information

We

consider

a

service

S

i

as

a

collection

of

terms

each

of

which

is

described

by

a

vector

of

the

documents

in

which

the

term

occurs

and

a

weight

describing

the

occurrence

frequency

of

the

term

in

the

corresponding

document

Hence

we

have

T

erms

-LRB-

S

i

-RRB-

-LCB-

term

term

term

N

-RCB-

For

the

N

terms

in

the

service

each

term

j

-LRB-

j

N

-RRB-

is

a

vector

of

documents

and

weights

term

j

-LCB-

-LRB-

doc

w

j

-RRB-

-LRB-

doc

w

j

-RRB-

-LRB-

doc

M

w

jM

-RRB-

-RCB-

We

can

define

a

segmentation

technique

for

finding

focal

term

groups

by

clustering

the

set

T

erms

-LRB-

S

i

-RRB-

into

k

clusters

Given

the

term

vectors

and

the

similarity

function

a

number

of

clus-FocalTerms

-LRB-

Num

Clusters

k

Input

Vectors

D

-RRB-

Let

D

-LCB-

d

d

n

-RCB-

denote

the

set

of

n

term

vectors

Let

M

denote

the

total

number

of

documents

in

D

Let

d

j

-LRB-

doc

w

j

-RRB-

-LRB-

doc

M

w

jM

-RRB-

denote

a

term

vector

of

M

elements

w

jl

is

TFIDF

weight

of

the

doc

l

in

term

j

-LRB-

l

M

-RRB-

Let

C

-LCB-

C

C

k

-RCB-

denote

a

clustering

of

D

into

k

clusters

Let

i

denote

the

center

of

cluster

C

i

foreach

cluster

C

i

Randomly

pick

a

term

vector

say

d

j

from

D

Initialize

a

cluster

center

i

d

j

where

d

j

D

repeat

foreach

input

term

vector

d

j

D

foreach

cluster

C

i

C

i

k

compute

i

sim

-LRB-

d

j

mu

i

-RRB-

if

h

is

the

smallest

among

k

mu

h

is

the

nearest

cluster

center

to

d

j

Assign

d

j

to

the

cluster

C

h

refine

cluster

centers

using

centroids

foreach

cluster

C

i

C

foreach

doc

l

in

d

j

-LRB-

l

M

-RRB-

-RRB-

cw

ij

C

i

M

l

w

jl

i

-LRB-

doc

cw

i

-RRB-

-LRB-

doc

M

cw

iM

-RRB-

until

cluster

centers

no

longer

change

return

C

Figure

Focal

Term

Clustering

Algorithm

tering

algorithms

can

be

applied

to

partition

the

set

T

erms

-LRB-

S

i

-RRB-

of

N

terms

into

k

clusters

We

choose

Simple

K-Means

since

it

is

conceptually

simple

and

computationally

efficient

The

algorithm

starts

by

generating

k

random

cluster

centers

Each

term

is

assigned

to

the

cluster

with

the

most

similar

-LRB-

or

least

distant

-RRB-

center

The

similarity

is

computed

based

on

the

close-ness

of

the

term

and

each

of

the

cluster

centers

Then

the

algorithm

refines

the

k

cluster

centers

based

on

the

centroid

of

each

cluster

Terms

are

then

re-assigned

to

the

cluster

with

the

most

similar

center

The

cycle

of

calculating

centroids

and

assigning

terms

in

T

erms

-LRB-

S

i

-RRB-

to

k

clusters

repeats

until

the

cluster

centroids

stabilize

Let

C

denote

a

cluster

in

the

form

of

a

set

of

terms

in

the

cluster

The

centroid

of

cluster

C

is

centroid

C

-LRB-

doc

C

jC

w

j

-RRB-

-LRB-

doc

C

jC

w

j

-RRB-

-LRB-

doc

M

C

jC

w

jM

-RRB-

where

w

jl

is

the

weight

of

term

j

in

document

l

and

the

formula

C

l

C

w

jl

denotes

the

average

weight

of

the

document

l

in

the

cluster

C

A

sketch

of

the

K-Means

term

clustering

based

on

term-vector

of

a

service

is

provided

in

Figure

The

similarity

function

used

in

Figure

can

be

defined

using

a

number

of

functions

In

this

paper

we

use

the

cosine

similarity

function

Given

a

set

of

N

terms

and

a

set

of

M

documents

where

w

ik

denotes

the

weight

for

term

k

in

document

i

-LRB-

k

N

i

M

-RRB-

the

cosine

function

prescribes

sim

-LRB-

term

i

term

j

-RRB-

N

k

w

ik

w

jk

N

k

-LRB-

w

ik

-RRB-

N

k

-LRB-

w

jk

-RRB-

In

Section

we

report

the

initial

experiments

on

effectiveness

of

using

focal

terms

to

optimize

the

source-biased

probing

algorithm

showing

that

the

source-biased

algorithm

with

focal

terms

results

in

more

efficient

probing

for

varying

numbers

of

focal-term

groups

Selecting

Focal-Based

Probes

Once

the

k

focal

term

groups

have

been

constructed

for

a

source

the

remaining

problem

is

how

to

select

the

best

terms

for

probing

a

target

service

We

propose

a

simple

round-robin

selection

technique

whereby

a

single

term

is

selected

from

each

focal

term

group

in

turn

Once

a

single

term

has

been

selected

from

each

group

the

cycle

repeats

by

selecting

a

second

term

from

each

group

a

third

term

and

so

on

Given

this

basic

strategy

we

may

use

a

number

of

techniques

for

determining

the

order

by

which

to

select

terms

from

the

k

groups

and

for

selecting

probe

terms

from

each

focal

term

group

One

way

to

determine

the

order

of

focal

term

groups

is

based

upon

the

size

of

each

group

We

begin

with

the

group

with

the

most

terms

and

end

each

cycle

with

the

group

that

has

the

smallest

number

of

terms

For

each

focal

term

group

we

may

decide

which

term

to

select

for

each

cycle

by

using

one

of

the

selection

criteria

discussed

in

Section

In

this

section

we

describe

four

sets

of

experiments

designed

to

evaluate

the

benefits

and

costs

of

BASIL

The

first

set

intends

to

show

the

effectiveness

of

our

source-biased

probing

algorithm

and

compare

its

performance

with

query-biased

probing

and

unbiased

probing

The

second

set

evaluates

the

biased

focus

measure

as

an

effective

tool

for

ranking

services

The

third

set

shows

the

efficiency

of

the

biased

focus

measure

in

identifying

interesting

inter-service

relationships

The

fourth

set

evaluates

the

efficacy

of

source-biased

probing

with

focal

terms

by

comparing

the

basic

source-biased

probing

versus

source-biased

probing

with

varying

number

of

groups

of

focal

terms

Our

experiments

show

that

focal

term

probing

can

achieve

about

ten

percent

performance

improvement

over

the

basic

algorithm

for

source-biased

probing

Since

there

are

no

large

data-intensive

web

service

collections

for

experimentation

we

rely

on

-LRB-

-RRB-

a

large

collection

of

newsgroups

designed

to

emulate

the

diversity

and

scope

of

real-world

data-intensive

web

services

and

-LRB-

-RRB-

a

modest

collection

of

real-world

web

sources

Since

the

services

in

the

web

collection

change

frequently

and

are

beyond

our

control

and

in

an

effort

not

to

overload

any

one

site

we

relied

on

the

newsgroup

dataset

for

rigorous

experimental

validation

Newsgroup

Collection

We

collected

articles

from

randomly

selected

usenet

newsgroups

over

the

period

June

to

July

We

eliminated

overly

small

newsgroups

containing

fewer

than

articles

heavily

spammed

newsgroups

and

newsgroups

with

primarily

binary

data

After

filtering

out

these

groups

we

were

left

with

single

topic

newsgroups

ranging

in

size

from

to

articles

In

an

effort

to

match

the

heterogeneity

and

scope

inherent

in

many

real-world

services

we

constructed

additional

groups

of

mixed

topics

by

randomly

selecting

articles

from

anywhere

from

to

single

topic

newsgroups

and

aggregate

topic

newsgroups

by

combining

articles

from

related

newsgroups

-LRB-

e.g.

by

selecting

random

documents

from

all

the

subgroups

in

comp.unix

into

a

single

aggregate

group

-RRB-

In

total

the

newsgroup

collection

consists

of

over

GB

worth

of

articles

in

groups

Web

Collection

For

the

second

collection

we

randomly

selected

sites

from

the

ProFusion

-LSB-

-RSB-

directory

of

web

sites

that

support

queries

in

addition

to

Google

and

PubMed

We

queried

each

site

with

a

randomized

set

of

single-word

probes

drawn

from

the

standard

Unix

dictionary

and

collected

a

maximum

of

documents

per

site

Probing

Framework

We

built

a

probing

engine

in

Java

for

use

in

all

of

our

experiments

For

each

group

in

both

Documents

Examined

Average

Source

Similarity

Source

Bias

Query

Bias

Query

Bias

No

Bias

Figure

Probing

Efficiency

for

Pairs

datasets

we

constructed

the

estimated

service

summary

based

on

the

overall

term

frequency

of

each

term

-LRB-

servF

req

-RRB-

We

eliminated

a

set

of

common

stopwords

-LRB-

e.g.

a

the

and

so

on

-RRB-

as

well

as

collection-specific

stopwords

-LRB-

e.g.

wrote

said

and

so

on

for

the

newsgroup

collection

-RRB-

Effectiveness

of

Source-Biased

Probing

The

goal

of

our

first

set

of

experiments

is

to

compare

source-biased

probing

with

existing

probing

techniques

and

to

evaluate

the

efficiency

and

quality

of

source-biased

probing

The

source-biased

probing

show

significant

gain

in

terms

of

the

percentage

of

documents

probed

that

are

similar

to

the

source

We

first

evaluate

the

efficiency

of

source-biased

probing

in

terms

of

the

number

of

documents

required

to

be

extracted

from

each

target

and

the

percentage

of

the

documents

extracted

that

are

similar

to

the

source

The

higher

percentage

of

documents

similar

-LRB-

relevant

-RRB-

to

the

source

the

more

effective

a

probing

algorithm

is

We

selected

random

source-target

pairs

from

the

newsgroup

collection

For

each

pair

we

evaluated

four

probing

techniques

a

source-biased

prober

-LRB-

Source

Bias

-RRB-

that

selects

probe

terms

from

the

source

summary

in

decreasing

order

of

servF

req

a

query-biased

prober

-LRB-

Query

Bias

-RRB-

that

randomly

selects

probes

from

the

standard

Unix

dictionary

of

English

terms

a

query-biased

prober

-LRB-

Query

Bias

-RRB-

that

selects

its

initial

probe

from

the

Unix

dictionary

but

once

the

first

document

has

been

retrieved

from

the

target

all

subsequent

probes

are

selected

based

on

the

estimated

servF

req

of

the

target

's

service

summary

and

an

unbiased

prober

-LRB-

No

Bias

-RRB-

that

selects

documents

at

random

from

each

target

For

each

pair

we

evaluated

each

of

the

four

probing

techniques

for

up

to

total

documents

extracted

from

each

target

collecting

a

maximum

of

documents

per

probe

query

from

each

target

In

Figure

we

show

the

average

percentage

of

documents

similar

-LRB-

relevant

-RRB-

to

the

source

-LRB-

Cosine

f

ocus

-LRB-

-RRB-

-RRB-

over

all

source-target

pairs

as

a

function

of

the

number

of

documents

examined

in

each

target

The

percentage

of

the

documents

extracted

that

are

similar

to

the

source

-LRB-

biased

f

ocus

measure

-RRB-

indicates

the

quality

of

document

being

extracted

from

each

target

We

see

that

the

source-biased

probing

outperforms

the

No

Bias

prober

and

the

Query

Bias

prober

by

about

and

outperforms

the

Query

Bias

prober

by

about

Clearly

the

higher

focus

value

means

the

higher

success

for

a

probing

algorithm

Figure

shows

another

experiment

where

we

also

identified

in

our

set

of

source-target

pairs

all

of

those

pairs

that

were

a

priori

similar

-LRB-

e.g.

mac.apps

and

mac.system

-RRB-

or

dissimilar

-LRB-

e.g.

textiles.sewing

and

perl.misc

-RRB-

We

show

the

relative

performance

of

the

Source

Bias

Query

Bias

and

No

Bias

Documents

Examined

Average

Source

Similarity

Source

Bias

Query

Bias

No

Bias

Similar

Dissimilar

Figure

Probing

Efficiency

Breakdown

Documents

Examined

Docs

Similar

to

Source

Source

Bias

Query

Bias

Query

Bias

No

Bias

Figure

Average

Document

Quality

for

Pairs

probers

against

these

similar

and

dissimilar

pairs

The

source-biased

prober

requires

fewer

documents

to

achieve

the

same

relevance

level

as

the

other

probers

for

all

source-target

pairs

and

for

the

similar

and

dissimilar

pairs

For

example

for

the

similar

source-target

pairs

in

Figure

the

source-biased

prober

identifies

target

documents

with

focus

after

extracting

fewer

than

documents

In

contrast

the

other

probers

require

between

two

and

three

times

as

many

documents

to

achieve

the

same

quality

The

third

experiment

is

shown

in

Figure

Here

we

want

to

show

how

quickly

a

source-biased

prober

can

hone

on

the

most

source-relevant

documents

in

a

target

by

plotting

the

percentage

of

the

documents

extracted

that

are

similar

-LRB-

relevant

-RRB-

to

the

source

for

each

of

the

four

probers

As

shown

in

Figure

the

source-biased

prober

performs

nearly

two-times

better

than

other

probers

over

of

the

first

documents

extracted

from

a

target

are

source-relevant

whereas

the

other

probers

identify

between

and

source-relevant

documents

As

more

documents

are

examined

for

each

target

the

source-biased

prober

continues

to

maintain

an

advantage

over

the

other

probers

Ranking

Effectiveness

with

Biased

Focus

The

second

set

of

experiments

intends

to

evaluate

how

well

source-biased

probing

compares

with

the

alternative

techniques

when

it

comes

to

evaluating

and

ranking

collection

of

target

services

We

use

PubMed

as

the

source

and

examine

all

web

sites

as

targets

We

computed

the

biased

focus

score

using

Cosine

f

ocus

-LRB-

-RRB-

and

then

ranked

all

targets

relative

to

PubMed

using

the

biased

focus

measure

Since

the

web

sites

do

not

support

random

document

selection

we

are

unable

to

evaluate

an

unbiased

prober

So

this

experiment

only

compares

the

source-biased

prober

with

query

biased

prober

Table

shows

Table

Identifying

Web

Sources

Relevant

to

PubMed

Query

Bias

Source

Bias

AMA

Open

Directory

-LRB-

-RRB-

WebMD

Google

-LRB-

-RRB-

Linux

Journal

About

-LRB-

-RRB-

HealthAtoZ

WebMD

-LRB-

-RRB-

DevGuru

AMA

-LRB-

-RRB-

FamilyTree

Magazine

HealthAtoZ

-LRB-

-RRB-

Mayo

Clinic

Monster

-LRB-

-RRB-

Novell

Support

Mayo

Clinic

-LRB-

-RRB-

Random

House

Random

House

-LRB-

-RRB-

January

Magazine

BBC

News

-LRB-

-RRB-

comp.sys.mac.system

comp.unix.misc

gnu.emacs.help

rec.aviation.owning

rec.games.chess.misc

rec.org.sca

rec.pets.cats.misc

sci.physics.research

soc.culture.hawaii

talk.religion.misc

Relevance

Precision

No

Bias

Query

Bias

Source

Bias

Figure

Precision

for

Source

Newsgroups

the

ranked

sites

relative

to

PubMed

In

the

Source

Bias

column

we

also

list

in

parenthesis

the

rank

of

each

site

assigned

by

the

Query

Bias

prober

The

query-biased

prober

identifies

several

health-related

sites

in

the

web

collection

but

it

mistakenly

lists

Linux

Journal

ahead

of

HealthAtoZ

as

well

as

listing

a

web

development

site

-LRB-

DevGuru

-RRB-

and

a

genealogical

magazine

-LRB-

FamilyTree

-RRB-

ahead

of

the

health-related

Mayo

Clinic

Overall

only

four

of

the

top-ten

sites

could

be

considered

topically

relevant

to

PubMed

In

contrast

the

source-biased

prober

's

top-eight

sites

are

all

relevant

to

PubMed

In

addition

to

the

health-related

sites

the

source-biased

prober

also

identifies

three

general

sites

that

offer

access

to

medical

literature

-LRB-

Open

Directory

Google

and

About

-RRB-

that

are

ranked

significantly

lower

by

the

query-biased

prober

Interestingly

the

source-biased

prober

identifies

a

fair

number

of

scientific

and

bioinformatics-related

job

descriptions

in

the

Monster

jobs

site

resulting

in

its

high

relevance

-LRB-

similarity

-RRB-

score

to

PubMed

-LRB-

high

biased

focus

value

-RRB-

To

validate

the

quality

of

source-biased

service

evaluation

we

next

randomly

selected

sources

from

the

newsgroup

collection

to

evaluate

against

the

entire

set

of

newsgroups

We

compared

the

three

probers

Source

Bias

Query

Bias

and

No

Bias

For

each

of

the

sources

we

measured

relevance

-LRB-

similarity

-RRB-

precision

as

the

percentage

of

the

ranked

target

services

that

are

considered

relevant

to

the

source

using

Cosine

f

ocus

-LRB-

-RRB-

Relevance

judgments

were

determined

by

the

consensus

opinion

of

three

volunteers

Figure

shows

the

precision

for

the

three

probers

after

extracting

documents

per

target

service

Source

Bias

results

in

the

highest

precision

in

nine

of

ten

cases

tying

with

the

next

best

prober

in

only

two

cases

For

the

lone

failure

Source

Bias

does

succeed

after

extracting

documents

indicating

that

the

mistake

may

be

attributable

to

the

error

inherent

in

probing

very

few

documents

In

general

the

average

precision

of

the

source-biased

prober

is

nearly

double

that

of

the

next

best

prober

In

Figure

we

show

the

average

precision

for

the

ten

sources

when

increasingly

more

documents

are

extracted

per

target

The

source-biased

approach

displays

higher

precision

than

both

Documents

Examined

Relevance

Precision

No

Bias

Query

Bias

Source

Bias

Figure

Average

Relevance

Precision

the

query-biased

and

unbiased

probers

in

all

cases

considered

especially

when

based

on

very

few

documents

Identifying

Interesting

Relationships

The

third

set

of

experiments

is

designed

to

evaluate

the

effectiveness

of

using

the

source-biased

framework

to

support

the

identification

of

interesting

inter-service

relationships

that

the

alternative

schemes

do

not

Unlike

the

query-biased

and

unbiased

probers

the

asymmetric

nature

of

source-biased

probing

allows

us

to

characterize

the

nature

of

the

relationship

beyond

the

single

relevance

ranking

using

biased

focus

measure

We

first

illustrate

relationship

sets

for

PubMed

over

the

web

collection

In

Table

we

show

four

classes

of

relationship

sets

for

high

low

and

dif

f

using

the

source-biased

prober

described

above

Again

we

note

that

our

interest

here

is

to

illustrate

the

power

of

the

formalation

we

leave

the

optimization

of

values

to

future

work

In

contrast

to

the

simple

relevance

ranking

in

Table

we

see

how

the

source-biased

framework

can

differentiate

between

the

very

similar

services

-LRB-

the

equivalent

sites

-RRB-

and

the

more

general

services

-LRB-

the

superset

sites

-RRB-

relative

to

PubMed

In

addition

we

can

identify

sites

with

some

common

data

-LRB-

the

overlap

sites

-RRB-

and

sites

concerned

with

significantly

different

topics

-LRB-

the

complement

sites

-RRB-

Similarly

we

show

in

Table

several

interesting

relationships

derived

from

the

newsgroup

collection

for

high

low

and

dif

f

using

the

Source

Bias

prober

discussed

before

Again

by

relying

on

BASIL

's

source-biased

analysis

we

may

characterize

relationships

sets

for

each

source

As

an

example

we

identify

sci.physics.particle

as

a

member

of

the

subset

relationship

set

of

the

mixed

topic

newsgroup

which

consists

of

physics-related

articles

in

addition

to

articles

on

backgammon

juggling

and

telecommunications

Interestingly

we

can

see

that

there

are

several

overlapping

relationships

between

newsgroups

in

related

but

slightly

different

fields

-LRB-

e.g.

volleyball

and

cricket

-RRB-

Finally

we

also

identify

several

unrelated

newsgroups

including

comp.sys.mac.system

relative

to

misc.immigration.usa

Probing

with

Focal

Terms

In

our

final

set

of

experiments

we

consider

the

impact

of

focal

term

probing

on

the

success

rate

of

source-biased

probing

We

evaluate

four

flavors

of

focal

term

probing

with

and

focal

term

groups

from

which

to

draw

source-biased

probes

In

our

initial

experiments

with

focal

term

probing

we

discovered

that

there

was

little

impact

on

either

the

efficiency

of

probing

or

the

quality

of

target

service

evaluation

when

considering

sources

from

the

single-topic

newsgroup

collection

-LSB-

Due

to

space

limitations

we

omit

these

results

here

-RSB-

Documents

Examined

Average

Source

Similarity

Original

Focal

Focal

Focal

Focal

Figure

Impact

of

Focal

Term

Probing

In

contrast

we

discovered

that

focal

term

probing

had

a

significant

impact

when

used

on

mixed

topic

newsgroups

in

which

there

are

documents

from

several

unrelated

single

topic

newsgroups

In

Figure

we

show

the

probing

efficiency

for

the

four

focal

term

source-biased

probers

relative

to

the

best

basic

source-biased

prober

for

source-target

pairs

from

the

newsgroup

collection

In

each

case

the

sources

were

drawn

exclusively

from

the

mixed

topic

newsgroups

All

of

the

focal

term

techniques

resulted

in

more

efficient

probing

versus

basic

source-biased

probing

and

only

minor

differences

in

ranking

precision

and

relationship

set

generation

quality

indicating

that

focal

term

probing

can

be

advantageous

in

certain

circumstances

Our

intuition

is

that

identifying

focal

terms

is

considerably

more

important

in

cases

in

which

there

are

clear

distinctions

in

term

distributions

as

would

be

reflected

in

the

mixed

topic

newsgroups

in

which

several

groups

of

documents

are

concerned

with

different

topics

Researchers

have

previously

explored

different

aspects

of

the

service

discovery

problem

ranging

from

discovery

in

a

federated

environment

-LSB-

-RSB-

to

identifying

services

that

meet

certain

quality-of-service

guarantees

-LSB-

-RSB-

to

evaluating

services

based

on

a

distributed

reputation

metric

-LSB-

-RSB-

to

other

quality

metrics

like

in

-LSB-

-RSB-

In

contrast

we

focus

on

the

data

relationships

between

services

to

power

efficient

discovery

and

ranking

Other

researchers

have

previously

studied

the

problem

of

re-peatedly

querying

an

unknown

database

in

an

effort

to

generate

a

summary

of

the

database

internals

-LSB-

-RSB-

The

main

purpose

of

these

techniques

is

to

generate

a

representative

content

summary

of

the

underlying

database

Querying

methods

suggested

include

the

use

of

random

queries

queries

learned

from

a

classifier

and

queries

based

on

a

feedback

cycle

between

the

query

and

the

response

More

recently

Gravano

et

al.

-LSB-

-RSB-

have

introduced

an

extension

to

the

Callan-style

probing

technique

that

relies

on

a

learned

set

of

queries

for

database

classification

Their

probing

method

is

effective

for

classifying

web

sites

into

a

pre-determined

Yahoo!

style

hierarchy

but

requires

the

potentially

burdensome

and

inflexible

task

of

labelling

training

data

for

learning

the

classifier

probes

in

the

first

place

Additionally

if

new

categories

are

added

or

old

categories

removed

from

the

hierarchy

new

probes

must

be

learned

and

each

source

re-probed

Previous

research

on

grouping

terms

-LRB-

as

in

our

source-biased

probing

with

focal

terms

-RRB-

has

focussed

on

finding

terms

that

are

effective

for

query-expansion

-LSB-

-RSB-

or

finding

similar

terms

-LSB-

-RSB-

Our

focal

term

formulation

is

similar

to

that

used

in

-LSB-

-RSB-

though

their

goal

is

to

find

close

semantic

relationships

between

terms

unlike

our

coarse-grained

groupings

Table

Source-Biased

Analysis

Identifying

Relationships

Relative

to

PubMed

Service

-LRB-

S

-RRB-

URL

Description

focus

P

M

-LRB-

S

-RRB-

focus

S

-LRB-

P

M

-RRB-

Relationship

WebMD

Health/Medical

equivalent

AMA

Health/Medical

equivalent

HealthAtoZ

Health/Medical

equivalent

Open

Directory

dmoz.org

Web

Directory

superset

Google

Web

Search

Engine

superset

About

Web

Channels

superset

Monster

Jobs

overlap

Mayo

Clinic

Health/Medical

overlap

Silicon

Investor

Finance

complement

Usenet

Recipes

Recipes

complement

Table

Source-Biased

Analysis

Identifying

Relationships

in

the

Newsgroup

Collection

A

B

focus

A

-LRB-

B

-RRB-

focus

B

-LRB-

A

-RRB-

Relationship

comp.sys.mac.apps

comp.sys.mac.system

equivalent

comp.sys.mac.system

comp.sys.mac.advocacy

equivalent

sci.physics.particle

sci.physics

equivalent

sci.physics.particle

subset/superset

comp.unix.misc

subset/superset

rec.sport.volleyball

rec.sport.cricket

overlap

rec.games.go

rec.games.chess.misc

overlap

rec.crafts.textiles.sewing

comp.lang.perl.misc

complement

comp.sys.mac.system

misc.immigration.usa

complement

In

this

paper

we

have

presented

a

novel

web

service

discovery

and

ranking

prototype

called

BASIL

that

supports

a

personalized

view

of

data-intensive

web

services

through

source-biased

focus

BASIL

supports

personalized

discovery

requests

and

relevance

reasoning

through

efficient

source-biased

probing

and

source-biased

relevance

metrics

Concretely

we

have

shown

that

BASIL

allows

us

to

determine

in

very

few

interactions

whether

a

target

service

is

relevant

to

the

source

service

by

probing

the

target

with

very

precise

probes

The

biased

focus

measure

allows

us

to

evaluate

and

rank

the

services

discovered

and

to

identify

interesting

types

of

source-biased

relationships

for

a

collection

of

services

Additionally

we

have

introduced

source-biased

probing

with

focal

terms

as

a

performance

optimization

to

further

improve

the

effectiveness

of

the

basic

source-biased

algorithm

-LSB-

-RSB-

Amazon.com

Amazon.com

Web

Services

-LSB-

-RSB-

Ariba

-LSB-

-RSB-

J.

Callan

M.

Connell

and

A.

Du

Automatic

discovery

of

language

models

for

text

databases

In

SIGMOD

-LSB-

-RSB-

J.

P.

Callan

and

M.

E.

Connell

Query-based

sampling

of

text

databases

Information

Systems

-LRB-

-RRB-

-LSB-

-RSB-

J.

Caverlee

L.

Liu

and

D.

Rocco

Discovering

and

ranking

web

services

with

BASIL

A

personalized

approach

with

biased

focus

Technical

report

GIT

-LSB-

-RSB-

W.

W.

Cohen

and

Y.

Singer

Learning

to

query

the

web

In

AAAI

Workshop

on

Internet-Based

Information

Systems

-LSB-

-RSB-

Google

Google

Web

APIs

FAQ

-LSB-

-RSB-

D.

Hawking

and

P.

Thistlewaite

Methods

for

information

server

selection

ACM

Transactions

on

Information

Systems

-LRB-

-RRB-

-LSB-

-RSB-

IBM

Web

Services

for

Life

Sciences

-LSB-

-RSB-

IBM

IBM

UDDI

Business

Registry

-LSB-

-RSB-

P.

G.

Ipeirotis

L.

Gravano

and

M.

Sahami

Probe

count

and

classify

Categorizing

hidden-web

databases

In

SIGMOD

-LSB-

-RSB-

P.

G.

Ipeirotis

L.

Gravano

and

M.

Sahami

QProber

A

system

for

automatic

classification

of

hidden-web

databases

ACM

TOIS

-LRB-

-RRB-

-LSB-

-RSB-

Y.

Liu

A.

H.

Ngu

and

L.

Zeng

QoScomputation

and

policing

in

dynamic

web

service

selection

In

WWW

-LSB-

-RSB-

W.

Meng

C.

T.

Yu

and

K.-L

Liu

Detection

of

heterogeneities

in

a

multiple

text

database

environment

In

CoopIS

-LSB-

-RSB-

Microsoft

NET

-LSB-

-RSB-

Microsoft

Microsoft

UDDI

Business

Registry

Node

-LSB-

-RSB-

National

Center

for

Biotechnology

Information

NCBI

BLAST

-LSB-

-RSB-

M.

P.

Papazoglou

Service-oriented

computing

Concepts

characteristics

and

directions

In

WISE

-LSB-

-RSB-

M.

F.

Porter

An

algorithm

for

suffix

stripping

Program

-LRB-

-RRB-

-LSB-

-RSB-

ProFusion

-LSB-

-RSB-

Y.

Qiu

and

H.-P

Frei

Concept-based

query

expansion

In

SIGIR

pages

Pittsburgh

US

-LSB-

-RSB-

G.

Salton

and

C.

Buckley

Term-weighting

approaches

in

automatic

text

retrieval

In

Readings

in

Information

Retrieval

Morgan

Kauffman

San

Francisco

CA

-LSB-

-RSB-

G.

Salton

A.

Wong

and

C.

Yang

A

vector

space

model

for

automatic

indexing

CACM

-LRB-

-RRB-

-LSB-

-RSB-

H.

Schutze

and

J.

O.

Pedersen

A

cooccurrence-based

thesaurus

and

two

applications

to

information

retrieval

Information

Processing

and

Management

-LRB-

-RRB-

-LSB-

-RSB-

K.

Sivashanmugam

K.

Verma

and

A.

Sheth

Discovery

of

web

services

in

a

federated

registry

environment

In

ICWS

-LSB-

-RSB-

R.

M.

Sreenath

and

M.

P.

Singh

Agent-based

service

selection

Journal

on

Web

Semantics

-LRB-

JWS

-RRB-

-LSB-

-RSB-

A.

Sugiura

and

O.

Etzioni

Query

routing

for

web

search

engines

Architecture

and

experiments

In

WWW

-LSB-

-RSB-

UDDI

-LSB-

-RSB-

Working

Group

Web

Services

Architecture

February

-LSB-

-RSB-

W.

Wang

W.

Meng

and

C.

Yu

Concept

hierarchy

based

text

database

categorization

in

a

metasearch

engine

environment

In

WISE

-LSB-

-RSB-

J.

Xu

and

W.

B.

Croft

Query

expansion

using

local

and

global

document

analysis

In

SIGIR

pages

-LSB-

-RSB-

L.

Zeng

B.

Benatallah

M.

Dumas

J.

Kalagnanam

and

Q.

Z.

Sheng

Quality

driven

web

services

composition

In

WWW

focal terms
web service discovery
ranking
biased discovery

query-biased probing
source-biased probing
data-intensive services
data-intensive web services