=Paper=
{{Paper
|id=Vol-484/paper-10
|storemode=property
|title=SPDQM: SQuaRE-Aligned Portal Data Quality Model
|pdfUrl=https://ceur-ws.org/Vol-484/paper10.pdf
|volume=Vol-484
}}
==SPDQM: SQuaRE-Aligned Portal Data Quality Model==
https://ceur-ws.org/Vol-484/paper10.pdf
SPDQM: SQuaRE-Aligned Portal Data Quality Model
Carmen Moraga1, Mª Ángeles Moraga1, Angélica Caro2 and Coral Calero1,
1
Alarcos Research Group – Institute of Information Technologies & Systems,
Paseo de la Universidad 4, 13071 Ciudad Real, Spain
Carmen.Moraga@alu.uclm.es, {MariaAngeles.Moraga, Coral.Calero}@uclm.es
2
Department of Computer Science and Information Technologies,
University of Bio Bio, Chillán, Chile
mcaro@ubiobio.cl
Abstract. Web portals are currently an important means to access Internet
information. The use of Web portals permits a vast amount of data to be
obtained rapidly. However, the quality of the data recovered by the user is
fundamental. We therefore propose a thesis in which a model, denominated as
SPDQM (SQuaRE Portal Data Quality Model) will be defined. The proposed
model will be based on a previous model, PDQM (Portal Data Quality Model)
and the SQuaRE (Software product Quality Requirements and Evaluation)
standard. Finally, upon the model‘s completion, an automatic tool with which to
asses the data quality (DQ) in Web portals will be developed.
Keywords: Web Portal, data quality, quality model.
1 Introduction
A Web portal is a Website or service that offers a broad array of resources and
services for customers and business partners [1]. Many companies currently use Web
portals to offer their products, thus providing users with 24-hour access in which to
buy them. However, users need to know whether the data offered by Web portals are
updated, reliable, correct, and so on. This is not only important for consumers but also
for providers. The aforementioned reasons led us to consider the necessity of a Web
portal data quality model.
In order to develop this model it is first necessary to study the existing research
into data quality in general and the data for Web portals in particular.
With regard to data quality, SQuaRE came into existence some months ago.
SQuaRE was selected in this paper, because it is the most recent series of
International Standards in which a data quality model is defined. We therefore believe
that it is necessary to consider the quality characteristics identified in this standard.
With regard to data quality in Web portals, we should highlight the PDQM model.
However, its definition is previous to SQuaRE, it is specific to only one type of Web
portal: that of universities, and it has only been partially implemented.
PDQM and SQuaRE will therefore be used as a basis to develop a more complete
DQ model for Web portals.
This article is organized as follow. In Section 2, a background is presented, while
the thesis proposal is presented in Section 3.
2 Background
This Section presents relevant proposals for our work.
2.1 PDQM (Portal Data Quality Model)
As was previously mentioned, the PDQM (Portal Data Quality Model) model will be
used as a starting point. PDQM is focused on the perspective of the data consumer.
The development of PDQM was divided into two stages: the theoretical definition and
the operational definition of the model [2].
The goal of the theoretical definition was to determine a set of DQ characteristics
that are relevant to data consumers when evaluating the DQ of any Web portal. To do
this, a set of DQ characteristics proposed in literature was chosen to evaluate DQ in a
Web context and a selection of the most relevant characteristics for a Web portal was
then defined. This set was empirically validated, resulting in the final set of DQ
characteristics for the model. The operational version of PDQM was obtained by first
organizing the characteristics into four DQ categories:
- Intrinsic, which denotes that data have quality in their own right.
- Operational, which emphasizes the importance of the role of systems; that is, the
system must be accessible but secure.
- Contextual, which highlights the requirement which states that data quality must
be considered within the context of the task in hand.
- Representational, which denotes that the system must present data in such a way that
they are interpretable, easy to understand, and concisely and consistently represented.
In each category, influential relationships were then established between the
characteristics to determine which characteristics were dependent on other characteristics.
As a result of this, a BN (Bayesian Network) was obtained which organizes the 33
DQ characteristics into four network fragments (one for each DQ category). The
problem is that PDQM was only created for university Web portals and, up to now,
only evaluates the representational category.
2.2 ISO/IEC 25012
SQuaRE is a set of International Standards which consists of different divisions. Our
model will be based on the ISO/IEC 25012 which proposes a data quality model that
defines fifteen characteristics considered from two points of view: inherent and
system dependent [3].
Inherent data quality refers to the degree to which quality characteristics of data
have the intrinsic potential to satisfy stated and implied needs when data is used under
specified conditions [3]. The characteristics in this set are specifically: “Accuracy”,
“Completeness”, “Consistency”, “Credibility” and “Currentness”. System dependent
data quality refers to the degree to which data quality is attained and preserved within
a computer system when data is used under specified conditions [3]. The
characteristics in this set are specifically: “Availability”, “Portability” and
“Recoverability”. The set of characteristics for both points of view is composed of
“Accessibility”, “Compliance”, “Confidentiality”, “Efficiency”, “Precision”,
“Traceability” and “Understandability”.
3 SPDQM (SQuaRE-Aligned Portal Data Quality Model)
This Section presents our thesis proposal:
- Problems to solve: As was explained in the introduction, Internet has increased the
amount of data that can be obtained through the network and the ability to provide
and obtain information from several sources. Web portals thus serve as an
important means to access information. Web portals have therefore undergone an
evolution and currently provide a variety of services. One of the aims of many
Web portals is to select, organize and distribute content (information or other
services and products) in order to satisfy their users/customers [4]. These users
wish to recover data with an acceptable level of quality. Web portals developers
should therefore take data quality into consideration. Otherwise, if users recover
data which are not-up-date or are incorrect, the next time they need information
they will probably not access the same Web portal.
- Objective of the proposal: The aim of this proposal is to create a data quality model
aligned to SQuaRE and develop a tool which implements SPDQM and evaluates the
DQ in a Web portal.
- Research methodology: The research methods used are:
Survey: The set of data quality characteristics which are relevant to the existing
proposal have been obtained through a survey, based on the steps of
Kitchenham [5]. Since PDQM is used as a starting point, our survey covered the
time period between 01/01/2006 and 31/12/2008 (the search in PDQM covered
the period prior to 2006). As a result, we obtained that 39 characteristics were
relevant for the Web portal context.
Methodology to develop a quality model: the quality model will be defined by
using a methodology which is being developed by colleagues at the University
of Castilla-La Mancha and the University of Málaga.
Methodology to define the measures: [6] identifies three activities with which to
correctly define the measures. These activities are:
• Measures definition: this is carried out by considering the specific characteristics
that we wish to measure and the experience of designers and users.
• Theoretical validation: this helps us to discover when and how to apply the measures.
• Empirical validation: this proves the practical utility of the proposed measures.
This methodology will be used to define the necessary measures.
- Model: The model was developed by using both the set of characteristics proposed
in PDQM and ISO/IEC 25012 and those obtained as a result of our survey. Due to
the survey selects the DQ characteristics for Web in general, the next step is to
refine the set of obtained characteristics and to study the applicability of the
characteristics to the Web portal context. Once the characteristics which are most
suitable for Web portals had been chosen it was necessary to resolve conflicts.
This was done by detecting both those characteristics which have the same name
but a different meaning and those characteristics which have a different name but
refer to the same meaning. And when a characteristic has an only subcharacteristic,
the subcharacteristic is removed and it is taken into account in the definition of the
characteristic. As a final result, we have obtained 42 DQ characteristics (see Table 1).
Table 1: SPDQM
Point of view Category Characteristic Subcharacteristic
Accuracy
Objectivity
Credibility:
Reputation
Traceability
Currentness
Expiration
Intrinsic: it denote that data have quality Completeness
Inherent
in their own right Consistency
Accessibility
Compliance
Confidentiality
Efficiency
Precision
Understandability
Availability
Interactive
Accessibility: Ease of operation
Operational: it emphasized the importance
Customer Support
of the role of Systems; that is, the system
must be accessible but secure Verifiability
Confidentiality
Portability
Recoverability
Reliability
Validity:
Scope
Applicability
Value-added: Flexibility
Novelty
Contextual: it highlights the requirement Novelty
Relevancy:
which states that data quality must be Timeliness
System Dependent
considered within the context of the task Specialization
in hand Usefulness
Efficiency
Effectiveness
Traceability
Compliance
Precision
Concise Representation
Consistent Representation
Representational: it denotes that the Interpretability
system must present data in such a way
Amount of data
that they are interpretable, easy to Understandability:
Documentation
understand, and concisely and
consistently represented Organization
Attractiveness
Readability
Having identified all the characteristics there are two possibilities to continue with
the operationalization of SPDQM:
We can classify the characteristics according to ISO/IEC 25012 as being
“Inherent” and “System Dependent”. We would therefore concentrate on both
(“Inherent” and “System Dependent”), and would define measures for each of
its characteristics. For some characteristics, the measures would be derived
from the user’s opinions. However, we would like that the vast majority of the
measures were automatable to allow us to develop a tool which is capable of
evaluating a Web portal. The tool will provide us with values for each indicator
that will have been defined (one for each characteristic). The tool will also
permit the user to give more or less importance to each indicator. For example,
for each indicator it is possible to ask the user if s/he believes it to be very
important, not very important or not at all important. For the subjective
measures, the tool will ask to the users, their opinion. The tool will use these
values to calculate the final value of the characteristics associated with the
“Inherent” and the “System Dependent” points of view.
The second option is to attempt to create an extension of PDQM that is aligned
with SQuaRE. To do this we would consider the “inherent” point of view of the
ISO/IEC 25012 which corresponds with PDQM’s intrinsic category, and
“System Dependent” which corresponds with the other three categories (see
Table 1). Therefore, by following the creation of a bayesian network as that of
PDQM, we would have to add new characteristics and group them with a
maximum of three entries per node. The network would thus have a first and
second level with the identified characteristics and the necessary artificial
nodes. A third level would contain the PDQM categories, and a fourth level
would contain those of ISO/IEC 25012.
We have not yet decided which option is most suitable.
- Automatic tool: We will create an automatic tool to evaluate the DQ in Web
portals. This tool will calculate the level of DQ based in measures calculated
automatically from the web portal and other measures obtained from user´s input
(like a questionnaire). The goal of this automatic tool is to facilitate the developer’s
task when incorporating data quality into Web portals and to provide the users with
the DQ level of a specific Web portal.
- Contributions to Web Engineering: This model could be used by Web portal
designers to improve data quality, promote good practices in DQ on the Web,
come up with data cleaning techniques, develop patterns for data refinement or
eliminate unnecessary data. Thus, the automatic tool will be developed to provide
us with the necessary guidelines to carry out those improvements.
Acknowledgments. This work is part of the INCOME project (PET2006-0682-01)
supported by the Spanish Ministerio de Educación y Ciencia, by the IVISCUS project
(PAC08-0024-5991) supported by Consejería de Educación y Ciencia (JCCM) and by
VIMECUS (TC20080556) supported by the University of Castilla-La Mancha.
PhD Student: Carmen Moraga; Supervisors: Mª Ángeles Moraga, Angélica Caro
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