A

Core

Ontological

Model

for

Semantic

Sensor

Web

Infrastructures

ABSTRACTSemantic

Sensor

Web

infrastructures

use

ontology-based

models

to

represent

the

data

that

they

manage

ontologies

(i.e.,

machine-processable

formal

models)

that

support

data

description,

discovery

Built

upon

the

huge

quantities

of

live

and

and

integration.

archived

sensor

data

available

in

the

Web,

the

The

inclusion

of

ontologies

into

Sensor

so-called

Sensor

Web

(Botts,

Percivall,

Reed,

Web

infrastructures

has

led

these

infrastructures

Davidson,

the

Semantic

Sensor

Web

to

evolve

into

Semantic

Sensor

Web

ones,

i.e.,

is

a

framework

for

providing

enhanced

mean-software

infrastructures

capable

of

processing

ing

for

sensor

observations

so

as

to

increase

sensor

data

that

are

modeled

with

ontologies

interoperability

(Sheth,

Henson,

Sahoo,

and

distributed

on

the

Web.

(e.g.,

the

Observations

and

Measurements

(Cox

et

al.,

and

the

Sensor

Model

Language

(Botts

Robin,

are

frequently

used

in

sensing

infrastructures

and

have

sometimes

been

transformed

into

ontological

models.

Furthermore,

the

recent

development

of

the

Semantic

SensorNetwork(SSN)

ontology

(Lefort

et

al.,

will

push

forward

the

use

ofcommonontologicalmodelsforrepresenting

sensor

networks..

However,

current

Semantic

Sensor

Web

infrastructures

have

furtherrepresentational

requirements

thanjustmodellingsensornetworks.

This

is

because

sensor

data

are

distributed,

which

requires

being

aware

of

the

different

sensor

datasets

available;

are

heterogeneous,

which

requires

knowing

the

schemas

used

in

these

datasets;

and

are

web-accessible,

which

requires

having

descriptions

of

the

web

services

that

provide

dataset

access..

Nevertheless,

of

the

data

models

proposed

fully

covers

these

requirements;

they

are

partially

covered

in

some

models

but

some

requirements

have

not

been

dealt

with

yet

(i.e.,

dataset

schema

descriptions)..

Furthermore,

in

addition

to

the

types

of

data

just

mentioned,

Semantic

Sensor

Web

infrastructures

need

to

manage

further

data

(e.g.,

geographical,

organizational,

or

domainspecific

data);

thus,

one

of

the

dilemmas

that

developers

face

is

to

know

which

is

the

most

appropriate

way

to

model

domain-specific

features

of

interest

and

their

properties,

so

that

they

can

link

domain

data

with

sensor

data..

The

main

contribution

of

this

paper

is

the

definition

of

a

core

ontological

model

for

Semantic

Sensor

Web

infrastructures

that

allows

describing

sensor

networks

(by

extending

the

SSN

ontology),

sensor

data

sources

with

their

underlying

schemas,

and

the

web

services

that

these

sources

expose.

This

core

ontological

model

provides

a

shared

vocabulary

to

discover,

access,

and

integrate

information

within

the

infrastructure

and

to

interoperate

both

across

the

internal

infrastructure

services

and

between

an

infrastructure

and

the

external

sources

that

adopt

alternative

approaches

(e.g.,

the

OGC

Sensor

Web

Enablement

ones)

(Botts

et

al.,

Asecond

contribution

is

a

set

of

guidelines

to

model

domain-specific

features

of

interest

and

properties;

these

guidelines

are

based

on

frequent

requirements

and,

in

contrast

to

the

current

practice,

they

encourage

to

take

advantage

of

the

expressiveness

of

the

underlying

logical

formalism

and

to

add

logical

constraints

to

the

model..

As

an

example

of

application,

the

core

ontological

model

is

currently

being

used

in

a

Semantic

Sensor

Web

infrastructure

that

is

being

applied

to

a

use

case

in

the

domain

of

coastal

flood

emergency

planning

(Gray

et

al.,

This

development

required

us

to

define

a

set

of

ontologies

to

represent

those

data

that

are

specific

to

that

domain,

and

to

this

end

the

mentioned

guidelines

have

been

applied..

As

any

other

engineered

product,

ontologies

may

have

defects;

some

of

these

defects

may

appear

duringtheirdevelopment,

butothers

only

emerge

once

they

have

been

deployed.

The

ontologies

described

in

this

paper

have

been

evaluated

through

different

aspects

(i.e.,

vocabulary,

syntax,

structure,

semantics,

representation,

and

context)

to

ensure

their

suitability

for

their

intended

use

scenario;

in

this

paper

we

present

the

details

of

such

an

evaluation

to

guide

future

evaluation

efforts.

This

paper

is

structured

as

follows.

First,

it

presents

the

use

case

in

the

coastal

flood

emergency

planning

domain

where

the

ontologies

have

been

used.

Second,

it

gives

an

overview

of

the

ontological

model

and

introduces

the

different

modules

that

compose

it.

Then

it

describes

the

ontology

modules

that

deal

with

infrastructure

data,

that

is,

an

extension

of

the

SSN

ontology

and

modules

for

representing

web

services,

datasets,

and

dataset

schemas.

The

following

section

presents

the

guidelines

to

model

domain

specific

features

of

interest

and

properties

along

with

the

domain-specific

ontology

modules.

Then,

it

describes

the

evaluationperformed

overthe

ontological

model

and

compares

our

model

with

other

models

used

in

sensing

infrastructures.

Finally,

itprovides

some

conclusions

derived

from

this

work.

This

paper

presents

an

ontological

model

that

can

be

fully

or

partially

reused

in

Semantic

Sensor

Web

infrastructures;

by

reusing

upper

ontologies,

we

aim

to

facilitate

interoperability

between

such

infrastructures.

Additionally,

the

separation

between

the

infrastructure

and

the

domain

ontologies

contributes

to

making

the

ontological

model

modular

and

extensible..

The

use

of

the

ontological

model

in

a

different

domain

or

use

case

should

not

entail

major

problems,

since

our

design

principles

have

been

to

reuse

ontologies

and

data

models

as

much

as

possible

and

to

modularize

the

ontologies

developed

so

as

to

facilitate

the

reuse

or

replacement

of

ontology

modules..

While

some

of

the

ontologies

are

specific

to

the

coastal

flood

warning

scenario

(e.g.,

Role),

those

related

to

the

infrastructure

are

generic.

Thus,

the

ontological

model

can

be

adapted

to

other

situations

by

replacing

the

domain

ontologies.

Note

that

if

an

ontology

exists

for

the

new

application

domain,

then

to

replace

the

domainontologies

with

anontologyforthe

new

domainis

a

straightforward

process.

Otherwise,

a

new

ontology

would

have

to

be

developed.

This

development

requires

adomainexpertwho

knows

the

terminology

and

relationships

of

the

concepts

in

the

domainwho

in

turn

should

work

with

an

ontology

engineer..

In

the

paper

we

present

a

static

evaluation

of

the

ontologies

developed.

Clearly,

a

complete

evaluation

would

also

require

performing

dynamic

evaluations

over

a

running

system

using

the

ontologies

(e.g.,

to

analyze

the

effect

of

the

ontology

structure

in

query

complexity

or

efficiency)..

The

evaluation

results

unveiled

some

problems

(or

potential

ones)

in

the

ontologies

that

were

later

fixed.

It

is

also

important

to

remark

the

important

role

of

evaluation

in

the

ontology

developmentprocess

since

sometimes

problems

in

the

ontologies

may

not

arise

from

the

same

process

but

from

other

factors,

such

as

changes

in

the

ontologies

reused,

as

it

occurred

in

our

case..

However,

ontology

evaluation

is

not

a

straightforward

task.

Even

if

different

ontology

evaluation

methods

are

proposed

in

the

literature,

applying

them

requires

manual

intervention

minimally

supported

by

software

tools.

Therefore,

we

would

like

to

remark

the

need

for

automation

of

ontology

evaluation

activities

by

means

of

software

ontology

evaluation

frameworks

that

facilitate

evaluation

and

enable

advanced

evaluation

scenarios

(e.g.,

regression

evaluations)..

Furthermore,

existing

ontology

evaluation

techniques

are

domain

independent;

however,

it

would

also

be

desirable

to

have

domainspecific

ontology

evaluation

techniques

and

guidelines.

An

example

of

such

a

technique

in

the

domain

of

sensor

networks

would

be

one

that

allows

evaluating

whether

the

SSN

ontology

is

properly

being

used

in

those

ontologies

that

reuse

or

extend

it.

Different

proposals

have

been

made

for

designing

sensor

network

infrastructures

to

integrate

data

coining

from

different

sources.

Sensor

network

infrastructures

follow

different

approaches

to

represent

data

some

of

them

use

database

models,

e.g.,

SenseWeb

(Kansal,

Nath,

Liu,

Zhao,

others

use

XML

schemas

IrisNet

(Gibbons,

Karp,

Ke,

Nath,

Seshan,

Hourglass

(Shneidman

et

al.,

SemSOS

(Henson,

Pschorr,

Sheth,

Thirunarayan,

and

SANY

(Klopfer

Simonis,

and

some

others

use

ontological

models

REASON

(Jabeur,

McCarthy,

Xing,

Graniero,

and

SWAP

(Moodley,

Knowing

the

concrete

schemas

that

these

infrastructures

use

to

represent

their

data

is

not

apparent

in

the

literature

because

scarce

details

on

these

schemas

are

provided

and

because

their

definitions

are

not

available.

Even

if

this

lack

of

information

may

be

due

to

the

fact

that

these

models

are

considered

as

implementationspecific

information,

our

viewpoint

is

that

having

details

about

such

models

can

facilitate

data

reuse

and

interoperability..

Similarly

to

our

approach,

other

existing

sensor

network

infrastructures

manage

data

to

represent

sensor

networks

and

their

spatio-temporal

characteristics,

including

information

to

support

the

discovery

and

integration

of

sensor

information

(mainly

through

registries).

However,

no

data

model

has

been

proposed

yet

for

representing

every

type

of

data

required

by

the

current

Semantic

Sensor

Web

infrastructures..

Depending

on

the

intended

use

of

the

infrastructures,

some

types

of

data

are

covered

or

not;

for

example,

dataset

descriptions

are

only

used

in

SANY,

SemSOS,

SenseWeb,

and

SWAP,

whereas

service

descriptions

are

only

used

in

Hourglass

SANY,

and

SWAP..

One

type

of

data

that

we

have

not

found

in

these

infrastructures,

and

that

we

take

into

account

in

our

model,

is

that

about

the

schemas

used

in

the

different

data

sources

to

represent

their

underlying

data,

which

are

needed

for

integrating

data

from

heterogeneous

data

sources

(e.g.,

sensor

data

streams,

relational

databases

or

XML

data).

In

the

infrastructures

mentioned,

this

integrationis

performedby

different

components

responsible

for

converting

data

to

a

common

model.

However,

in

our

case

integration

is

managedby

a

specific

integration

service

that

requires

information

about

the

underlying

data

schemas

(Calbimonte,

Corcho,

Gray,

Sensor

network

infrastructures

have

been

extensively

applied

to

environmental

monitoring

(Hart

Martinez,

mainly

in

the

field

of

risk

monitoring

and

management;

these

infrastructures

have

been

used

in

different

domains

such

as

volcanic

eruptions

(WernerAllen,

Johnson,

Ruiz,

Lees,

Welsh,

forest

fires

(Terhorst

et

al.,

agricultural

risks

(Panchard,

Rao,

Prabhakar,

Hubaux,

Jamadagni,

or

flood

events

(Baptista,

Chang

Guo,

Zhou

De

Roure,

Smith

et

al.,

Sunkpho

Ootamakorn,

which

is

our

case..

The

use

of

ontologies

to

model

domain

data

in

these

infrastructures

is

quite

recent

(Asuncion

et

al.,

Tanasescu

et

al.,

Terhorst

et

al.,

McCarthy

et

al.,

Klopfer

Simonis,

and

the

models

defined

in

each

infrastructure

are

different

and

difficult

to

reuse.

This

is

mainly

so

because

the

use

cases

implemented

overthese

sensing

infrastructures

are

quite

heterogeneous

and

their

end

users

work

under

a

set

of

specific

regulations

(e.g.,

the

Flood

and

Water

Management

Act

in

the

UK,

which

is

our

case,

or

the

Andalusian

Regional

Ministry

of

Environment

in

Spain)

(Asuncion

et

al.,

that

are

completely

different

from

one

scenario

to

another..

Nevertheless,

the

principles

for

modelling

domain-specific

features

of

interest

and

properties

in

these

infrastructures

are

similar

and,

since

the

appearance

of

the

Observations

and

Measurements

model

(Cox

et

al.,

all

the

approaches

are

based

on

it.

All

these

models

usually

include

feature

and

property

categories

(i.e.,

hierarchies),

and

the

only

noticeable

features

in

them

are

the

absence

of

the

rich

expressivity

that

the

ontology

language

permits

and

of

the

representation

of

links

between

feature

categories

and

properties.

These

absences

canbe

justified

by

the

lack

of

requirements

that

demand

them

in

the

corresponding

use

scenarios;

however,

we

have

also

included

recommendations

for

them

in

the

guidelines

presented..

Another

remarkable

fact

is

that

only

one

of

these

infrastructures

has

reused

existing

ontologies

during

development

(the

AFIS

system

reuses

the

SWEET

ontologies)

(Terhorst

et

al.,

In

our

opinion,

the

development

of

models

for

Semantic

Sensor

Network

infrastructures

should

be

guided

by

reuse,

which

has

been

our

approach

while

developing

the

ontologies

presented

in

this

paper.

We

think

that

the

Semantic

Sensor

Network

ontology

proposed

in

the

(Lefort

et

al.,

will

fill

an

important

gap

here

by

allowing

the

interchange

of

cross-domainsensordatabetween

infrastructures.

O O O O O B I I 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 B O O O O O O O O O O O B O B O O O O O O O O O O O O O O O B I I O O O B I I O O O O O O O O O O O O O O O B 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 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 B O O O O O O O O O O B O O O B I I O O O O O O O O B 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 B I I O O O O O O O O O O O O O O O O O O O O O O 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 B I I O O O O B O O O O O B 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 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 B I I O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 I I O O O O 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 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 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 B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 B 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 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 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 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 B 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 B O O O O O O B 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 B 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 B 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 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 O O O O O O B O O O O O O B 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 B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 B O B O O O O O O O O O O O O O O O O O O O O O


Advances on sorting by reversals The problem of sorting signed permutations by reversals is inspired by genome rearrangement problems in computational molecular biology. Given two genomes represented as signed permutations of the same elements (e.g. orthologous genes), the problem consists in finding a most parsimonious scenario of reversals that transforms one genome into the other. Following the first polynomial solution of this problem, several improvements, simplifications, generalizations, tutorials or surveys have been published on the subject. While the reversal distance problem-i.e. the problem of computing the minimum number of reversals in a sorting sequence, without giving the sequence itself-seems to be well explored, the problem of giving a scenario realizing the distance still raises some open questions, one of which by Ozery-Flato and Shamir about whether an algorithm with subquadratic time complexity could ever be achieved for solving the problem. We give a positive answer to this question by describing an algorithm of time complexity O(n 3/2 √ log n). The problem of sorting a permutation by reversals is inspired and motivated by comparative genomics. Given two or more ordered sets of genes, or other markers on a genome, biologists use methods that solve this problem to estimate an evolutionary distance (number of global mutation events) between genomes, to infer the localization of evolutionary breakpoints along a DNA molecule, or to reconstruct ancestral genomes.. Genome rearrangements such as reversals may change the order of the genes in a genome, and also the direction of transcription. We identify the genes with the integers 1, . . . , n, with a plus or minus sign to indicate their direction. The order and direction of genomic markers will be represented by a signed permutation of {1, . . . , n}, that is a bijective function over [−n, n]\{0} such that −i = − i , where i = (i).. To simplify exposition, we adopt the usual extension which consists in adding 0 = 0, and n+1 = n + 1 to the permutation. We usually denote a signed permutation by simply writing (0 1 . . . n n + 1). The identity permutation (0 1 . . . n + 1) is denoted by Id. The inverse permutation −1 of is the (signed) permutation such that · −1 = Id.. The reversal of the interval If 1 , . . . , k is a sequence of reversals, we say that it sorts a permutation if · 1 · · · k = Id. The length of a smallest sequence of reversals that sorts is called the reversal distance of , and is denoted by d( ).. The problem of sorting by reversals has been the subject of an extensive literature. For an introduction and a general survey-oriented more specifically towards the distance computation problem-see [3] . The first polynomial algorithm was given by Hannenhalli and Pevzner [4] , and took O(n 4 ) time to give a sequence of reversals that optimally sorts a signed permutation. After many subsequent improvements on the running time, the currently fastest algorithms are those of Kaplan et al. [5] , and another by Berman and Hannenhalli [2] , both running in O(n 2 ). Bader et al. [1] designed a linear time algorithm for computing d( ), without giving the sequence of reversals. Noting that the most costly part of the algorithms that give a sequence is to check for very unfrequent configurations, Kaplan and Verbin [6] presented a random algorithm which runs in O(n 3/2 √ log n), and gives, most of the time, an optimal sequence of reversals, but fails with very high probability on some precise permutations. In a recent paper [7] , Ozery-Flato and Shamir compiled and compared the best algorithms, and wrote that: "A central question in the study of genome rearrangements is whether one can obtain a subquadratic algorithm for sorting by reversals".. In this paper, we give a positive answer to Ozery-Flato and Shamir's question. Inspired by the relations between the classical approaches [2, 4, 5] , the data structure given in [6] , and some remarks we first made in [9] , we describe how it is possible to bypass the usual costly tests and give an optimal solution in time O(n 3/2 √ log n). Sorting a signed permutation by reversals involves two successive procedures on what is called the overlap graph of . The first one consists in transforming the overlap graph such that all its connected components become oriented. This can be done in linear time [1, 3] . The computational bottleneck of the sorting by reversals problem occurs in the second procedure: sorting the oriented components. This paper addresses this problem.. In the next section, we define overlap graphs and basic operations on them. We next state, in a graph theoretical framework, a theorem by Hannenhalli and Pevzner that is the cornerstone of all sorting strategies. In Section 3, we give a new proof of this theorem that yields a constructive algorithm to sort oriented components without testing beforehand the safety of a reversal. However, with any classical data structure, such as vector arrays to represent permutations, the time complexity of this new algorithm is still O(n 2 ), even if this bound is achieved much more naturally. In Section 4, we show that the data structure introduced in [6] can be adapted to achieve an overall subquadratic time complexity.

O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Collecting spectral data over cropland vegetation using machine-positioning versus hand-positioning of the sensor The paper describes an all-terrain motorized platform for deploying sensors and compares data collected by means of that system with those collected by means of hand-held sensors. The results not only highlight the greater variability in spectra that can be expected when deploying field radiometers by hand but, more importantly, they quantify the difference. Researchers should be aware of the potential for diminishing the validity of findings based on reflectance spectra acquired by means of hand-held sensors. In a review article dealing with the principles of field spectroscopy, Milton (1987) suggested several guidelines for collecting data in the field by means of a radiometer or spectroradiometer. Four of those suggestions are: (1) use a mast or tripod to ensure a fixed geometry between the sensor, the standard panel and the target if at all possible; (2) ensure that the sensor is at least 1 m (preferably 2 m) above the upper surface of the target; (3) unless variations in reflectance factor with azimuth are being studied, be consistent in always orientating the sensor horizontal support and positioning the other field equipment (including people) in the same positions relative to the Sun; and (4) operators should wear dark clothing and kneel some distance away from the target during measurements. The sum total of these selected recommendations is that there are inconsistencies and problems associated with a researcher physically holding and positioning a field radiometer during data collection. The solution, of course, is to deploy spectral-measurement equipment by means of a mast, tripod, or truck-mounted (or similar) boom. While it may be intuitively obvious that collecting spectral data with hand positioning is less systematic than positioning with a boom or similar, and therefore, the spectra may be more variable and less precise than those made using a mast or tripod, there is little or no information about the nature and/or quantitative extent of the difference.. Over the years, scientists have explored a variety of ways of deploying spectral-sensing and other similar devices in field settings. The approaches include holding the sensor with the human hand (e.g., Stark and Gitelson, 2000) , attaching the instruments to hand-held masts (e.g., Price et al., 1993) , or mounting them on a variety of motorized booms (e.g., Rundquist et al., 1995) . Our instrument-deployment approach is focused on "Goliath," a motorized, all-terrain platform ( Fig. 1) for positioning numerous types of scientific instruments (Rundquist et al., 2001) .. The pertinent characteristics of the Goliath platform include a ground clearance of 1.91 m, a width of 2.59 m, and wheels positioned for movement through crops with 76.2 cm rows, a common width for row crops in Nebraska, USA. The tracked wheels allow access to difficult landscape positions and/or muddy fields. The working platform constructed on Goliath is 2.62 m × 2.95 m. It includes a seat for the driver along with four seats and desks for scientists and/or technicians. The hydraulic boom is 3.8 m long when stowed, but can be extended to 12 m. It can be raised to a height of 10.5 m, and rotated through 360° around the platform. The boom can be lowered nearly to ground level to accommodate either sensor attachment/adjustments or calibration by means of a reference panel. The maximum sensor payload for the boom, at full extension, is 13.3 kg. A metal frame at the end of the boom allows for easy attachment of sensors/instruments. It was fitted with small motors to facilitate movement of instruments, including from nadir to off-nadir positions.. Goliath allows one to easily address Milton's concern regarding fixed geometry, as the instrument position is repeatable from one sample site to the next. Once the boom height and viewing angle are set on the Goliath platform, the sensor-deployment configuration is rigid; thus, the field radiometer is oriented in exactly the same manner from one spectral sample to the next, across an entire field or study site. With regard to Milton's second concern, the hydraulic boom on Goliath can achieve a height of 10.5 m above the ground. The machine can obviously exceed the distance limitations experienced by a person attempting to hand-hold a sensor above a vegetation canopy. Again, once the vertical distance is set, it remains the same throughout a given data-collection campaign. Our approach in cropland vegetation, for example, is to maintain a consistent height above the canopy, and to collect the data from the nadir position. The hydraulic boom on Goliath can be rotated through 360°. Therefore, it is relatively easy to address Milton's third concern, and maintain the sensors in the principal plane of the sun, regardless of the orientation of the platform (e.g., with regard to the row directions in a field crop). Instruments are deployed via the Goliath boom; thus, there is no problem with people "contaminating" the spectral reflectance because of, for example, the color of one's clothing. The field researchers are stationed on the Goliath working platform, away from both the sensor and the target. Additionally, Goliath is painted flat-black.. The objective of our paper is to demonstrate quantitatively the differences one can expect when collecting spectral data using hand-held positioning of the sensor versus data collection with a machine-mounted sensor. The Goliath platform allows for deployment of sensors in a controlled manner with repeatable positioning from place to place in a field, and systematic data collection. This approach minimizes the variance in spectra caused when sensors are deployed by hand. A pronounced quantitative difference between the two approaches was demonstrated. Results showed that when hand-held positioning of the sensor is used, one should expect a high variability in reflectance, and, as a result, error in estimating the biophysical characteristics of vegetation. Such error can prevent accurate estimation of vegetation status. The authors recommend that researchers avoid collecting spectral data in the field using hand-pointed instruments. Although most investigators will not have access to a stable platform, such as Goliath, field radiometers should, at the very least, be attached to a rigid mast and deployed in a consistent manner.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Gaussian message passing-based cooperative localization on factor graph in wireless networks Location information has become attractive for a variety of applications in wireless networks. Cooperative localization was proposed to improve the performance in harsh environment where conventional localization methods failed due to the insufficient number of anchors. In this paper, distributed cooperative localization is studied based on message passing on factor graph. The joint a posteriori distribution of nodes' positions is represented by factor graph. Due to the nonlinearity between the positions and range measurement, the expressions of messages cannot be obtained in closed form by directly applying sum-product algorithm. Most existing methods resort to particle-based representation, which leads to both high computational complexity and large communication overhead. We propose to replace the factor node of the likelihood function by a linear Gaussian model. Accordingly, we are able to derive Gaussian messages on the revised factor graph, which only requires to update the mean vectors and the covariance matrices of multivariate Gaussian distributions. Simulation results show that the proposed method significantly outperforms the distributed maximum likelihood estimator and the extended Kalman filter, and performs very close to or even better than SPAWN estimator with much lower communication overhead and computational complexity in both static and mobile wireless networks. Location information has become an important feature in many wireless networks, and enabled a variety of applications, including navigation, tracking, monitoring, and emergency services [1] . Generally, the Global Positioning System (GPS) [2] is the most recognized technique for localization. However, localization using GPS may be failed in some harsh conditions, such as indoor environments, urban canyons, and tunnels. In addition, it can be costly and energy prohibitive to equip each device with a GPS receiver in some networks, especially in the large-scale wireless sensor networks [3] . Hence, only a few nodes called anchors in the networks have known positions, which will help the rest nodes called agents to be localized. Obviously, agents may fail to locate themselves due to the insufficient number of neighboring anchors available to them.. Recently, a location technique, namely, cooperative localization [4] has been introduced to enhance the conventional localization by enabling the cooperations among the agents through peer-to-peer communications. Each agent makes measurements with its neighboring nodes and exchanges information with them. The cooperative localization can work in harsh environment with insufficient anchors, which shows the advantage in sparse and limited-power networks, and improves the localization accuracy and reliability compared to the non-cooperative localization. Besides, the cooperative localization requires no additional infrastructure to be established, which makes it especially attractive in wireless ad hoc networks.. Many researches based on non-Bayesian estimators have been proposed for the range-based localization [5] [6] [7] [8] [9] [10] . A least-squares method is proposed for the source localization in [5] , where the iterative nonlinear methods, such as Newton-Raphson, Gauss-Newton and steepest descent, are used to tackle the nonlinear equation. However, these methods cannot guarantee the optimal results for the multimodal cost functions. In [6] , linear least-squares method is proposed, which converts the nonlinear equation to linear one by introducing an additional variable. However, it suffers from the ill-conditioned measurement matrix when the distances between an agent and its neighbors are closely identical. In [7] , the centralized maximum likelihood (ML) estimator is proposed to solve the relative location estimation in wireless sensor networks. The algorithm involves a multivariable nonlinear optimization problem that makes it difficult to implement in practice. To solve this problem, [8, 9] relax the ML estimation to a centralized semi-definite programming problem and a distributed second-order cone programming problem, respectively. With these relaxations, localization problem becomes a convex optimization [11] , which can be solved more efficiently.. All the above non-Bayesian estimators treat the positions of nodes to be located as unknown deterministic parameters and ignore their priors. However, the deterministic estimations of agents' positions by the non-Bayesian estimators cause the error accumulation when they act like anchors to help the neighboring agents in distributed cooperative localization, which leads to severe performance degradation. Therefore, the estimation uncertainties of agents' positions has to be considered when they act as reference nodes. To this end, Bayesian estimators [13] [14] [15] [16] [17] [18] [19] [20] are utilized to locate the agents, which treat the nodes' positions as random variables. The extended Kalman filter (EKF) and unscented Kalman filter (UKF) [12] are proposed for cooperative localization in [13] . Both the EKF and UKF interpret the effects of neighbors' position uncertainties as range measurement errors, which leads to performance loss. In [14], the particle filter is proposed for cooperative localization in hybrid GNSS-terrestrial environment, which is especially useful for nonlinear systems at the cost of a huge computational complexity. Message passing algorithms are studied for the cooperative localization in [15] [16] [17] [18] [19] , which are attractive for distributed processing. In [15], a nonparametric belief propagation (NBP) method is proposed for the self-localization in wireless sensor networks, which approximates messages by particles and the corresponding weights. However, the large numbers of particles leads to a huge computational complexity and large communication overhead. To reduce the communication overhead in NBP, a method using the approximations on the position beliefs by Gaussian mixtures and information censoring is proposed in [16] . In [17], a cooperative localization method based on the sum-product algorithm and factor graph [21] is proposed in wireless networks, which is named as SPAWN. However, directly applying SPAWN, especially computing messages on factor graph, becomes extremely difficult due to the nonlinearity between the positions and observations. Hence, approximations have to be made on the messages to ease the computational complexity. Note that, the message representations are closely related to the computational complexity and communication overhead in SPAWN, which has been studied in [18] . The particle-based method suffers both the high computational complexity and communication overhead, while the parametric method has a lower communication overhead, but may involve in parameters optimization by minimizing Kullback-Leilbler divergence [22] , which may lead to a huge computational complexity using numerical methods. Variational message passing estimator is proposed for the self-localization of agents in wireless sensor networks in [19] , which is a mean field solution to the estimation of the a posteriori distributions. However, the independence assumption of variables in the mean field solution tends to produce overly confident marginals [23] .. In this paper, we consider two-dimensional Bayesian cooperative localization in wireless networks, which can be extended to the three-dimensional scenarios. Due to the nonlinearities between the positions and range measurements, localization has become a complex nonlinear and multivariate optimization problem. Hence, the linearization on the Euclidean norm in the range measurement is employed. Factor graph representation of the linearized state-space model is given. Then, the Gaussian messages are derived and passed on factor graph, which requires the mean vectors and covariance matrices of the multivariate Gaussian distributions to be updated and transmitted. Numerical results show that the proposed algorithm outperforms the distributed ML estimator and the extended Kalman filter. It performs very close to or even better than particle-based SPAWN with much lower computational complexity and communication overhead.

O O O B I O B I O B I O O O O O O O O O O O B I B I O 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 I O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 I O O O O O O O O O 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 I O O O O O O O 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 I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 I O O O O O O O O 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 I O O 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 I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 I O O O O O O O B I O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 I O B I O O O O O O O O O O O O O O O O O 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 I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


RISE: A general simulation interoperability middleware container In recent years, new services on the Internet have enabled global cooperation; in particular, the Web has enabled new distributed simulation technology. Much research has been devoted to develop middleware interoperability methods on the Web. However, most existing methods have constraints in the structural rules that are placed on the design of middleware interoperability methods. For example, such constraints make it difficult to enhance interoperability via decoupling systems implementations and design, which is essential in open computing networks, as in the case of the Web. In order to achieve such objectives we present the RISE (RESTful Interoperability Simulation Environment) middleware. This all-purpose WS-based distributed simulation middleware decouples design and implementation while allowing composition scalability and dynamicity. Furthermore, it supports experiment-oriented frameworks and has the ability to put Web 2.0 services in the simulation loop. RISE is the first existing middleware to achieve such objectives, and the first to employ RESTful Web-services. We present the foundations for meeting the above objectives, and the distinct characteristics of RISE from existing Web-based approaches. Modeling and Simulation (M&S) has evolved to become a discipline that has its own knowledge, formalisms, and methodologies. At the heart of the M&S, technology is the model concept: a representation of a system with the purpose to promote understanding of that system. The second concept is simulation, which refers to the execution of those models with particular sets of data using a computing device [46] . As simulated systems become increasingly sophisticated, the simulation software becomes larger and more complex. In these cases, the resources provided by a singleprocessor machine often become insufficient to execute these systems. Parallel and distributed simulations deal with these issues by executing simulations over multiple processors [17] . Distributed Simulation (DS) is distinguished from parallel simulation by their physical architecture, communication network and latency. A focal point of distributed simulation software has been on how to achieve model reuse via interoperation of different simulation components (without relocating people/equipment to other locations). Other benefits include information hiding such as the protection of intellectual property.. With the expansion of the Internet, the desire toward global cooperation via the Web in the distributed simulation technology has also been on the rise as indicated by number of surveys such as [4, 37] . Consequently, much research has been devoted to develop middleware interoperability methods on the Web, particularly using the Simple Object Access Protocol (SOAP) based WebServices (WS) [30] (e.g. [15, 35, 42, 45] ), the High Level Architecture (HLA) standard [23] , or a combination of both (e.g. [6, 22, 48, 49] ).. The HLA is a distributed simulation architecture mainly used in the defense community since 1996 [23] . HLA-based simulations interact with each other via a common software layer (acting like a bus), called the Run-Time Infrastructure (RTI). On the other hand, SOAP-based WS provide a general interoperability framework on the Web, in which systems (i.e., simulation software) interact through the WS layer, using the Remote Procedure Call (RPC) mechanism. The WS layer transports those RPCs in the form of the Extensible Markup Language (XML) SOAP messages. However, such methods have constraints in the structural rules that are placed on the middleware design methods. In particular, the way they exchange, structure, and use the information is usually tied to internal software design issues, making it difficult to decouple the system implementations and their design. This path usually leads to the need for homogenizing different implementations, which is usually a complex problem to resolve, particularly, in open computing networks, as in the case of the Web. In such open communities, we need interoperating systems that are developed independently without being aware of each other internal software design issues [7] . Any number of systems is expected to be able to join/disjoin the overall distributed environment dynamically at runtime.. Current Web-based distributed simulation systems (particularly using SOAP WS), are mostly under the control of single team or a closed community, using interfaces that are tied to the implementation (e.g. [35, 42] ). On the other hand, hiding the implementations (where heterogeneity resides) enhances interoperability, allowing the systems to evolve independently from each other.. Based on the above, our main goal is to develop an all-purpose Web-services based distributed simulation middleware with the following objectives:. (1) The middleware will decouple design and implementation, while allowing composition scalability (i.e. any number of systems can join the distributed environment) and dynamicity (i.e. systems can be created and destroyed at runtime). The main motivation is hiding these internal details can improve interoperability. This problem has never been investigated before. The assumption has been that, if system A is able to perform distributed simulation on the Web using SOAP WS, there will be a straightforward way to interoperate it with another SOAPbased system. However, in reality, as interoperability is tied to implementation, combining systems developed independently is not trivial (in fact, it usually requires major software design refactoring). ( 2) The middleware must serve as a container of any of simulation environment. Therefore, it must be independent of any specific implementation, synchronization algorithms, semantics, and formalisms. This means that the simulation software should run one level above the middleware. Further, design scalability must be ensured. This means that the middleware design structure must scale up when adding/removing a supported simulation environment type. (3) The middleware should support Experiment-oriented frameworks. To do so, we provide Modeler-oriented Templates, which are experiment resources created and named by modelers. They can be created for any model, of any settings of any simulation environment. The experiments should be provided with a Web interface that enables those simulation experiments to be manipulated externally via the Web, hence enhancing interoperability with other Web-based applications. Finally, all experiment resources must be preserved unless deleted by their owner. This Experimental Framework (EF) approach is one of the characteristics that distinguish the proposed methods here from existing Web-based simulations, which usually build entire specific implementations around specific goals while ignoring other possibilities. For example, a system may focus on visualization access via a Web browser while ignoring distributed simulation and vice versa. (4) We want to have the ability to put Web 2.0 services in the simulation loop. The ability to create and manipulate experiments on the Web allows putting anything attached to the Web within the simulation loop. This means that one can mix simulations with any service addressed with a URI according to the Web interoperability style. This Web-based information sharing (Web 2.0 [27] ) can be used to compose Web-enabled services to produce a new application, known as mashup [21] .. In order to achieve the goals above, we defined a new simulation architecture based on RESTful WS [32] . The Representational State Transfer (REST) term first appeared in Chapter 5 in [12] to describe the WWW architectural elements. In this client/server model, resources hold representations (states) where these representations are transferable between resources. For example, as shown in Fig. 1 , the client call transfers the HyperText Markup Language (HTML) representation from the resource (e.g. www.carleton.ca) to the Web browser.. SOAP-based WS imposes many constraints to interoperability [42] : for instance, one cannot decouple interoperated SOAPbased systems implementations as in most cases the data channels (implemented as procedures) and the exchanged data (described as programming parameters) are part of implementation itself. Composition scalability is complex as every service (implemented as procedure) at the server side, usually requires a stub on the client side. Instead, as discussed here, RESTful WS interoperability mechanism along with our design and methods, allowed us to meet the listed objectives above.. The RESTful Interoperability Simulation Environment (RISE) middleware (formally known RESTful-CD++) [3, 2] is the first existing simulation middleware to achieve the goals stated above, and the first distributed simulation middleware to be based on RESTful WS. RISE serves as a container to hold different simulation environments without being specific to any of them. In RISE, all functionalities are hidden in resources, named with uniform resource identifiers (URIs). 1 Those resources (URIs) are connected to each other via uniform virtual channels in which the simulation synchronization is done using XML messages. Thus, the RESTful interoperability approach allows system designers, as we did in the presented RISE middleware to accomplish the following: (1) decompose the systems in components (i.e. called resources/URIs), (2) hide the implementation within those components, hence separating component interfaces from software implementation. This concept similar is information hiding in modular programming, which is the chief aspect behind the well-known style of objectoriented programming [31] . These fundamentals were adapted by the RESTful WS style, which was adapted by the World Wide Web (WWW), the largest open computing environment. In contrast, existing simulation interoperability approaches do the opposite to these principles by following procedural programming style, hence mixing systems implementation and interface. By going against the Web interoperability principles will always cause serious difficult interoperability issues when interoperating on the Web with other existing systems. These issues became obvious during the current efforts on standardization of Discrete Event System Specifications (DEVS) [46] . This standardization effort is aiming on interoperating various DEVS-based implementations systems via the Web [41] .. In the following sections, we present the RISE design, the foundations of meeting the above objectives, and the distinguished characteristics from existing Web-based approaches.. The paper is organized as follows: Section 2 presents background of the concept of simulation interoperability, and we describe how RISE fits within this concept. Section 3 presents the RISE middleware design fundamentals. Section 4 summarizes a number of RISE-based services, applications, and extensions. We presented a new concept and ideas for distributed simulation, implemented as the RISE middleware. We introduced the software design of this middleware along with a number of RISEbased services and applications examples. We showed that the three design principles of RISE (i.e. general resource-orientation, uniform-interface, and message-orientation) can achieve various objectives with respect to interoperability. Particularly, hiding interoperating systems heterogeneities (by decoupling systems APIs from internal implementations), composition scalability (by advocating uniform-interface), and dynamicity (since information channels automatically exist). We also showed how the middleware could be designed to serve as a general container to hold simulation environments. This is done via the concept of layered interoperability: The middleware layer (the interoperability methods), the simulation layer (i.e. the simulation environments; in our examples above we used DCD++ as a proof of concept), and the modeling layer (i.e. partitioning granularity). Thus, the middleware design is not specific to any simulation package, keeping the door open for additional extensions. Interoperating in the Web style at the Web layer level (i.e. RESTful WS) allows the middleware to take advantage of any new Web-based applications and ideas such as Web 2.0. Doing this with SOAP-based WS is difficult, mainly because SOAP creates an RPC layer above the Web layer that exposes internal systems implementations to each other. On the other hand, RISE provides better interoperability applying RESTful WS principles. As in the case of any technology, the decision to use such technology can be different from a project to another. Therefore, we recommend the use of RESTful WS principles in Web-service based projects that contain some or all of the following characteristics: (1) Projects that desire to interoperate with applications that use the Web interoperability style such as Web 2.0 and mashup solutions. (2) Projects that are expecting to interoperate with systems outside their control. In this case, as we did in RISE design, systems APIs can be moved outside the implementation, decoupling the system components. (3) Projects that are expecting to scale up well when composing large number of systems (components). The REST style has proved to work well on the WWW, which RISE imitates. (4) Projects that expect having different components joining/disjoining the distributed structure dynamically at run time. The REST (Web) style has been proven to work on the WWW by having countless number of systems to join/disjoin all times. In recent years, some studies conducted in the form of surveys of experts from different backgrounds (e.g. [4, 5, 37] ) discussed the trends and challenges of distributed simulation. The studies pointed out for the need to enhance cooperation across geographical areas at different levels, as follows: (1) Cooperation at a global level via the Internet, which is driven by economic incentives to form industrial clusters. (2) Hiding/packaging information in components (e.g. to enhance reuse and protect intellectual property rights). The surveys acknowledged that the most serious challenge to resolve is interoperability. Our objectives of enhancing interoperability, particularly by decoupling/hiding implementations and the general Web-based middleware container follow these guidelines.. Interoperability enables two or more different software systems to interface and use each service correctly [38] . The complexity of interoperability arises when systems are heterogeneous, as in the case of distributed simulation. This is usually because systems have been developed independently with different semantics (i.e. the meaning of the exchanged information) and/or syntactic (i.e. the rules of structuring and exchanging the information). Since such capabilities are realized in software design and implementation, interoperability needs to be studied from the software perspective, in particular, at the Application Programming Interface (API) level (since this is how systems access and use other systems services).. To hide implementation details and raise the level of abstraction, a layered architecture can be used (similar to the shown model in Fig. 2 ). In this model, each layer defines its interoperability methods, and provides services to the layer above it. Following this concept, RISE is organized in the following layers: middleware, the simulation, and modeling. The middleware layer (discussed in Section 3) provides a number of services to the simulation layer, such as all means of communication and managing all simulation experiments lifecycle and executions. The simulation layer deploys different simulation environment types, each of which supports its own time management. The modeling layer operates above the simulation layer. This represents the system under study, which is simulated by a specific simulation environment. This RISE model layers match other existing interoperability conceptual layers, particularly the Level of Conceptual Interoperability Model (LCIM) [38] , as shown in Fig. 2 .. LCIM divides all related interoperability aspects into three general layers (Fig. 2) : the LCIM integrability layer deals with network and connectivity issues. In RISE environment, the middleware layer uses existing Web protocols to resolve networking and connectivity issues. In this case, the middleware layer conceals the network and any connectivity issues from the upper layers. The LCIM interoperability layer deals with the software implementation of interoperation, including simulation and middleware. In RISE, this layer is presented in two layers: middleware and simulation. As discussed previously, the simulation layer contains the simulation engine implementation including simulation algorithms and formalism. This makes the simulation implementation and algorithms independent of the middleware layer. Thus, the simulation layer can have different types of simulation implementations and algorithms. The middleware layer provides management and common interoperability services to the simulation services in the upper layer. For example, the middleware layer manages the distributed aspects in for distributed simulation services, simulation experiments lifecycle, formatting simulation remote messages, interfacing simulation systems with the Web, etc. The LCIM composability layer deals with the alignment issues of the models, that is, how to compose various models correctly to meet the overall purpose. In RISE, this is the modeling layer discussed previously. RISE composes the models based on the modeler's instructions (e.g. in form of XML) as part of setting up a simulation experiment. In this case, composition here deals with interoperating different partitions. This partition encloses a portion of the distributed model along with the simulation environment. The assumption here is that a simulation environment in a partition is capable of executing the model portion in its partition. However, in the case of the LCIM and other conceptual frameworks (e.g. [11, 29] ), composability only deals with the conceptual ideas of constructing a model correctly to achieve a solution. Thus, those works only provide modelers with conceptual ideas of building models to represent systems under study correctly. However, composability in RISE goes beyond building a model correctly. For example, in RISE, those models are enabled with a Web interface, and models can be partitioned and simulated over a number of machines in a distributed matter. Of course, models partitions might be heterogeneous, hence are not necessary simulated by the same simulation environment type.. The way current distributed simulation approaches exchange, structure, and use information, is usually tied to programming and implementation, which exposes the heterogeneity of the systems. In order to deal with these issues, we need to homogenize different implementations, which is usually a complex problem to resolve.. In particular, the Common Object Request Broker Architecture (CORBA) based and SOAP-based distributed simulation protocols are complex to interoperate. SOAP-based WS simulations [30] group the services as procedures in WS ports (addressed by a single URI). Thus, simulation data is exchanged and described in the form of procedure parameters while the data channels are described as procedures. SOAP messages in XML (describing RPCs) are not exchanged at the simulation level, but at the Web service technology layer. This is the case for all SOAP-based WS simulations such as [15, 35, 42, 44] .. It is worth to note that we tried to achieve our goals based on SOAP-based WS [42] , which was one of the first Service Oriented Architecture (SOA)/DEVS simulators, as part of trying to standardize the middleware [1] for DEVS-based systems. Discrete Event System Specification (DEVS) [46] is a modeling and simulation formalism that has been widely used through many different implementations over the last three decades to study complex discrete event systems. A number of DEVS-based implementations are presented in [41] . However, we realized that it was not possible due to the restrictions imposed by SOAP WS structural rules. For example, we cannot decouple interoperated systems implementations if the data channels (procedures) and the way data is described (programming parameters) are part of implementation itself. Composition scalability is complex if every service (implemented as procedure) at the simulations at the server side require stubs on each simulation client. Thus, this interoperability approach is difficult to achieve in open communities as in the case of the Web. This is because in such communities practice, systems need to be designed, implemented, and evolved independently. On the other hand, the SOAP-based WS ports (along with their procedures) had to be created and compiled before even starting up the system. This approach usually ends in a close community where software developers can discuss with each other to resolve systems API related design issues. The Web Services Description Language (WSDL) role is to describe the RPCs signatures (i.e. names and input/output parameters). However, once the published WSDL document is compiled to programming stubs (usually by a tool), programmers need to code those stubs and compile them with their software [30] . This . This argument also applies to CORBA-based simulations (e.g. [25] ), since they use the same RPC interoperability style, but with different standards. The HLA standard [23] performs distributed simulation via interfacing a number of federates via the Run Time Infrastructure (RTI). To be part of the Web, the HLA recently allowed interfacing the RTI with a SOAP WS interface (e.g. [6, 22, 48, 49] ). However, this WS interface still does not cover RTI-to-RTI interoperability. This is done in the form of programming functions (called interactions); hence, data is exchanged at the federate level via those functions' parameters. The data exchanged between federates is described in programming parameters, while the data channels are realized as programming procedures. Further, the RTIs themselves are implementation-specific, which makes it difficult to interoperate different vendors' RTIs. At the RTI level, simulation data is usually exchanged as attributes according to an RTI specific implementation.. In contrast, the WWW is the largest existing distributed structure where countless of systems interoperate with each other according the Web standards. Considering this open-community interoperability style, we decided to use RESTful WS [32] . This is mainly because the RESTful interoperability approach allows system designers to decompose systems in resources/URIs while hiding implementation within those components, separating component interface from their software implementation (as we did in the presented RISE middleware). Specifically, the resources exchange messages and they are connected via virtual constant standardized channels. RESTful Web-services are also gaining increased attention with the advent of Web 2.0 [27] and the concept of mashups [21] (a grouping of various services from different providers presented as a bundle in order to provide single integrated service). For example, IBM enterprise mashup solutions [20] aim on integrating Web 2.0 functions as rapid as possible.. The proposed RISE middleware, which is the first RESTful-based simulation middleware, provides a novel approach to decouple interoperability from simulation systems implementations and internal software design issues (Table 1) . This is mainly because APIs are moved outside implementations. Information is exchanged using XML messages and transferred via virtual channels (HTTP methods [13] in our case). Table 1 compares RISE with other existing approaches, classifying the way in which synchronization messages are defined and exchanged, and in the way simulation services are accessed, structured, and addressed. These RISE design issues are discussed in Section 3. SOAP-based WS ports are usually connected with specific designed remote procedures, targeting specific systems implementations. On the other hand, the Web-based HLA method is similar to the typical HLA, but federates are able to communicate with their RTI via the Internet using SOAP-based RPC-style.. Other Web-based simulation efforts have mainly focused on visualization and models reusability [8, 14, 28, 33, 50] by providing Web access to model repositories and visualization environments. However, these models are only executable on specific simulation environments. Table 2 compares the presented RISE middleware with current Web-based simulation approaches. Table 2 compares the proposed RISE framework with various Web-based simulation systems. Although there are numerous web-based simulation environments, the references selected for the table reflect the most recent Web-based simulation works. Table 2 summarizes a number of characteristics:. (1) General middleware (Row #1 in Table 2 ): the ability to support different simulation environments (e.g., the middleware is independent of the implementation). (2) Distributed simulation (Row #2 in Table 2 ): several processors perform a single simulation via synchronization through the Web. (3) Composition scalability (Row #3 in Table 2 ): the number of partitions in distributed simulation is independent from the way synchronization information is exchanged. (4) Standardized information channels (Row #4 in Table 2 ): RISE virtual information channels use the HTTP standards [13] . Existing Web distributed simulation systems often design their specific RPCs. There are few systems based on standardized SOAP-based RPCs, but those implementations are heavily influenced by such standards. For example, the SOAP-based IDSim system [15] uses the Globus Toolkit [16] implementation. The Globus Toolkit [16] realizes the SOAP-based OGSI [39] interoperability specifications standards. Because the SOAPbased interoperability rules are tied to programming, the OGSI standards become tied to programming as well. In this case, the IDSim software design architecture (as described in [15] ) has heavily been tied to the Globus implementation of the OGSI standards. In general, it is difficult to come up with a software design interface that would fit every system implementation needs. (5) Synchronization messages description (Row #5 in Table 2 ): RISE uses XML to describe all synchronization messages. In fact, RISE is the only Web-based simulation system on this list (which is generic) to use XML descriptions. This messageoriented approach provides a better method compared to programming parameters. XML messages and the virtual channels put systems APIs outside their implementations, hence, decoupling distributed systems implementations. For example, SOAP-based systems exchange simulation information as programming parameters via RPCs. Even though the WS engines describe those RPCs as XML SOAP messages, information still exchanged as programming parameters at the simulation engines. Table 2 ): it is simple to due to the use of RESTful WS.

O O O B B B 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 B B O O O O O O O O O O O O O O O O O O O O O B B 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 B B O B O O O O B 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 B 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 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 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 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 B O O O O O B 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 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 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 B 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 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 B O O O O O O O O B O O O O O O O O O O O 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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 B 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 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 B 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 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 B O O O O O O O O O O O O O O O O O B B O O B O O O O O O O O O B B O O O O O O O O O O B B 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 B O O O O O O O O O B 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 B B 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 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 B B 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 B O O O O B 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 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 B O O O O O O O O O O O B O O O O O O O O O B O B O O B O O B 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 B 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 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 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 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 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 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 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 B O O O O B O O O O O O O O B O O O O B 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 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 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 B O O O O O O O O O O O O O O O O O O O B O B 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 B O O O O O B O O O B 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 B O O O O O O O O B O O O O O O O B 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 B 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 B O O O O O O O O O B O B O O O O O O O O O B O B O O O O B O O O B O O O B O O O O O O B O O O O O O B O O O B O O O O O O B O O O O B O O O O O B O O O B O O O O O O O O B O O O O O O O O B O B O O O B 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 B 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 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 B 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 B 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 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 B 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 B 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 B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 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 B 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 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 B O O O O O B B O O O O O O B 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 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 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 B O O O O O O O B O O O B O O O O O O O O O O O B 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 B O O O O O O O O O O O B O O O B O O O O O O O B 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 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 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 B 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 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 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 B O O O O O O O O O O O O O O O


Identifying commented passages of documents using implicit hyperlinks This paper addresses the issue of automatically selecting passages of blog posts using readers' comments. The problem is difficult because: (i) the textual content of blogs is often noisy, (ii) comments do not always target passages of the posts and, (iii) comments are not equally useful for identifying important passages. We have developed a system for selecting commented passages which takes as input blog posts and their comments and delivers, for each post, the sentences of the post which are the most commented and/or the most discussed. Our approach combines three steps to identify commented passages of a post. The first step is to remove the complexity of processing the contents of posts and comments using heuristics adapted to the language of the blog. The second step is to find useful comments and assigns them a degree of relevance using a model automatically built and validated by an expert. The third step is to identify important passages using relevant comments. We conducted two experiments to evaluate the usefulness and the effectiveness of our approach. The first study show that in only 50% of the posts, the most commented sentence elicited by our approach corresponds to the post extract generated using generic summarization. In the second study, human participants confirmed that, in practice, selected passages are frequently commented passages. Thanks to collaborative tagging systems, weblogs and other wikis, communicating on the Web is easier than ever. Weblogs are websites where authors post entries (or posts) that anyone can read and comment. Readers' comments often target passages of posts which may be, for instance, funny or provocative. This paper addresses the problem of identifying passages of a post which are the most commented and/or the most discussed.. Automatically identifying commented passages of a document could be useful in different applications such as document summarization. Summaries are helpful to tackle the complexity of information retrieval and processing. Methods for summarizing webpages [1, 3] , websites [29] and generating snippets of search results have been proposed. These methods try to capture the gist of their targets and therefore usually focus on the author's intention. Taking into account existing comments could help produce more reader-oriented summaries. Typically, such methods could be useful to blog search engines such as technorati. 1 Another potentially interesting application of identifying commented passages is to assist moderators to detect subversive contents on weblogs.. Commenting on a post is like linking to it. Generally speaking, links often convey information about their targets. That is why, many web applications successfully rely on the context (i.e. source pages linking to a target document) to categorize [27] , rank [25] or abstract web pages [1, 3] , as well as to support web search [26, 28] . In the case of weblogs, readers do not explicitly link to posts: either comments are inserted along with the posts or they are put together on external pages. In order to take advantage of this implicit linking, one needs to identify the exact targets of the comments.. Identifying commented passages is more specifically related to summarization by extraction and summarization by context. Summarization by extraction consists in locating passages that best characterize the content of a document [15] . Summarization by context [2] is concerned with the problem of abstracting a document from external knowledge. For example, [1, 3] have proposed methods to summarize a web page from excerpts of pages linking to it. Readers often comment on passages that are unrelated to the central ideas of the posts. The need of commenting may be triggered by an anecdote, a private joke, or even something unwanted by the author such as a wrong hypothesis or a misspelled name. Therefore, our problem differs from automatically summarizing a post. Combining the ideas of summarization by extraction and summarization by context is the key of our approach for selecting passages in posts using comments.. The complexity of the problem of identifying commented passages is mainly due to three reasons. First, the language used in blogs is difficult to automatically process. Posts and comments often contain numerous misspelled words, abbreviations and phonetic expressions which confuse text mining techniques. Second, comments do not always target passages of posts. This type of comments may mislead the identification of commented passages. Analyzing the types of comments is necessary to characterize relevant comments, i.e. comments which really target passages of posts. Third, relevant comments are not equally useful for identifying commented passages. The usefulness of a comment depends on the communicative intention and the audience targeted by the commenter. A degree of relevance should therefore be associated to each relevant comment.. In this article, we introduce a new approach which combines three steps to identify commented passages of a post. The first step is to remove the complexity of processing the contents of posts and comments using heuristics adapted to the language of the blog. The second step is to find useful comments and assigns them a degree of relevance using a model automatically built and validated by an expert. We have developed a method to automatically detect if a comment is relevant. Our key idea is to combine both the power of machines and human expertise to characterize relevant comments. The machine is used to present clusters of similar comments while the expert verifies whether the clusters contain only relevant or only irrelevant comments. Clusters represent prototypes of relevant and irrelevant comments and they form a model that is used to predict the relevance of comments. Finally, the third step is to select passages of the post from relevant comments. Several types of commented passages and their selection strategies are presented. We report also on the evaluation of our method and present the results of two experiments -one being quantitative and the other one involving human experts.. The paper is organized as follows. Section 2 reviews related work. Section 3 outlines our approach for identifying blog posts from readers' comments. The three following sections present with more details each of the main steps of our approach. In Section 7 we present the results of our experiments. Our conclusions and suggestions for future research are discussed in Section 8. This paper addressed the issue of automatically selecting passages of blog posts using readers' comments. The problem is difficult because: (i) the textual content of blogs is often noisy, (ii) comments do not always target passages of the posts and, (iii) comments are not equally useful for identifying important passages.. We have developed a system for selecting commented passages which takes as input blog posts and their comments and delivers, for each post, the sentences of the post which are the most commented and/or the most discussed. Our approach combines three steps to identify commented passages of a post. The first step tackles to pre-process the contents using heuristics adapted to the type of language used on blogs. The second step identifies relevant comments and their degrees of relevance using a model automatically built and validated by an expert. The third step selects passages using relevant comments. Three selection strategies are proposed to identify: the most commented passages, the most discussed passages and passages that can be identified from the text following cue words in readers' comments, e.g. "you're completely right… ".. We conducted two experiments to evaluate the usefulness and the effectiveness of our approach. The first study show that in only 50% of the posts, the most commented sentence elicited by our approach corresponds to the post extract generated using generic summarization. In the second study, human participants confirmed that, in practice, selected passages are frequently commented passages.. In future work, we plan to propose new summarization techniques taking the popularity of passages of documents. Existing summaries are intended to reflect the authors' intentions. The approach proposed in this paper will enable to generate summaries reflecting what the reader is likely to be interested in even if the author did not stress on it. Commented passages may be useful to detect what is perceived as, for example, important, original and provocative in a post. However, these passages may not reflect the central ideas of the post and cannot be identified by existing summarization techniques. To our best knowledge, identifying commented passages of a document has never been addressed in previous research. In the next section, we present our approach which combines the key ideas of summarization by extraction and summarization by context. Adding comments to documents is a functionality that has long been provided by hypermedia systems [12] . Randy Trigg, Textnet's designer, distinguished fifty-one types of comment. However, Trigg's taxonomy was only intended to classify scientific research works and not any kind of comments. Comments as a special kind of annotations have also been widely studied [9, 11] . In [9] , Marshall suggests several dimensions to classify annotations with respect to their audience (publishedprivate), duration (permanent-transient) and so forth. Shipman et al. have proposed a system to identify useful passages of documents from hand-written annotations [11] . Their system aims to detect typical marks (comment, underline marks, margin bar, symbols, etc.) included in annotations of important passages.. The problem of identifying commented passages overlaps with two well-established areas of research: summarization by extraction and summarization by context. In the following, we briefly survey these issues. We collected 32549 posts from blogs hosted on Blogger.com and 67441 comments associated with these posts. To gather this data, we started by building queries about politics, business and technologies that we submitted to Google. 2 We extracted the URLs of blogs hosted on Blogger.com from the result lists. Then, we fetched the latest entries posted on the blogs. Overall, we downloaded 32549 posts thanks to this process. We waited before fetching the comments because readers often comment on the posts several days later. Thus, after one week, we downloaded 67441 readers' comments. 30.77% of posts (10017) received at least one comment and 99% of the posts have less than 20 comments. We used a part-of-speech tagger to keep only posts and comments containing at least 20% of known English terms. It enabled us to discard noisy documents or documents written in other languages. Mean number of terms in common between a post and a comment 2.5. We implemented a program to display the comments, the posts and the commented passages of the posts. It helped the expert to build the model selecting relevant comments. Table 1 summarizes preliminary results about the comments. To cluster the comments, we chose the K-means algorithm and used its implementation in the WEKA package [24] . The model is built from 1000 comments which are clustered in 40 classes. After analyzing a large sample of comments within each class and the post contents, we assigned a degree of relevance to each class. These degrees are taken into account at the second step of our approach where passages are selected using relevant comments. We assigned the weight 0 to eight classes containing only irrelevant comments and the weight 1 to twenty-nine classes containing partly relevant comments and the weight 2 to the remaining classes made up with comments directly targeting passages of their targets.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O