Mining multidimensional and multilevel sequential patterns Multidimensional databases have been designed to provide decision makers with the necessary tools to help them understand their data. This framework is different from transactional data as the datasets contain huge volumes of historicized and aggregated data defined over a set of dimensions that can be arranged through multiple levels of granularities. Many tools have been proposed to query the data and navigate through the levels of granularity. However, automatic tools are still missing to mine this type of data in order to discover regular specific patterns. In this article, we present a method for mining sequential patterns from multidimensional databases, at the same time taking advantage of the different dimensions and levels of granularity, which is original compared to existing work. The necessary definitions and algorithms are extended from regular sequential patterns to this particular case. Experiments are reported, showing the significance of this approach. Multidimensional databases have been studied for more than 10 years. They provide an easy-to-use interface for decision makers to navigate through their data. The modeling of such databases and the operations that can be applied are now well defined, and implemented by the main editors of database management systems (e.g. Oracle, Microsoft, IBM). However, only a few methods have been designed to automatically mine the relevant knowledge from these large amounts of historicized data. In this framework, sequential patterns are suitable as they aim at discovering correlations between events through time. For instance, rules like Many customers buy first a TV and a DVD player at the same time, and then a recorder can be discovered. Scalable methods and algorithms to mine such rules have been proposed in the literature [Srikant and Agrawal 1996] . As for association rules, the efficiency of the discovery is based on the support, which indicates to what extend data from the database contains the patterns.. However, these methods cannot take advantage of the framework of multidimensional databases, because -they only consider one dimension to appear in the patterns, which is usually called the product dimension; -they do not consider hierarchies.. Some studies claim to combine several dimensions [Pinto et al. 2001; de Amo et al. 2004; Yu and Chen 2005] . However, we argue here that they do not provide a complete framework for multidimensional sequential pattern mining. The way we consider multidimensionality is indeed generalized in the sense that patterns contain several dimensions combined over time [Plantevit et al. 2005] . Moreover these dimensions are considered at different levels of granularity so as to automatically mine the most relevant rules. Note that mining the rules at very high levels of granularity leads to trivial rules, whereas mining rules at very low levels of granularity is not always possible because the support value becomes too low.. In our approach, we aim at building rules like "When the sales of soft drinks are high in Europe, exports of Perrier in Paris and exports of soda in the US become high later on." This rule not only combines two dimensions (Location and Product) but it also combines them over time and at different levels of granularity (as Perrier is considered as a kind of soft drink). As far as we know, no method has been proposed to mine such rules, except in our first proposal [Plantevit et al. 2006] .. In order to mine the most relevant sequences, our approach is designed so as to take into account the most appropriate level of granularity, including partially instantiated tuples in sequences where the highest level is considered. More precisely, our algorithms are designed in order to mine frequent multidimensional sequences that may contain several levels of hierarchy, including the most general, usually referred to as the ALL value. However, in order to avoid mining non relevant patterns, only the most specific patterns (meant to be the most informative) are shown to the user.. The article is organized as follows: Section 2 introduces a motivating example illustrating the goal of our work, and then, Section 3 presents existing work concerning multidimensional databases, multidimensional approaches and sequential pattern mining. Section 4 introduces basic definitions and Section 5 presents the M 3 SP algorithm for mining multidimensional and multi-level sequential patterns. Section 6 presents the results of the experiments performed on synthetic and real data. In Section 7, we conclude and present future research directions based on the present work. In this article, we have proposed a novel approach for mining multidimensional and multilevel sequential patterns, according to which, contrary to the work from Pinto et al. [2001] ; de Amo et al. [2004] ; and Yu and Chen [2005] , several analysis dimensions at several detailed levels can be taken into account.. We have provided formal definitions and properties from which algorithms have been designed and implemented. Experiments on synthetic and real datasets have been reported and show the interest and the scalability of our approach.. This work offers several research perspectives. First, the study of the relationship between moving up and down in the hierarchies and the tuning of the minimum support threshold can be an interesting extension of the present work.. On the other hand, the efficiency of the extraction could be enhanced by considering condensed representations of the mined knowledge, based on the notions of closed, free, or non-derivable patterns [Mannila and Toivonen 1996; Pasquier et al. 1999a Pasquier et al. , 1999b Pei et al. 2000; Burdick et al. 2001; Zaki and Hsiao 2002; Calders and Goethals 2002; Boulicaut et al. 2003; Zaki 2004; Bonchi and Lucchese 2006; Calders et al. 2006] .. Finally, we are aware that considering maf-sequences to generate the frequent sequences prevents us from extracting all of the most specific sequences. This is so because such sequences may contain items that do not occur in maf-sequences, and thus cannot be mined by our approach. Coping with this issue is another interesting extension of the present work that we plan to investigate in the near future. The experiments on both synthetic and real datasets reported in this section show that our approach allows the efficient mining of frequent multidimensional and multilevel sequences from huge datasets. Compared to the preliminary version of this work in Plantevit et al. [2006] , the algorithms have been significantly improved. The major improvements are the following. -In Step 1, maf-sequences are mined according to a BUC-based strategy, instead of an Apriori-like strategy, as done in Plantevit et al. [2006] . -The transformation of the underlying database presented at the end of Section 5 allows for a better memory usage than handling the dataset itself, as done in Plantevit et al. [2006] . -In Step 2, based on the transformation, any standard algorithm for mining frequent sequences can be used. Having compared the efficiencies of these algorithms, SPADE has shown the best performance. This algorithm was therefore chosen, instead of PSP, as done in Plantevit et al. [2006] .. We note that our approach may lead to the study of the relationship between moving up and down in the hierarchies and the tuning of the minimum support Then, given a minimum support threshold minsup, the average level of all levels of the frequent maf-sequences can provide information on how the value of minsup relates to the average level of the items occurring in frequent sequences.. One could even think of a preprocessing phase to compute maf-sequences for some given values of minsup. In this way, one could not only efficiently get the frequent multidimensional sequences given a minimum support threshold (as the output of the preprocessing phase could be used to optimize the computation), but also get the appropriate minimum support threshold value, given a desired average level of detail in the frequent multidimensional sequences to be computed.. Although this issue has not been investigated in the present work, it can be considered as a valuable extension of our approach. In this section, we present the context of multidimensional databases and the existing work related to sequential patterns. As usual, the edges of such a tree H i can be seen as is-a relationships, and the specialization relation (respectively the generalization relation) corresponds to a top-down (respectively bottom-up) path in H i : a path connecting two nodes when scanning H i from the root to the leaves (respectively from the leaves to the root).. In the case where no hierarchy is defined for a dimension D i , we consider H i as being the tree whose root is ALL i and whose leaves are all the elements in dom(D i ) \ {ALL i }. We recall in this respect that this article is a generalization of our previous work [Plantevit et al. 2005] , where, for every dimension D i , no hierarchy is considered, and the symbol * is used in place of ALL i .. A fact table T over universe D is a finite set of tuples. , which is a leaf of the associated hierarchy H i . In other words, we assume that the table T to be mined, contains only the most specific values with respect to all hierarchies over dimensions.. We note that in our approach, the domains of attributes are not restricted to be discrete, even if each value occurring in the fact table T is treated as a nominal value. Moreover, it should be clear that discretizing continuous numerical attributes can be seen as defining a hierarchy on the corresponding domain.. Since we are interested in sequential patterns, we assume that D contains at least one dimension with a totally ordered domain, corresponding to the time dimension.. Moreover, given a fact table T over D, for every i = 1, . . . , n, we denote by Given an element x in Dom(D i ) and the associated hierarchy H i , we introduce the following notation: -down(x) and up(x) denote respectively the set of all direct specializations and the singleton containing the only direct generalization of x.. More precisiely, if x is not a leaf in H i , then down(x) is the set of all y in Dom(D i ) such that H i contains an edge from x to y; otherwise, down(x) is set to be equal to the empty set. Similarly, if x = ALL i , up(x) is the set containing the only element y in Dom(D i ) such that H i contains an edge from y to x; otherwise, up(x) is set to be equal to the empty set. -We denote by x ↑ (respectively x ↓ ) the set containing x along with all generalizations (respectively specializations) of x with respect to H i that belong to Dom(D i ).. Clearly, for every i = 1, . . . , n, we have ALL. ↑ and x ↓ = {x}. We also note that, for every x,. Example 1. Referring back to our motivating example, the considered set of dimensions D is defined by D = {D, Cat, Age, Loc, Prod , Qt y}. Considering the fact table shown in Table 1 , we have for instance, Dom(Prod ) = {beer, soda, wine, whisky, pretzel, chocolate, M 1, M 2}. Figure 2 shows the hierarchy H Prod associated to the dimension Prod . It can be seen from this figure that down(drink) = {s drink, a drink} and. Moreover, Figure 2 also shows that drink is a generalization of sod a, that is drink ∈ sod a ↑ and sod a ∈ drink ↓ . We also note that, as sod a is a leaf of We end this section by pointing out that hierarchies on dimensions could be defined as directed acyclic graphs (DAGs). However, although this more general case can be considered in our approach, this would imply redundancies in the computation of frequent sequences. We shall come back to this point in Section 5. The algorithms of our approach have been implemented in Java 1.5. Experiments have been carried out both on synthetic and real data, aiming at studying the scalability issues with respect to the main parameters when mining multidimensional databases (e.g., the support threshold, the number of dimensions, the number of elements in every dimension).. First, we study the impact of taking hierarchies into account. We show that considering the different levels of hierarchies as separate attributes and running our previous M 2 SP algorithm in this case, is less efficient than running M 3 SP. Second, we discuss the behavior of our algorithms in the case of various synthetic datasets, and third, we consider the case of a real dataset.

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


Voting

policies

that

cope

with

unreliable

agents

Collaboration

plays

a

critical

role

when

a

team

is

striving

for

goals

that

are

difficult

to

achieve

by

an

individual.

In

previous

work,

we

defined

the

ETAPP

(Environment-TaskAgents-Policy-Protocol)

framework,

which

describes

the

collaboration

of

a

team

of

agents.

According

to

this

framework,

team

members

propose

agents

to

perform

a

task,

and

the

team

applies

a

voting

policy

to

choose

an

agent

for

the

task.

In

this

paper,

we

expand

on

three

parameters

of

this

framework.

We

model

team

members

that

have

variable

proposal

making

attitudes,

and

team

members

whose

performance

exhibits

different

levels

of

stability.

We

then

consider

two

new

voting

policies

for

group

decision-making,

and

use

a

simulation-based

evaluation

to

investigate

the

interaction

between

the

different

types

of

team

members

and

the

voting

policies.

Our

results

show

that

our

previous

optimistic

voting

policy,

which

chooses

the

agent

that

seems

to

have

the

best

performance,

yields

an

unstable

task

performance

for

teams

where

even

a

few

agents

do

not

make

the

best

possible

proposal.

In

contrast,

our

new

voting

policies

yield

a

stable

task

performance.

In

many

multi-agent

collaboration

scenarios,

the

agents

involved

have

different

opinions

about

how

to

perform

a

task

and

who

should

perform

it.

Voting

policies

are

methods

that

elicit

the

opinions

of

individuals

in

order

to

make

group

decisions.

However,

agents

may

not

always

be

reliable

when

they

propose

agents

for

a

task,

thus

compromising

the

performance

of

the

group.

Making

reliable

proposals

means

that

agents

make

proposals

to

optimize

utility

according

to

the

criteria

of

a

task

(rather

than

an

agent's

own

criteria).

Two

examples

of

task

criteria

are

to

improve

the

quality

and

to

reduce

the

time

spent

to

achieve

a

task..

The

goal

of

our

research

is

to

provide

insights

into

how

unreliable

agents

influence

team

performance,

and

propose

group

decision

procedures

that

cope

with

unreliable

agents

in

the

context

of

a

collaborative

activity

-the

allocation

of

agents

to

tasks.

We

investigate

these

aspects

under

the

ETAPP

framework.

This

framework

expresses

the

collaboration

of

a

team

of

agents

in

terms

of

five

operating

parameters:

Environment,

Task,

Agents,

Policy

and

Protocol

In

our

framework,

agents

do

not

accurately

know

the

capabilities

of

team

members,

and

combine

their

knowledge

and

coordinate

their

activities

by

means

of

a

group

decision

procedure.

Specifically,

agents

decide

which

agent

should

perform

a

task

by

applying

an

optimistic

voting

policy,

which

chooses

the

proposed

agent

with

the

most

promising

performance

compared

to

all

the

other

proposed

agents.

This

policy

was

applied

under

the

simplistic

assumption

that

agents

always

make

proposals

in

a

reliable

manner

with

the

best

intention

for

the

group.

However,

decisions

made

under

this

assumption

could

lead

to

bad

team

performance

when

some

agents

exhibit

unreliable

proposal-making

behaviour..

To

investigate

the

impact

of

such

behaviour,

we

extend

the

ETAPP

framework

by

considering

the

following

types

of

unreliable

agents,

which

can

make

proposals

that

are

not

beneficial

for

a

team..

Selfish.

If

the

agent

considers

a

task

to

be

onerous,

it

proposes

other,

perhaps

less

qualified,

agents

for

the

task;

or

alternatively,

if

a

task

is

of

benefit

to

the

agent,

it

may

propose

itself,

while

disregarding

more

qualified

candidates..

Lazy.

The

agent

knowingly

uses

less

information

than

is

available

when

it

proposes

agents

for

a

task..

Corrupt.

The

agent

attempts

to

undermine

the

collaboration

when

it

proposes

agents

for

a

task..

Conservative.

The

agent

does

not

use

the

most

recent

information

to

update

its

opinion

of

team

members,

thus

proposing

agents

on

the

basis

of

old

information..

We

propose

to

cope

with

unreliable

team

members

by

applying

different

voting

policies

that

enable

a

team

to

perform

in

a

stable

manner.

In

this

paper,

we

consider

majority

voting,

where

the

agent

preferred

by

most

team

members

is

chosen,

and

weighted-observation

voting,

where

a

proposal

for

an

agent

who

has

been

previously

seen

performing

a

task

is

weighed

higher

than

a

proposal

for

an

agent

that

was

not

observed

in

action

before..

Some

researchers

assume

a

trusted

third

party

that

is

determined

by

reputation

mechanisms

This

would

be

an

agent

that

is

deemed

to

make

proposals

in

a

more

reliable

manner

than

other

agents

in

a

team.

However,

in

our

framework,

there

is

no

reason

to

assume

that

any

agent

will

make

more

reliable

decisions

than

other

agents.

This

means

that

agents

do

not

know

if

team

members

are

unreliable

or

reliable,

and

therefore

the

team

is

not

able

to

exclude

unreliable

team

members

from

the

decision

making

process..

An

additional

extension

considered

in

this

research

consists

of

building

models

of

agents

that

exhibit

a

nondeterministic

performance.

That

is,

each

time

an

agent

performs

a

task,

its

level

of

performance

may

change

due

to

the

influence

of

factors

that

are

not

explicit.

This

extension

requires

a

probabilistic

representation

of

an

agent's

task-related

capabilities,

such

as

mean

level

of

performance

and

stability;

a

procedure

for

building

agent

models

from

a

sequence

of

observations;

and

a

representation

of

an

observer's

observation

capacity

(which

is

similar

to

attention

span),

i.e.,

how

many

observations

can

the

observer

remember..

We

assess

the

influence

of

these

factors

on

task

performance

by

means

of

a

simulation

where

we

vary

the

stability

of

agents

and

their

observation

capacity,

and

the

type

and

quality

of

the

proposals

made

by

agents

(by

considering

teams

composed

of

reliable

agents

and

selfish,

lazy,

corrupt

or

conservative

agents),

and

apply

different

policies

for

selecting

agents

for

a

task..

Our

approach

can

be

applied

to

a

number

of

examples,

such

as

the

routing

problem

in

peer-to-peer

networks,

where

a

group

of

peers

(also

called

nodes)

is

selected

based

on

their

bandwidth

to

establish

a

transmission

routing

between

two

remote

peers.

Each

node

that

is

part

of

a

routing

provide

bandwidth

and

consume

battery

power

in

order

to

establish

a

transmission.

Single

peers

have

incomplete

knowledge

of

the

bandwidth

of

peers

in

the

network,

and

may

ask

other

peers

for

their

opinion

about

which

routing

is

optimal.

This

process

of

collecting

and

assessing

other

peers'

opinion

resembles

a

voting

process.

Each

peer

in

such

a

network

is

concerned

to

preserve

its

own

bandwidth

and

battery

power,

and

thus

a

peer

may

only

propose

other

peers

for

the

routing

(rather

than

itself).

The

voting

policies

proposed

in

this

paper

are

designed

to

cope

with

the

problem

of

unreliable

peers..

Section

outlines

the

ETAPP

framework

and

discusses

the

extensions

where

we

model

agents

with

a

nondeterministic

performance

and

unreliable

agents,

and

consider

new

voting

policies.

Our

evaluation

is

described

in

Section

In

Section

we

consider

related

research,

followed

by

our

conclusions.

A

lazy

agent

does

not

maintain

models

of

team

members,

thus

it

randomly

selects

an

agent

when

making

a

proposal,

and

attributes

to

it

a

random

performance..

A

corrupt

agent

proposes

a

random

agent,

attributing

to

it

a

high

level

of

performance,

in

order

to

undermine

the

collaboration..

A

conservative

agent

does

not

observe

the

behaviour

of

the

team

members,

and

hence

does

not

update

its

models,

using

only

the

initial

models

of

collaborators

when

it

makes

proposals..

Policy.

In

previous

work,

we

implemented

an

optimistic

voting

policy,

where

the

agent

with

the

most

promising

performance

was

chosen

for

a

task.

This

policy

is

adequate

when

the

agents

that

make

proposals

are

reliable.

However,

since

unreliable

agents

can

now

make

proposals,

we

consider

two

additional

policies:

majority

and

weighted-observation.

According

to

the

majority

policy,

the

agent

that

receives

the

most

votes

is

chosen.

In

contrast,

the

weighted-observation

policy

gives

more

weight

to

proposals

for

agents

that

have

some

"credibility".

That

is,

a

proposal

for

an

agent

who

has

been

previously

seen

performing

a

task

is

weighed

higher

than

a

proposal

for

an

agent

that

was

not

observed

in

action

before.

This

weight

is

proportional

to

the

number

of

times

the

observed

agent

has

been

seen

in

action.

For

example,

if

agent

has

just

performed

a

task,

while

has

never

performed

a

task,

the

votes

of

the

agents

that

prefer

are

weighed

higher

than

the

votes

of

the

agents

that

prefer

We

have

extended

our

ETAPP

collaboration

framework

to

model

team

members

that

exhibit

variable

task

performance

and

different

proposal-making

behaviours.

We

have

modeled

four

types

of

unreliable

proposal-making

behaviours,

employed

a

probabilistic

representation

to

model

variable

task

performance,

and

endowed

observer

agents

with

the

capability

to

observe

team

members

and

a

procedure

for

building

agent

models

from

observations.

We

then

investigated

the

interaction

between

these

parameters

and

three

voting

policies..

We

evaluated

our

extensions

by

means

of

a

simulated

rescue

scenario,

where

we

varied

the

agents'

proposal-making

behaviour,

the

performance

stability

of

teams

of

agents,

the

number

of

observations

retained

by

observer

agents,

and

the

policy

used

to

allocate

agents

to

tasks.

Our

results

show

that

performance

variability

has

a

significant

impact

on

task

performance,

and

that

when

agents

are

reliable,

it

is

enough

to

make

a

few

observations

to

improve

task

performance

for

stable,

mixed

and

medium

teams.

Further,

the

task

performance

obtained

by

applying

the

optimistic

selection

policy

is

at

least

as

good

as

that

obtained

with

the

majority

policy.

In

contrast,

when

agents

are

unreliable,

the

majority

and

weighted-observation

policies

yield

the

most

stable

performance

for

all

proposal-making

behaviours,

while

the

optimistic

policy

produces

an

unstable

task

performance,

even

when

only

a

few

agents

are

unreliable.

As

seen

in

Figure

the

average

performance

obtained

for

the

other

types

of

teams

is

generally

worse

than

that

obtained

for

the

Invariant

team.

This

is

due

to

the

higher

variability

in

agent

performance.

In

fact,

the

more

unstable

the

agents

in

the

team

are,

the

worse

the

performance

becomes.

We

posit

that

the

main

reason

for

this

outcome

is

that

the

observing

agents

are

unable

to

update

their

models

reliably

when

team

members

exhibit

unstable

performance..

The

optimistic

policy

yields

a

substantially

better

performance

for

the

Invariant

and

Stable

teams

than

the

majority

policy,

and

it

yields

a

slightly

better

performance

for

the

Medium

and

Mixed

teams

than

the

majority

policy

(these

results

are

significant

with

This

is

because

if

we

assume

that

agents

are

honest

and

helpful

(i.e.,

they

always

make

the

best

proposal

according

to

their

models,

and

select

the

best

of

the

proposals

communicated

by

team

members),

the

optimistic

policy

is

similar

to

a

global

optimization,

where

the

agent

that

appears

to

be

best

overall

is

selected.

In

contrast,

the

majority

policy

selects

the

most

popular

agent,

which

may

not

be

the

best

overall..

Task

performance

improves

for

Medium

and

Mixed

teams

when

agents

are

able

to

remember

observations

of

the

per-

formance

of

team

members.

This

improvement

is

larger

for

the

optimistic

voting

policy

than

for

the

majority

policy

(these

results

are

significant

with

Further,

this

improvement

is

achieved

with

only

observations

for

the

optimistic

policy,

and

with

observations

for

the

majority

policy.

This

discrepancy

may

be

caused

by

the

need

for

additional

"evidence"

in

order

to

get

several

agents

to

prefer

the

same

agent,

as

required

by

the

majority

policy.

The

performance

of

Unstable

teams

is

not

affected

by

the

voting

policy

or

the

agents'

observation

capacity,

as

the

agents

in

these

teams

exhibit

too

much

performance

variation

for

the

observer

agents

to

reach

reliable

conclusions.

Experiment

Figure

depicts

the

average

task

performance

obtained

with

three

voting

policies

as

a

function

of

NUT

and

our

four

types

of

proposal-making

behaviours

(selfish,

lazy,

corrupt

and

conservative).

Additionally,

as

above,

we

considered

the

rand

and

omni

settings

for

comparison

purposes..

We

found

that

the

proposal-making

behaviour

of

a

group

of

agents

has

a

significant

influence

on

task

performance.

As

expected,

the

optimistic

policy

compares

favourably

with

the

other

policies

when

all

agents

are

reliable.

However,

as

soon

as

agents

make

selfish

or

corrupt

proposals,

the

optimistic

policy

yields

a

performance

that

is

as

bad

as

that

obtained

with

a

random

assignment

of

agents,

i.e.,

the

rand

scenario

(Figure

(a)

and

Figure

A

similar

but

less

dramatic

result

is

obtained

for

conservative

agents

(Figure

The

reason

for

these

results

is

that

once

an

agent

proposes

an

inferior

team

member

with

a

high

attributed

performance,

this

deficient

proposal

has

a

large

influence

on

the

proposal

selection

process.

Only

for

lazy

agents,

which

propose

random

agents

with

a

random

performance,

the

optimistic

policy

yields

better

results

than

the

majority

or

weighted-observation

policy..

For

the

corrupt,

conservative

and

selfish

agents,

as

the

value

of

the

NUT

parameter

increases,

the

majority

and

weighted-observation

policies

yield

a

more

stable

task

performance

than

that

obtained

with

the

optimistic

policy.

In

addition,

one

or

two

corrupt

or

lazy

agents

have

almost

no

effect

on

task

performance

under

the

majority

and

weightedobservation

policies,

but

as

expected,

performance

deteriorates

rapidly

when

there

are

four

or

five

of

these

agents..

For

groups

with

corrupt,

lazy

and

selfish

agents,

the

majority

voting

policy

yields

slightly

better

results

than

those

obtained

with

the

weighted-observation

policy.

In

contrast,

for

groups

with

conservative

agents,

the

weightedobservation

policy

yields

better

results.

This

may

be

explained

as

follows.

The

added

weight

of

proposals

made

for

previously

observed

agents

also

favours

the

proposals

made

by

agents

who

make

observations.

These

agents

in

turn

are

able

to

update

their

models,

and

thus

make

more

accurate

proposals

than

those

made

by

conservative

agents.

We

conducted

two

experiments

as

follows.

In

the

first

experiment,

we

investigated

the

interaction

between

team

stability,

agent

observation

capacity

and

voting

policy,

and

the

impact

of

these

parameters

on

task

performance.

In

this

experiment,

agents

were

assumed

to

be

reliable..

This

assumption

was

relaxed

in

our

second

experiment,

where

we

investigated

the

impact

of

number

and

type

of

unreliable

agents

(corrupt,

lazy,

conservative

and

selfish)

and

voting

policy

on

task

performance.

In

this

experiment,

we

assumed

that

all

agents

exhibit

a

stable

performance,

and

have

an

observation

capacity

of

OC

(this

value

yields

the

best

performance

for

teams

of

stable

agents,

Section

We

ran

one

simulation

for

each

combination

of

the

simulation

parameters

in

each

experiment.

For

Experiment

we

performed

simulations

types

of

teams

values

for

OC

voting

policies),

and

for

Experiment

we

ran

simulations

types

of

unreliable

agents

values

for

NUT

voting

policies).

In

addition,

we

ran

one

simulation

for

each

of

the

benchmark

settings,

rand

and

omni.

Each

simulation

consists

of

ten

trials,

each

divided

into

runs

(we

selected

this

number

of

trials

and

runs

because

it

yields

stable

and

continuous

patterns

of

behaviour).

Each

run

consists

of

a

rescue

task

that

is

repeated

until

convergence

is

reached..

The

IR

and

M

for

each

agent

are

initialized

at

the

beginning

of

each

run.

IR

are

initialized

as

specified

by

the

type

of

the

team

(e.g.,

Stable

or

Unstable),

and

M

are

initialized

with

random

values.

We

also

conducted

experiments

where

all

the

models

are

initialized

with

a

value

of

(medium

expected

performance),

and

with

a

value

of

(high

expected

performance).

The

overall

results

are

similar

to

those

obtained

with

the

randomly

initialized

models,

except

for

the

Invariant

and

Stable

group

of

agents

and

the

initialization,

which

yield

a

worse

average

performance.

This

is

because

a

run

terminates

when

the

chosen

agent's

performance

is

repeatedly

better

than

and

so

other

agents

who

may

be

better

are

not

given

a

chance,

thereby

converging

to

a

local

maximum

(Section

The

IR

of

each

agent

remain

constant

throughout

a

run

(the

agent's

performance

is

drawn

from

the

distribution

specified

in

the

IR),

while

M

are

updated

from

the

observations

made

for

each

rescue

in

the

run..

The

process

for

reaching

convergence

works

as

follows.

At

the

beginning

of

a

run,

different

lifesavers

may

be

proposed

for

a

rescue

task

due

to

the

discrepancy

between

the

models

maintained

by

the

different

agents.

After

each

rescue,

the

agents

update

their

models

based

on

the

performance

of

the

chosen

agent.

Hence,

when

a

rescue

task

is

announced

in

the

next

turn,

more

agents

are

likely

to

propose

the

same

lifesaver

(but

not

necessarily

the

lifesaver

chosen

for

the

previous

task).

A

run

is

terminated

when

the

same

lifesaver

is

chosen

in

N

consecutive

turns

(we

have

experimented

with

N

the

results

presented

in

Section

are

for

N

Our

measure

of

task

performance

for

a

run

is

the

mean

of

the

IR

distribution

for

the

agent

on

which

the

observers

eventually

converged.

For

instance,

in

the

example

in

Table

this

agent

is

whose

IRA

(rescue)

has

mean

This

measure

reflects

the

final

outcome

of

the

combination

of

the

parameters

of

the

simulation

for

the

run

in

question.

As

expected,

the

results

for

the

rand

and

omni

settings

correspond

to

the

worst

and

best

performance

respectively,

and

are

used

as

a

benchmark

for

comparison

with

the

other

settings.

The

performance

for

the

Invariant

team

is

slightly

worse

than

that

for

the

omni

setting.

This

is

due

to

the

fact

that

agents

in

the

Invariant

team

sometimes

converge

to

a

local

maximum,

which

is

reached

when

the

agents

in

the

team

select

an

agent

that

is

not

the

best.

This

happens

when

the

agents

under-estimate

the

performance

of

the

best

agent

to

the

extent

that

it

will

never

be

proposed

by

any

agent

in

the

group,

and

hence

will

never

perform

the

task.

These

results

are

consistent

with

the

results

obtained

for

the

rand,

omni

and

default

scenarios

in

our

previous

work

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


Towards domain-independent information extraction from web tables Traditionally, information extraction from web tables has focused on small, more or less homogeneous corpora, often based on assumptions about the use of <table> tags. A multitude of different HTML implementations of web tables make these approaches difficult to scale. In this paper, we approach the problem of domain-independent information extraction from web tables by shifting our attention from the tree-based representation of web pages to a variation of the two-dimensional visual box model used by web browsers to display the information on the screen. The thereby obtained topological and style information allows us to fill the gap created by missing domain-specific knowledge about content and table templates. We believe that, in a future step, this approach can become the basis for a new way of large-scale knowledge acquisition from the current "Visual Web." The Web is an enormous source of information contained in billions of individual pages. Information extraction (IE) tries to process this information and make it available to structured queries. Most often, information extraction systems are targeted towards specific domains of interest and involve either manual or semi-automatic learning of the target examples involved. In contrast, the goal of automatic information extraction is to discover relations between data items of interest and similar data items on a large scale and independently of their domain without any training [2, 14] . * This research was supported in part by a DOC scholarship from the Austrian Academy of Sciences and by the FIT-IT programme of the Austrian Federal Ministry for Transport, Innovation and Technology.. Copyright is held by the International World Wide Web Conference Committee (IW3C2). Distribution of these papers is limited to classroom use, and personal use by others. May 8-12, 2007 , Banff, Alberta, Canada. We are currently working to improve our approach in the following ways:. • Table extraction . We are still in the process of improving the quality of our table extraction step. We are experimenting in a number of ways to bring the defined heuristics close to the decision process that a human follows when spotting aligned information on a web page. We expect that a rule-based approach working on the correctly defined set of discriminating heuristics can bring the results close to human performance.. • Table interpretation . Whereas we think that the model we use is ideally fitted for our task, we are currently working on a limited base of table types. We are currently working on an improved table phenomenology, which incorporates a greater variety of tables found on web pages. Again, as we are working on distinctive visual characteristics, we also expect to be able to considerably improve our accuracy except for some very exotic cases, which would not play an important role for large-scale knowledge acquisition anyway.. • Nested substructures. Currently we do not consider any form of nested substructures whose dominant logical elements do not consist of atomic units (Fig. 3) . We focus on locating dominant structures and regard their content as atomic units with words ordered in western reading order. Enlarging the scope of analysis also to such nested structures poses less a problem to the location and recognition, but rather the interpretation step. Our current model of tables (and other structured data) cannot be enlarged to this kind of nested data structures on the Web.. • Other spatially and visually structured data. So far, we have only focused on tables. We intend to enlarge the scale of this approach to other typologically and typographically structured data. An example is that of book list results on pages like Amazon. No spatial features are present, but distinctive typographical features like colors for attribute name and attribute value can be used for relational learning.. • Information Integration. Our described approach is only one step in a sequential series of three steps to large-scale knowledge acquisition from the Web (Fig. 7) . One of our subtasks is developing the correct infrastructure that allows us to reason on the extracted information and attempt a probabilistic information integration from web tables. In this paper, we formalized an unconventional and promising approach towards structured information extraction from the Web; in particular, from web tables. The approach uses a model of the visual representation of web pages as rendered by a web browser and, therefore, shifts the problem of information extraction from the lower level of code interpretation (HTML tag structure, CSS, JavaScript code, etc.) to the higher level of visual features (2-D topology and typography). We have also presented a model for representing web table structures along with algorithms to derive instances of the model given some arbitrary web pages. Our approach strives to perform well even without tuning for specific application domains such as the interpretation of product catalogues. We have shown this by providing a diverse test set of web tables that has been gathered by 63 students. Although our results are preliminary at the current state, we believe that applying a visual paradigm towards automatic information extraction from web tables is promising, especially given the rising complexity in the encoding of web pages on the source code level. Specifically, highly dynamic pages which tend to get more popular with the rise of Web 2.0 can not be processed without complex interpretation of the source code. Web Table Analysis. Web table analysis has become a widely researched topic on its own over the years. Penn et al. [25] define genuine uses of HTML tables as document entities where the 2-D grid is semantically significant and describe a couple of heuristics to distinguish genuine from non-genuine leaf <table> tables on web pages. Yalin Wang and Hu [33] train a classifier on content features of individual cells and non-text layout features from the HTML source to perform the same task of table location. Chen et al. [7] employ heuristic rules to filter out non-genuine tables from their test set and make assumptions about cell content similarity for table recognition and interpretation. Like most of the approaches we are aware of, their method relies on the hierarchical HTML tag structure of the documents, most notably that of <table> tags. Yang and Luk [36] describe how they extract attribute-value pairs from 1-D or 2-D tables, a notion similar to our view of 1-D and 2-D lists and tables. But for them, a 1-D table contains what they call "mixed-cell" content, which means that the attribute name (label) and value are in the same cell. We regard this data structure as nested, substructured list. Yoshida et al. [38] base their work on a general knowledge ontology and employ an expectation maximization algorithm to distinguish between attribute and value cells. They assume that tables do no contain any spanned cells. Tengli et al. [29] present an algorithm that extracts tables and differentiates between label and data cells.. All these approaches have in common that they assume that relevant tables only appear inside leaf tables, which are such <table> tags that do not contain other nested <table> tags. In contrast, Lerman et al. [22] mention that just a fraction of tables are actually created with <table> tags. In their algorithm, they leverage the list page-detail page structure present in some websites to find boundaries between records in what we would classify as a substructured 1-D list. They also mention that layout is important for table extraction, but go on to say that this means that records are separated by HTML tags. In contrast, we base our table extraction on positional information that is independent of the HTML tag structure and do not rely on particular HTML structures being present. Tijerino et al. [30] describe the automatic generation of ontologies from normalized tables, which is a structure they get after normalizing table-equivalent data. Pivk et al. [26] focus on understanding table-like structures only due to their structural dimension and transforming the most relevant table types into F-logic frames. Neither paper makes it clear to us how the described approaches locate tables on web pages in the first place.. We refer to two recent surveys on table analysis by Embley et al. [12] and by Zanibbi et al. [39] , which cover a broad range of table processing literature and illustrate the general challenges of information extraction from tables despite their primary focus on non-HTML documents.. Visual Web Page Analysis. As far as we know, the idea of analyzing the visual representation of a web page for content analysis originated in the area of web page segmentation. Yang and Zhang [37] describe an approach which derives features directly from the layout of web pages. By using a "pseudo rendering process" they try to detect "visual similarities" of HTML content objects. We fully believe in their remark that HTML tags are not stable features for analyzing structures of HTML documents. Gu et al. [16] describe a top-down approach to segment a web page and detect its content structure by dividing and merging blocks. Kovacevic et al. [19] use visual information to build up a "M-tree", a concept similar to the DOM tree enhanced with screen coordinates. They then use further defined heuristics to recognize common page areas such as header, left and right menu, footer and center of a page. Cai et al. [6] describe a web page segmentation process that uses visual information from Internet Explorer. Their VIPS algorithm segments a DOM tree based on visual cues retrieved from the browser's rendition. Cosulschi et al. [9] describe an approach that uses positional information of DOM tree elements to calculate block correspondence between web pages.. Visual web page analysis can also be increasingly found in information extraction literature, specifically on record boundary detection. Zhao et al. [41] , Zhai and Liu [40] and Simon and Lausen [28] describe different approaches for detecting repetitive patterns on web pages, which are predominantly source-code based and enhanced with visual cues. In contrast, Aumann et al. [3] describe a system that works only on a hierarchical structure of the visual representation (experiments are performed with PDF documents) and learns to recognize text fields such as author or title from manually tagged training sets of documents. Conversely, our approach does not attempt to find individual text fields, but rather, larger structures, does not require training sets and neither imposes a tree structure on web pages.. Visual Web Table Extraction. To our best knowledge, the idea of actually rendering or "executing" HTML code and using the results for detecting relational information in web tables was first mentioned by Cohen et al. [8] . The described approach, however, does not actually render web pages, but rather infers relative positional information of table nodes in an abstract table model with relative positional information deduced from the source code. In contrast, in our previous work [21] we describe a top-down web table location mechanism working exclusively on visual information obtained from the Mozilla web browser. The approach works on word bounding boxes after manipulation of the DOM tree and detects tables with the help of space density graphs and recursive application of the X-Y cut algorithm. This approach is later adapted in [20] to a bottom-up clustering algorithm starting with word bounding boxes as well. In [15] , we describe a method that works on both word and element node bounding boxes, and that is able to locate concepts of a predefined seed knowledge in web tables.. The system of this paper builds upon this last method and extends it in the following ways: (1) the approach does not need pre-defined seed knowledge and is as such completely domain-independent; (2) the approach can recognize and transform the tabular information into a format, suitable for a subsequent information integration step.

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


TDMA frame design for a prototype underwater RF communication network Very low frequency electromagnetic communication system is used in a small scale underwater wireless sensor network for coastal monitoring purposes, as recent research has demonstrated distinct advantages of radio waves compared to acoustic and optical waves in shallow water conditions. This paper describes the detailed TDMA and packet design process for the prototype sensor system. The lightweight protocol is time division based in order to fit the unique characteristics and specifications of the network. Evaluations are based on initial beach trial as well as modeling and simulations. The recent growing interests in monitoring aqueous environments have led to extensive research on underwater wireless sensor networks (UWSN). Given modern operational requirements and digital communications technology, previous work in Ref. [1] has re-evaluated role of radio frequencies electromagnetic (RF-EM) signals in the underwater sensor networks, where a case study was also conducted on a project entitled Automated Sensing Technology for Coastal Monitoring (ASTEC). The project attempted to use RF-EM waves as the communication medium. This paper will demonstrate in details the protocol and packet design regime for the prototype UWSN proposed in this case.. In order to gather data and consequently quantify the effects of coastal erosion beneath the sea surface, ASTEC project aims to create the world's first automated system for monitoring and forecasting for this purpose. Engineers have classically modeled the submerged beach profile by describing two different zones: near shore zone and offshore zone. The near shore zone is active while the offshore zone is inactive with respect to the movement of sediment, silt, sand etc. In environmental science, coastal erosions can be explicitly indicated by depth of closure (DoC) which is defined as the depth seaward where neither change in bottom elevation nor net sediment exchange between nearshore and offshore can occur significantly [2] ; the contour of such closure is approximately parallel to the coastline in most cases.. The UWSN built in this project aim to gather data and consequently quantifies the effects of coastal erosion beneath the sea surface. The primary role of sensors will be to either directly or indirectly measure the movement of sea bed sediments, so as to monitor the DoC. All measurement data will be sent to one destination node (i.e. the sink) on the shore or at any other designated position. The sink will be enabled with the function of Global System for Mobile communications (GSM) which allows all the collected data to be eventually transported to a central office for processing. This enables the remote collection and processing of the data. The system will also gather data regarding the health of the sensor nodes which will also be accessible by the user so enabling the failed nodes to be replaced as needed.. Although the usability of underwater RF technology has been quite controversial to date, it can offer great potentials in practical applications if all the system parameters are properly tuned. Unlike in acoustic systems where data rates remain relatively constant regardless of the frequency, the data rates of underwater RF communication systems are frequency-dependent and the attenuation of an EM signal increases significantly both with frequency and distance, for instance, higher frequencies although can enable higher data rates, would result in shorter transmission range [3] . Hence, when RF technology is attempted in underwater systems, the choice of operating frequency, bit rate and sensor density requires careful deliberation during the design phase. Based on respective project requirements, each design has to uniquely balance parameters such as antenna design, transmit power, duty cycle, data bandwidth, and local noise source in order to achieve an optimized solution for the specific application. This paper presents a good case of such circumstance with all the system specifications explained.. Section 2 briefly reviews the underwater RF communication characteristics. Section 3 describes the system implementation and network scenario. Section 4 discusses the protocol scheme and key issues. Section 5 demonstrates the detailed packet design process. Section 6 reveals some information of the initial beach trial and evaluates the system with modeling and simulations, and Section 7 concludes the paper. A small scale underwater wireless sensor network is designed to employ RF electromagnetic communication of which the capabilities have been re-evaluated in previous research. The network has a multi-hop static topology for coastal monitoring purposes which will work under shallow water conditions. TDMA is chosen as the control protocol for the half-duplex pattern communication network. This paper described the details of the MAC layer protocol and the use of a flexible TDMA framing technique that was used to gather the data from the prototype sensor network. The basic aim of designing the prototype was to maintain as much flexibility as possible because of the many unknowns in the hostile deployment environment. The initial beach trial demonstration and the analysis of the simulation results have led to the conclusion of feasibility and effectiveness of the designated system and network archi- tecture. Future research should further explore the different aspects of network performance, such as scalability, necessity for reliable transmission, and comparing the chosen protocol with other potential options.

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