A BAYESIAN NETWORK TO STRUCTURE A DATA QUALITY

MODEL FOR WEB PORTALS

Angélica Caro

, Coral Calero

, Houari Sahraoui

2,3

, Ghazwa Malak

, Mario Piattini

Universidad del Bio Bio, Departamento de Auditoria e Informática, Chillán, Chile

Alarcos Group – Computer Science Dept., Universidad de Castilla-La Mancha

Paseo de la Universidad, 4 – 13071 Ciudad Real (Spain)

Dept. d’Informatique et de Recherche Opérationnelle, Université de Montréal

CP 6128 succ. Centre Ville, Montréal QC H3C 3J7 Canada

Keywords: Data Quality, Information Quality, Web Portals, Bayesian Network, Data Quality Model.

Abstract: The technological advances and the use of the in

ternet have favoured the appearance of a great diversity of

web applications, among them Web portals. Through them, organizations develop their businesses in a

highly competitive environment. One decisive factor for this competitiveness is the assurance of its data

quality. In previous works, a data quality model for Web portals has been developed. The model is

represented as a matrix that links the user expectations of data web quality to the portal functionalities. Into

this matrix a set of 34 attributes where classified. However, the quality attributes on this model have not an

operational structure, necessary to be used actual assessment. In this paper we present how we have

structured these attributes by means of a probabilistic approach, using Bayesian Networks. The final

objective is to use the Bayesian network obtained for evaluating the quality of a data portal (or a subset of

its characteristics).

1 INTRODUCTION

During the past decade, an increasing number of or-

ganizations have established Web portals to comple-

ment, substitute or widen existing services to their

clients. In general, portals provide users with access

to different data sources (providers) (Mahdavi et al.,

2004), as well as to on-line information and informa-

tion-related services (Yang, 2004). Moreover, they

create a working environment where users can easily

navigate in order to find the information they need to

perform their operational or strategic tasks and make

decisions (Collins, 2001). The users of Web portals

need to ensure that this information is appropriate

for the use they make of it.

In the literature, the concept of Information or

Data Quality (DQ) is

often defined as “fitness for

use”, i.e., the ability of a data collection to meet user

requirements (Strong et al., 1997; Cappiello et al.,

2004). Recently, due to the particular nature of Web

applications the research community started

studying the subject of data quality on the Web

(Gertz et al., 2004).

However, there are no works on data quality that

address the particular conte

xt of Web portals, in

spite of the fact that some work highlights the data

quality as one of the relevant factors in the quality of

a portal (Moraga et al., 2004; Yang, 2004).

Likewise, except for few work in the data quality

area, like (Wang and Strong, 1996; Burgess et al.,

2004; Cappiello et al., 2004), most of the works not

targeted the quality from the data consumers

perspective (Burgess et al., 2004).

In a previous work, we have developed a Portal

Data Quality Mo

del (PDQM), focused on the data

consumer’s perspective (Caro et al. 2006). This

model are composed of 34 DQ attributes.

The definition of a model does not mean that it

can b

e operational, i.e., it can be used to assess the

quality of web portals in our case. To reach this

goal, we need to define a structure that allows from

the one hand, to evaluate each attributes using

147

Caro A., Calero C., Sahraoui H., Malak G. and Piattini M. (2006).

A BAYESIAN NETWORK TO STRUCTURE A DATA QUALITY MODEL FOR WEB PORTALS.

In Proceedings of the First International Conference on Software and Data Technologies, pages 147-152

 SciTePress

measures and, from the other hand, to combine

attribute evaluations to access the portal quality.

Considering the uncertainty inherent to the

quality perception, we propose to use a probabilistic

approach (Bayesian network) to structure, refine and

represent our model.

This rest of this paper is organized as follows.

Section 2 presents a brief summary of PDQM. The

description of Bayesian networks (BN) and their use

to structure our model is presented in Section 3.

Section 4 shows the process used for representing a

new version of PDQM as a Bayesian network. Fina-

lly, Section 5 summarizes and concludes the paper.

2 PDQM

PDQM is a data quality model for Web portals

focused in three key elements:

Data consumer perspective. Represented by DQ

expectations of data consumer on Internet, stated in

(Redman, 2000). These expectations are organized

into six categories: Privacy, Content, Quality of va-

lues, Presentation, Improvement, and Commitment.

Web DQ attributes. We have identified DQ

attributes which have been proposed for different

domains in the context on the Web. The idea was to

take advantage of work already carried out in the

Web context and apply it to Web portals.

Web portal functionalities. Web portals present ba-

sic functionalities to data consumer deploying their

tasks. Under our perspective, the consumer judges

the data by using the application functionalities. So,

we used the web portal functions proposes in

(Collins, 2001) considering them as basics in our

model. These functions are: Data Points and

Integration, Taxonomy, Search Capabilities, Help

Features, Content Management, Process and Action,

Collaboration and Communication, Personalization,

Presentation, Administration, and Security.

We produced the PDQM model through a three-

phase process. In the next subsections we explain

each of these phases with their results.

2.1 Web DQ Attributes

The first phase consisted in gathering Web DQ

attributes from the literature. For this we have made

a systematic review of the relevant literature. Then,

we selected works proposed for different domains in

the Web context (Web sites (Katerattanakul and

Siau, 1999; Eppler et al., 2003; Moustakis et al.,

2004), integration of data (Naumann and Rolker,

2000; Bouzeghoub and Peralta, 2004), e-commerce

(Katerattanakul and Siau, 2001), Web information

portals (Yang et al., 2004), cooperative e-services

(Fugini et al., 2002), decision making (Graefe,

2003), organizational networks (Melkas, 2004) and

DQ on the Web (Gertz et al., 2004)). The idea was

to take advantage of the work already carried out in

the Web context and apply it to Web portals. As

result and after summarizing the collected initial set

of attributes, we obtained 41 DQ attributes (see the

top of Table 1).

2.2 Definition of a Classification

Matrix for Web DQ Attributes

In the second phase, we have built a matrix for the

classification of the DQ attributes obtained in

previous phase. This matrix relates two basic aspects

considered in our model: the data consumer

perspective by means their DQ expectations on

Internet (Redman, 2000) and the basic

functionalities in a Web portal. On this matrix we

carried out an analysis of what expectations were

applicable in each different functionality of a Web

portal, represented in Figure 1 with a “√” mark.

Figure 1: Matrix to classify the Web DQ attributes.

2.3 Classification of Web DQ

Attributes in the Matrix

In the third phase, we used the obtained matrix to

classify the Web DQ attribute identified in phase 1.

Then for each relationship between functionality and

expectation, we assigned the DQ attributes that

could be used by the data consumer to evaluate the

DQ in a portal. We did it by studying the

appropriateness of each attribute (based on its

definition), in relation to the objective of each portal

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Table 1: Data quality attributes assigned for functionality.

functionality and the user DQ expectation. On Table

1, we have summarized the attributes assigned for

functionality.

On the other hand some attributes, have not

assigned, because in our analysis they are result not

be important or visible for the data consumer. And

therefore, the first version of PDQM has 34 DQ

attributes.

3 BAYESIAN NETWORKS

A BN is a directed acyclic graph, whose nodes are

the uncertain variables and edges are the causal or

influential links between variables. A conditional

probability functions model the uncertain relation-

ship between each node and its parents (Neil et al.,

2000). In our context, BNs offer an interesting fra-

mework with which it is possible to: (1) Represent

the interrelations between attributes in an intuitive

and explicit way by connecting influencing factors

to influenced ones. Such a representation facilitates

the comprehension of the model, its validation, its

evolution and its exploitation, (2) Circumvent the

problems of subjectivity uncertainty, (3) Actually

use the obtained network to predict/estimate the

quality of a portal, and (4) Isolate responsible factors

in the case of low quality.

Another interesting property of the Bayesian

approach is the fact that it considers the probability

as being a dynamic entity that can be updated as

more data arrive (self learning mechanism). New

data may naturally improve the degree of belief in

certain propositions (Baldi et al., 2003).

Consequently, a BN model is particularly adapted to

the changing domain of web portals.

Building a BN for a particular quality model can

be done in two stages: (1) build the graph structure

and (2) define the node probability tables for each

node of the graph. In this paper we focus on the first

stage (see next section). To this end, we use the

approach proposed by (Malak et al., 2006) for

building BN for web quality models.

4 STRUCTURING PDQM USING

A BAYESIAN NETWORK

In its current state PDQM is defined as a set of DQ

attributes without a structure that allows it to be used

as an evaluation framework for web portals. To

structure PDQM (in the form of a BN), we have de-

cided to use the draft of the standard ISO/IEC 25012

(ISO-25012, 2006). Our choice is basically motive-

ted by two facts. First, ISO/IEC 25012 defines DQ

requirements and describes DQ characteristics for

any computer system application (i.e.: e-govern-

ment, e-business, e-commerce). Second, the attribu-

tes of the standard are already structured in a hierar-

chy which can be used for our model. Thus, PDQM

can be seen as a specialization of this standard.

In ISO/IEC 25012, there are three different ways

of viewing DQ: Internal DQ, External DQ and DQ

in Use. It categorises internal and external DQ attri-

butes into six characteristics (functionality, reliabi-

lity, usability, efficiency, maintainability and porta-

bility), which are further subdivided into subcharac-

teristics. DQ in use is categorized into 4 characteris-

A BAYESIAN NETWORK TO STRUCTURE A DATA QUALITY MODEL FOR WEB PORTALS

149

tics effectiveness, productivity, safety and satisfac-

tion), which are refined into sub-characteristics.

The ISO/IEC 25012-guided generation of a BN

for PDQM was performed following a three-phase

process. In the first 2 phases, we matched the DQ

attributes of PDQM with the sub-characteristics of

ISO/IEC 25012. These 2 phases produced a hierar-

chy-like model. In the third phase, we studied and

integrated the influence relationships that can exist

between the attributes of PDQM. The final result

was a graph-like model. The 3 phases are explained

together with their results in the next 3 subsections.

4.1 Names Matching

In the first phase, we started to build the BN

structure by identifying which attributes in PDQM

are also included in the ISO/IEC 25012 as sub-

characteristics. For doing this, we have matched the

attributes in PDQM with the sub-characteristics in

the standard by means of the coincidence between

their names. For instance, the Accuracy attribute

(which is part of PDQM) is present in the standard

as a sub-characteristic of the Functionality

characteristic. Then, in PDQM structure, we have

considered, at the Internal/External DQ category, the

Functionality characteristic and inside this, the

Accuracy sub-characteristic.

This BN contains 4 levels: the model (PDQM),

the quality views (I/E_DQ and DQ_in_use), the

characteristics that have sub-characteristics present

in PDQM, and the attributes of PDQM that match

sub-characteristics in the standard (see Figure 3).

4.2 Definition Matching

In the first phase, we used a direct name mat-ching

to structure PDQM attributes. The aim of this second

phase was to complete the structure obtained. This

was done by finding correspondences between the

not-yet-assigned attributes and the characteristics in

the standard. To this end, we used definition

matching between PDQM and ISO/IEC 25012. To

illustrate this phase, let’s take the following

example. For instance, the attribute Flexibility was

associated to the characteristic Portability and we

add this last to PDQM. This obeys to the following

analysis. ISO/IEC defines Portability as: “The

capability of data to be transferred from one

technological environment to another”. In PDQM

Flexibility is defined as: “The extent to which data

are expandable, adaptable, and easily applied to

others needs”. So, in our opinion, flexibility is

necessary for the Portability of data. The summary

of the matching performed in the two first phases is

presented in Table 2 (three first columns).

Figure 3: BN obtained in the phase 1.

Table 2: Attributes for the next level in the PDQM.

4.3 Causal Relationships

Establishing

The third phase was dedicated to the search for

causal relationships between the attributes of

PDQM. We used the definitions of the attributes to

establish these relationships.

For instance, the PDQM attribute Concise Repre-

sentation is defined as “the extent to which data are

compactly represented without elements superfluous

or not related”. Then, based on this definition, we

established a causal relation with the ISO subcharac-

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5 CONCLUSIONS

teristics Understandability, Applicability and Inter-

pretability. Indeed, if the data are compactly repre-

sented and without unnecessary elements, they can

be more easily understood, applied and interpreted

(Katerattanakul and Siau, 2001). Table 2 shows the

attributes that were incorporated in this phase. And

the Figure 4 shows the final BN generated.

In this paper, we have proposed an operational

model for web portal quality assessment. This model

is defined as a Bayesian network that was build

using the non-structured PDQM model. PDQM is a

DQ model containing 34 attributes that were

selected specifically for web portals. The BN model

was obtained following a three-phase process guided

by the ISO/IEC 25012 standard.

4.4 Probability Definition

The choice of a BN-like model is motivated by

the fact that many issues in quality assessment are

circumvented: threshold value definition, metric

combination, and uncertainty.

To be operational, the BN obtained needs to be

supplemented with the probabilities. As stated by

Malak et al. in (Malak et al., 2006), there are two ty-

pes of probabilities that must be defined: input-node

probabilities and intermediate-node probabilities.

We are currently working on the definition of the

parameter of the network, i.e., probabilities, using a

hybrid approach that combines expert judgment with

learning mechanisms.

Intermediate-node probabilities are obtained

through tables that define conditional probabilities

of the different values that can be taken by quality

characteristic of the node knowing the values of the

characteristics of the parent nodes. These tables are

defined using expert judgment and refined by the

self-earning mechanism as the new portals are

evaluated.

One of the advantages of our model is its

flexibility. Indeed, the model a global framework

that can be adapted for both the goal and the context

of the evaluation. From the goal perspective, the

user can choose the sub-network that evaluates the

characteristics he is interested in. From the context

point of view, the parameters (probabilities) can be

changed to consider the specific context of the

evaluated portal. This operation can be done using

available historical data from the organization.

Characteristics that are represented by input-

nodes are those that can be directly measured from

the web portals. Input-node probabilities are

produced by a transformation of numerical-value

measures into probabilities. Consider the node

Response_Time in our model. The actual time can be

classified into three categories: short, medium, and

long. Using a fuzzy logic-based clustering

algorithm, we can derive a probabilistic classifier

that calculate respectively the probabilities that a

response time value of a particular web belongs to

each of the categories (Malak et al., 2006).

To evaluate our model, we are currently

designing an experimental study. This study will

concern a large number of portals and will involve a

set of portal-user subjects. The goal of the study is to

compare the subjective judgments of the subjects

with the evaluation results produced by our model.

Figure 5: PDQM represented in a BN.

A BAYESIAN NETWORK TO STRUCTURE A DATA QUALITY MODEL FOR WEB PORTALS

151

ACKNOLEDGEMENTS

This research is part of the following projects:

CALIPO (TIC2003-07804-C05-03) supported by the

Dirección General de Investigación of the Ministerio

de Ciencia y Tecnología (Spain) and DIMENSIONS

(PBC-05-012-1) supported by FEDER and by the

“Consejería de Educación y Ciencia, Junta de

Comunidades de Castilla-La Mancha” (Spain). This

work was performed during the stay of Houari

Sahraoui in the University of Castilla-La Mancha

under the “Programa Nacional De Ayudas Para La

Movilidad de Profesores en Régimen de año

sabático”, from Spanish

Ministerio de Educación y

Ciencia, REF: 2004-0161.

REFERENCES

Baldi, P. et al. (2003). Modeling the Internet and the Web;

Probabilistic Methods and Algorithms. Wiley

Bouzeghoub, M. and V. Peralta (2004). A Framework for

Analysis of data Freshness. International Workshop on

Information Quality in Information Systems,

(IQIS2004), Paris, France, ACM.

Burgess, M., et al. (2004). Quality Measures and The

Information Consumer. Proceeding of the Ninth

International Conference on Information Quality.

Cappiello, C., et al. (2004). Data quality assessment from

the user´s perspective. International Workshop on

Information Quality in Information Systems,

(IQIS2004), Paris, Francia, ACM.

Caro , A., et al. (2006). Defining a quality model for portal

data. International Conference on Web Engineering,

ICWE-2006, Palo Alto, California, USA.

Collins, H. (2001). Corporate Portal Definition and

Features, AMACOM.

Eppler, M., et al. (2003). Quality Criteria of Content-

Driven Websites and Their Influence on Customer Sa-

tisfaction and Loyalty: An Empirical Test of an Infor-

mation Quality Framework. Proceeding of the Eighth

International Conference on Information Quality.

Fugini, M., et al. (2002). Data Quality in Cooperative Web

Information Systems.Personal Communication.

citeseer.ist.psu.edu/fugini02data.html.

Gertz, M., et al. (2004). "Report on the Dagstuhl Seminar

"Data Quality on the Web"." SIGMOD Record vol.

33, Nº 1: 127-132.

Graefe, G. (2003). Incredible Information on the Internet:

Biased Information Provision and a Lack of

Credibility as a Cause of Insufficient Information

Quality. Proceeding of the Eighth International

Conference on Information Quality.

ISO-25012 (2006). " ISO/IEC 25012: Software

Engineering - Software Quality Requirements and

Evaluation (SQuaRE) - Data Quality Model (Draft)."

Katerattanakul, P. and K. Siau (1999). Measuring

Information Quality of Web Sites: Development of an

Instrument. Proceeding of the 20th International

Conference on Information System.

Katerattanakul, P. and K. Siau (2001). Information quality

in internet commerce desing. Information and

Database Quality. M. Piattini, C. Calero and M.

Genero, Kluwer Academic Publishers.

Mahdavi, M., et al. (2004). A Collaborative Approach for

Caching Dynamic Data in Portal Applications. Pro-

ceedings of the 5th conference on Australian database.

Malak, G, Sahraoui, H, Badri, L, Badri, M. (2006). A

Proposal of a Probabilistic Framework for Web-Based

Applications Quality, Proceedings of the 10th ECOOP

Workshop on Quantitative Approaches in Object-

Oriented Software Engineering, (QAOOSE06).

Melkas, H. (2004). Analyzing Information Quality in

Virtual service Networks with Qualitative Interview

Data. Proceeding of the Ninth International

Conference on Information Quality.

Moraga, M. Á., et al. (2004). Comparing different quality

models for portals. To appear on Online Information

Review, 2006.

Moustakis, V., et al. (2004). Website Quality Assesment

Criteria. Proceeding of the Ninth International

Conference on Information Quality.

Naumann, F. and C. Rolker (2000). Assesment Methods

for Information Quality Criteria. Proceeding of the

Fifth International Conference on Information Quality.

Neil, M., Fenton, N.E., Nielsen, L., (2000). Building

large-scale Bayesian Networks. The Knowledge

Engineering Review, 15(3). 257-284

Pressman, R. (2001). Software Engineering: a

Practitioner's Approach. 5/e, McGraw-Hill.

Redman, T. (2000). Data Quality: The field guide. Boston,

Digital Press.

Strong, D., et al. (1997). "Data Quality in Context."

Communications of the ACM Vol. 40, Nº 5: 103 -110.

Wang, R. and D. Strong (1996). "Beyond accuracy: What

data quality means to data consumers." Journal of

Management Information Systems; Armonk; Spring

1996 12(4): 5-33.

Yang, Z., et al. (2004). "Development and validation of an

instrument to measure user perceived service quality

of information presenting Web portals." Information

and Management. Elsevier Science 42: 575-589.

ICSOFT 2006 - INTERNATIONAL CONFERENCE ON SOFTWARE AND DATA TECHNOLOGIES

152