A SYSTEMATIC LITERATURE REVIEW OF HOW

TO INTRODUCE DATA QUALITY REQUIREMENTS

INTO A SOFTWARE PRODUCT DEVELOPMENT

César Guerra-García, Ismael Caballero and Mario Piattini

ALARCOS Research Group, Department of Information Technologies and Systems(UCLM)

Paseo de la Universidad 4, Ciudad Real, Spain

Keywords: Data Quality, Requirements Specification, Systematic Review.

Abstract: In recent years many organizations have come to realize the importance of maintaining data with the most

appropriate levels of data quality when using their information systems (IS). We therefore consider that it is

essential to introduce and implement mechanisms into the organizational IS in order to ensure acceptable

quality levels in its data. Only by means of these mechanisms, will users be able to trust in the data they are

using for the task in hand. These mechanisms must be developed to satisfy their data quality requirements

when using specific functionalities of the IS. From our point of view as software engineering researchers,

both these data quality requirements and the remaining software requirements must be dealt with in an

appropriate manner. Since the goal of our research is to establish means to develop those software

mechanisms aimed at managing data quality in IS, we decided to begin by carrying out a survey on related

methodological and technical issues to depict the current state of the field. We decided to use the systematic

review technique to achieve this goal. This paper shows the principal results of the survey, along with the

conclusions reached.

1 INTRODUCTION

Several authors have reported the problems caused

by inadequate levels of data quality (DQ) in the use

of IS (Caballero, Caro et al. 2008). These problems

make a negative impact on an organization‟s

performance, and additionally involve, among other

things, certain types of damages, and an increasingly

higher cost in economical terms (Laudon 1986;

Wang, Storey et al. 1995; Eppler and Helfert 2004).

Once organizations become aware of this situation,

they are willing to eradicate these kinds of problems.

As a solution, Karel et al. propose that it is

necessary to implement mechanisms by means of

specific Data Quality Software (Karel, Moore et al.

2009). The Data Quality Software capabilities

include data cleansing, standardization, matching,

merging, enrichment and data profiling. However,

these are “post-mortem” solutions and, although

widely used, they are costly to buy and to

implement. In addition, they are not focused on

specific users‟ data quality requirements, which

could embrace different data quality dimensions,

such as those proposed by Strong et al. in (Strong,

Lee et al. 1997) or those that appear in ISO/IEC

25012 (ISO-25012 2008), but solely on what are

commonly called intrinsic requirements such as

completeness, or accuracy. The latter are necessary

but are not sufficient for a broader kind of related

data quality problems, in which data quality

requirements go beyond these intrinsic data quality

dimensions.

We propose that these kinds of problems require

preventive action (such as avoiding storing data

which are not reliable or believable) rather than

corrective actions (such as the use of data cleansing

tools). We agree that corrective actions are

necessary (Bertino, Dai et al. 2009), but they imply

greater costs to organizations, e.g. the time and

money necessary to execute cleansing processes on

data that could already be used within a business

process, in which unproductive costs might be

incurred for nothing.

The goal of our research is, therefore, to discover

how to introduce the appropriate mechanisms by

implementing preventive actions at the point-of-

entry of organizational IS software. These

Guerra-Garc

ıa C., Caballero I. and Piattini M. (2010).

A SYSTEMATIC LITERATURE REVIEW OF HOW TO INTRODUCE DATA QUALITY REQUIREMENTS INTO A SOFTWARE PRODUCT DEVELOP-

MENT.

In Proceedings of the Fifth International Conference on Evaluation of Novel Approaches to Software Engineering, pages 12-19

 SciTePress

mechanisms might make it possible to attain a trade-

off between preventive and “post-mortem”

corrective action, thus minimizing investment or

unnecessary costs.

We are conscious that preventive mechanisms

must exist as part of the IS development, which

requires bringing together specific software

requirements with specific data quality requirements

for the software being developed: it is important to

delimit how software can be enhanced in order to

satisfy data quality requirements. To achieve this

goal, we must first identify existing proposals (both

methodological and technological) that have dealt

with some kind of solution for the introduction of

DQ requirements management as part of IS software

development.

To assist in this task, we have decided to use

Systematic Review (SR) techniques in order to attain

a strict view of the relevant literature. More

precisely, we have decided to follow the formal

Systematic Review protocol template proposed by

Biolchini et al. in (Biolchini, Mian et al. 2007), since

it is one of the most widely used techniques in the

field of Software Engineering.

An SR focuses on integrating empirical research

with the aim of creating generalizations. This

integration challenge involves specific objectives

which allow the researcher to critically analyze the

data found, resolving conflicts in the material of the

literature involved, and identifying aspects in order

to plan future research areas.

The descriptions for the different phases of

Biolchini et al.‟s protocol are:

1. Planning, which primarily focuses on

defining the research objectives, the selection of

information sources and the definition of the

inclusion and exclusion criteria of studies.

2. The Execution phase, which focuses on the

selection and evaluation of the studies found, along

with extracting information from the selected

studies.

3. The results analysis phase, which is

responsible for analyzing and presenting the results

according to different criteria and perspectives that

will facilitate their understanding and subsequent

use.

This paper shows the results of the execution of

an instance of this protocol for dealing with the

specification and modeling of DQ requirements, and

the consequent conclusions reached after analysing

these results. We have structured the paper in order

to present the main issues of each of the stages of the

protocol in each section.

2 PLANNING THE SYSTEMATIC

REVIEW

The expected outcome at the end of the SR is that of

presentation the state-of-the-art of existing research

proposals for the specification and modelling of DQ

requirements, published in the resources available.

Once the results have been obtained, the main

beneficiaries of this work will be those people

related to software development, such as: systems

analysts, designers, programmers and project

managers, in addition to, academics and researchers

related to the data quality area, and other relative

areas such as quality in Information Systems and

Requirements Engineering.

2.1 Question Formularization

The research question which has motivated the SR

is: “are there any works that propose mechanisms

(both methodological and technological) for the

specification, representation and incorporation of

data quality requirements during the process of

developing a software product?”

In order to seek a response to our research

question, we elaborated a list with keywords, which

could be used when querying the different search

engines of the bibliographic resources in hand.

These keywords are shown in Table 1.

Table 1: Keywords.

Data Quality Dimension

Data Quality Metadata

Data Quality Requirement

Data Quality Framework

Data Quality Metamodel

Data Quality Methodology

Data Quality Modeling

Data Quality Dimensions

2.2 Resource Selection

In this section, we show the set of criteria for the

selection of resources, the search methods and the

specification of the search strings (aforementioned

keywords) used in the SR protocol. According to the

recommendations of experts in the SR field, the

searches should be conducted by using the search

engines in the electronic databases of leading

publishers. We have also added several specialized

resources to the set of these databases: on the one

hand, the conference proceedings of the

“International Conference on Information Quality”

(ICIQ, http://mitiq.mit.edu/) since it is the most

important international event in the DQ area; and on

the other hand the contents of the only two journals

to deal with this area: International Journal of

Information Quality “IJIQ” and Journal of Data

A SYSTEMATIC LITERATURE REVIEW OF HOW TO INTRODUCE DATA QUALITY REQUIREMENTS INTO A

SOFTWARE PRODUCT DEVELOPMENT

and Information Quality “JDIQ” (note that the

search in both journals was carried out manually).

All of these resources contain works of great

importance and most of them offer search engines.

The complete list of resources is presented in Table 2.

Table 2: List of resources.

ACM Digital Library

IEEE Computer Society

Information Quality at MIT (ICIQ)

Science Direct

Wiley InterScience

IJIQ

JDIQ

Upon considering the list of keywords mentioned

above (see Table 1), and making a combination

through the logical connectors “AND” and “OR” we

decided to coin the following search string: (“Data

Quality”) AND (“requirements” OR “dimensions”

OR “Metamodel” OR “Modeling” OR “Model” OR

“metadata” OR “framework” OR “Methodology”

OR “Modelling” OR “MDA” OR “representation”

OR “accuracy” OR “completeness” OR

“consistency” OR “credibility” OR “currentness”

OR “accessibility” OR “compliance” OR

“confidentiality” OR “efficiency” OR “precision”

OR “traceability” OR “understandability” OR

“availability” OR “portability” OR

“recoverability”). It is worth noting that it was

decided incorporate in a particular way, each of the

different DQ dimensions defined in ISO/IEC 25012,

with the aim of broadening the range of search.

Please note that the syntax of the string may differ

according to the specific requirements of the

different search engines of the available resources.

2.3 Studies Selection

Once the resources in which we intended to carry

out the searches had been selected, we then defined

the procedure for selecting the studies, which also

included criteria for the inclusion and exclusion of

the studies (works) found during the SR.

The procedure used to select studies was

basically as follows: initially a researcher read only

the title and the abstract of the set of papers found in

each of the searches in order to select the most

relevant studies from each set. After an initial

coarse-grained filtering, the researcher analyzed the

complete article, deciding which works he judged to

be unsuitable since they did not make a particularly

great contribution to the DQ requirement field. A list

was then made of those studies that were considered

to be very important (typically named primary

studies). Once this list was considered to be

complete, other researchers with a higher expertise

in the field were encouraged to conduct an

investigation of this list in order to verify that the

studies really provided important knowledge with

regard to that area.

The procedure for the selection of primary

studies consists of an iterative and incremental

process. It is said to be iterative because some of the

main activities such as searching, reading and

information extraction are carried out for each of the

selected resources. In addition, if a search does not

produce a minimal set of results, it is possible that

the search string must be refined to obtain more

accurate results. It is also said to be incremental,

because when we perform the searches and extract

information a set of potential studies grows from

scratch, thus leading to the completion of the

systematic review by obtaining a complete

document. In our case, the first author of this paper

acted as a researcher, and the remaining authors

were those who had greater expertise in the area of

DQ. The inclusion and exclusion criteria defined for

this work are listed below.

Inclusion criteria:

 The articles should describe proposals or

strategies for the specification and/or modelling

of data quality requirements as a software

specification.

 The articles must be written in English.

 There will be an analysis of the title, keywords

and summary of each of the studies found.

 There is no restriction with regard to the date of

publication.

Exclusion criteria:

 Articles that do not propose any methodology,

strategy or model (or metamodel) with which to

specify data quality requirements.

As a result of the application of these criteria, we

were able to decide which studies found by the

searchers could be considered as primary studies.

3 EXECUTION

OF THE SELECTION

After executing the search procedure on the different

resources, a total of 820 studies were found (once

eliminated the duplicated studies). By applying the

inclusion and exclusion criteria, 42 were considered

to be important, while only 8 were eventually

considered as primary studies by the experimental

ENASE 2010 - International Conference on Evaluation of Novel Approaches to Software Engineering

researchers. Table 3 summarizes a report of our

findings.

Table 3: Distribution of studies by resource.

Studies

Resources

Date

Found

Relevant

Primary

ACM Digital

Library

Oct „09

164

IEEE Computer

Society

Oct „09

169

ICIQ

Nov„09

JDIQ

Nov„09

IJIQ

Nov„09

Science Direct

Dec „09

100

Wiley

InterScience

Dec „09

297

Total

820

Of all the studies reviewed, only the following

were considered as primary studies:

1. Toward quality data: An attribute-based

approach (Wang, Reddy et al. 1995).

2. Data Quality Requirements Analysis and

Modeling (Wang and Madnick 1993).

3. A flexible and generic data quality metamodel

(Becker, McMullen et al. 2007).

4. IP-UML: Towards a Methodology for Quality

Improvement Based on the IP-MAP Framework

(Scannapieco, Pernici et al. 2002).

5. A Product Perspective on Total Data Quality

Management (Wang 1998).

6. DQRDFS: Towards a Semantic Web Enhanced

with Data Quality (Caballero, Verbo et al.

2008).

7. Quality Views: Capturing and Exploiting the

User Perspective on Data Quality (Missier,

Embury et al. 2006).

8. A Data Quality Metamodel Extension to CWM

(Gomes, Farinha et al. 2007 ).

Once the primary studies had been identified,

the next step was to extract the relevant information

for the systematic review from each one of them. A

form was designed to better guide this process of

extracting relevant information. All the information

from the studies is displayed in Tables 5 to 12,

located in the Appendix.

4 ANALYSIS OF OBTAINED

RESULTS

Once the information had been extracted from all the

primary studies, the main aim was to address the

usability of the identified primary studies in

accordance with our interest in discovering

proposals dealing with methodological and

technological issues. In this section, we show the

results of the corresponding analysis. It is worth

highlighting that the number of proposals is

significantly poor in comparison to the degree of

interest that data quality and information quality

field has motivated in recent years. Our concern

about this led us to ask various DQ researchers and

practitioners from different countries and

organizations why we had not found more works.

Most of them agreed that since data quality is dealt

with as a specific issue rather than an organizational

issue, many organizations are not yet aware of the

possible benefits of our research topic; moreover,

most of them also agreed that the topic is quite

relevant because the results could help organizations

to improve their performance at a relative low cost.

Table 4 summarizes the information extracted

from each proposal: the technology or data model

used, the existence of a tool or prototype supporting

it, the inclusion of an example or study case, and

reports concerning whether the proposed results

have already been tested in a real environment.

Table 4: Technology or Model used by the selected

studies.

Studies

Model

Tool

Example

Test

(Wang and

Madnick 1993)

Relational

Yes

(Wang, Reddy

et al. 1995)

Relational

Yes

(Wang 1998)

Relational

Yes

(Scannapieco,

Pernici et al.

2002)

Object

Oriented

Yes

(Missier,

Embury et al.

2006)

XML

Yes

(Becker,

McMullen et al.

2007)

Relational

Yes

(Gomes,

Farinha et al.

2007 )

Object

Oriented

Yes

(Caballero,

Verbo et al.

2008)

XML

Yes

Upon studying the analysis in greater depth, we

noted that none of the existing works provide a

methodology for obtaining and managing DQ

requirements. We had hoped to find a methodology

that could, at some point, lead analysts and

developers to implement a correct management of

data quality requirements from the earliest stages,

and throughout the process of an information

system‟s development. This lack consequently

A SYSTEMATIC LITERATURE REVIEW OF HOW TO INTRODUCE DATA QUALITY REQUIREMENTS INTO A

SOFTWARE PRODUCT DEVELOPMENT

motivates the challenging research goal of depicting

a methodology for managing and combining data

quality software requirements together with those

that remain. On the other hand, and with regard to

the technology used, we concluded that since many

different kinds of applications could be developed

by using different kinds of technologies, some sort

of generalization should be used, in order to make

different kinds of developments possible. This

generalization can be achieved by working with

models and metamodels. Therefore, our most

important conclusion in relation to this issue is that

we should work upon the foundations of Model

Driven Engineering, MDE (Bézivin 2004) and

Model Driven Architecture, MDA (OMG 2003), in

order to make the development of different kinds of

development possible by using the same concepts

concerning data quality requirements.

5 CONCLUSIONS

Conducting an SR is a highly intensive task in

comparison to that of a conventional literature

search. However, if the complete protocol of an SR

is followed step by step, then a better validation of

the results is generated, and the efforts are

worthwhile. The main goal of this paper is to show

the results obtained and conclusions reached after

carrying out an SR to discover how well the

management of data quality requirements (at both

the methodological and technological levels) is dealt

with in specialized literature. After analyzing the

obtained results, it is evident that there is a need for

new proposals dealing with methodological issues,

owing to the scarcity of existing initiatives aimed at

this particular area. Furthermore, technological

issues must be also dealt with. To do this, we can

conclude that MDA foundations might be the best

environment in which to carry out research into this

area. Due to the benefits it provides, mainly in the

generation of diverse models and transformations

between different abstraction levels. We can

consider the incorporation of elements for

management of DQ requirements from the early

stage, and propagate them throughout all the

development cycle of any kind of software.

ACKNOWLEDGEMENTS

This research is part of the PEGASO-MAGO

(TIN2009-13718-C02-01), and DQNet (TIN2008-

04951-E/TIN) projects, both of which are supported

by the Spanish Ministerio de Educación y Ciencia,

ENGLOBAS (PII2I09-0147-8235), and TALES

(HITO-2009-14), both supported by the Consejería

de Educación y Ciencia of Junta de Comunidades de

Castilla-La Mancha.

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Ballou, D. P., R. Y. Wang, et al. (1998). "Modelling

Information Manufacturing Systems to Determine

Information Product Quality." Management Science

44(4): 462-484.

Becker, D., W. McMullen, et al. (2007). A Flexible and

Generic Data Quality Metamodel. International

Conference on Information Quality.

Bernes-Lee, T., J. Hendler, et al. (2001). "The Semantic

Web." Scientific American.

Bertino, E., C. Dai, et al. (2009). The Challenge of

Assuring Data Trustworthiness. Database Systems for

Advanced Applications. Springer-Verlag. Volume

5463/2009: 22-33.

Bézivin, J. (2004). "In Search of a Basic Principle for

Model Driven Engineering." UPGRADE, Novática.

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Biolchini, J. C. d. A., P. G. Mian, et al. (2007). "Scientific

research ontology to support systematic review in

software engineering." Adv. Eng. Inform. 21(2): 133-

151.

Caballero, I., A. Caro, et al. (2008). "IQM3: Information

Quality Maturity Model." Journal of Universal

Computer Science 14: 1-29.

Caballero, I., E. M. Verbo, et al. (2008).

DQRDFS:Towards a Semantic Web Enhanced with

Data Quality. Web Information Systems and

Technologies, Funchal, Madeira, Portugal.

Eppler, M. and M. Helfert (2004). A Classification and

Analysis of Data Quality Costs. International

Conference on Information Quality, MIT, Cambridge,

MA, USA.

Gomes, P., J. Farinha, et al. (2007 ). A data quality

metamodel extension to CWM Proceedings of the

fourth Asia-Pacific conference on Comceptual

modelling - Volume 67 Ballarat, Australia Australian

Computer Society, Inc.: 17-26

ISO-25012 (2008). "ISO/IEC 25012: Software

Engineering-Software product Quality Requirements

and Evaluation (SQuaRE)-Data Quality Model."

Karel, R., C. Moore, et al. (2009). "Forrester‟s report for

Business Process and Application Professionals on

Trends 2009: Master Data Management." Forrester.

Laudon, K. C. (1986). "Data Quality and Due Process in

Large Interorganizational Record System."

Communications of the ACM 29(1): 4-11.

Missier, P., S. Embury, et al. (2006). "Quality views:

capturing and exploiting the user perspective on data

quality." Proceedings of the 32nd international

conference on Very large data bases-Volume 32.

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OMG. (2003). "Common Warehouse Metamodel (CWM)

Specification v1.1." October, 2008, from

http://www.omg.org/docs/formal/03-03-02.pdf

[Consultado el: 29-09-2008].

OMG (2003). MDA Guide Version 1.0.1., Object

Management Group: 62.

Scannapieco, M., B. Pernici, et al. (2002). IP-UML:

Towards a Methodology for Quality Improvement

Based on the IP-MAP Framework. International

Conference on Information Quality, ICIQ-02.

Shankaranarayan, G., R. Y. Wang, et al. (2000). IP-MAP:

Representing the Manufacture of an Information

Product. Fifth International Conference on

Information Quality (ICIQ'2000), MIT, Cambridge,

MA, USA.

Strong, D. M., Y. W. Lee, et al. (1997). "Data Quality in

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Quality Management." Communications of the ACM

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APPENDIX

Table 5: Primary Study (Wang and Madnick 1993).

Data Extraction of the Study

Publication

Richard Wang, Henry Kon, and Stuart Madnick. April, 1993. Data Quality Requirements Analysis and Modeling. In:

Proceedings of the Ninth International Conference of Data Engineering. Austria.

Objective Results of the Study

Proposal

The article is focused on: (1) establishing a set of premises, terms and definitions for the management of DQ, and (2)

developing a step by step methodology for defining and documenting DQ parameters for users. The requirements

analysis methodology proposed by the authors is based on two main approaches:

- Specification of labels needed for users with the objective of assessing, determining or improving data quality.

- Obtaining, from the user viewpoint, the general aspects of DQ non-sensitive to labeling, for example, the features of

completeness and response time.

A series of views (view of application, view of parameters and quality view) is also proposed which should be included

as part of the documentation of quality requirements specification, the authors jointly refer to a list of possible data

quality candidates.

Results

Methodology for collecting and documenting data quality requirements.

Model

It uses a “Relational” type of model.

Mentioned

Difficulties

There is no definition and standardization of quality dimensions.

Table 6: Primary Study (Becker, McMullen et al. 2007).

Data Extraction of the Study

Publication

David Becker, William McMullen y Kevin Hetherington-Young. November, 2007. A flexible and generic data quality

metamodel. In: Proceedings of the 12

. International Conference on Information Quality, ICIQ 2007. U.S.A.

Objective Results of the Study

Proposal

Analyze and describe three generic metamodels mentioning some of their most important capabilities: Common

Warehouse Metamodel (CWM), Data Warehouse Quality (DWQ) and Universal Meta Data Model.

The authors propose an architecture and a basic metamodel for DQ, which meets the objectives of adequately providing

a flexibility, generality and ease of use of the requirements in situations of potential use. This metamodel adequately

represents the information products (IP), data objects, and metrics, measurements, requirements, evaluations and actions

of DQ.

Results

Proposal for architecture and a generic metamodel for DQ.

Model

It uses a “Relational” type of model.

Mentioned

Difficulties

No mention of any.

A SYSTEMATIC LITERATURE REVIEW OF HOW TO INTRODUCE DATA QUALITY REQUIREMENTS INTO A

SOFTWARE PRODUCT DEVELOPMENT

Table 7: Primary Study (Wang, Reddy et al. 1995).

Data Extraction of the Study

Publication

Wang, Richard Y., Reddy, M., Kon, H.. March, 1995. Toward quality data: An attribute-based approach. In: Journal of

Decision Support Systems. U.S.A.

Objective Results of the Study

Proposal

The authors propose a quality perspective using labeled data in a cell level with quality indicators, which are objective

characteristics of the data and its manufacturing process. Based on these indicators, the user can evaluate the quality of

data for a specific application. Additionally, the authors investigate how these quality indicators can be specified, stored,

retrieved and processed. They propose a data model based on attributes, query algebra and integrity rules that facilitate

cell-level tagging, along with data processing of the application that is augmented with quality indicators.

Results

A methodology for analyzing of data quality requirements based on an entity-relationship model, for the specification of

the types of quality indicators to be modelled.

Model

It uses a “Relational” type of model.

Mentioned

Difficulties

Study and research object-oriented approach, because the relational model that represents the schema of quality, may be

restrictive. An object-oriented approach seems simpler to model the data and its quality indicators, because many of the

quality control mechanisms are oriented towards procedures and this approach could manage them without any

problem.

Table 8: Primary Study (Scannapieco, Pernici et al. 2002)

Data Extraction of the Study

Publication

Monica Scannapieco, Barbara Pernici y Elizabeth Pierce. November, 2002. IP-UML: Towards a Methodology for

Quality Improvement Based on the IP-MAP Framework. In: Proceedings of the Seventh International Conference on

Information Quality, ICIQ´02. U.S.A.

Objective Results of the Study

Proposal

It proposes a UML profile for data quality in order to sustain the quality improvement within an organization. This

profile is based on the IP-MAP Framework (Shankaranarayan, Wang et al. 2000), but differs from it, mainly owing to:

(1) it specifies the artefacts for production during the improvement process in terms of diagrams drawn using UML

elements defined in the data quality profile, (2) it uses the IP-MAP not only to evaluate the quality and think about the

improving actions, but also as a schematic means to design and implement improving actions. The IP-MAP is an

extension of a Information Manufacturing System (IMS) proposed by (Ballou, Wang et al. 1998), this Framework has

the advantage of combining both data analysis and process analysis, with the aim of assessing the quality of the data.

The data quality profile consists of three different models: Data Analysis Model, Quality Analysis Model and Quality

Design Model. The data analysis model specifies which data are important to consumers because their quality is critical

to organizations‟ success. The quality analysis model consists of modelling the elements that permit the representation

of the data quality requirements, a quality requirement can be related to a dimension of quality or features that are

typically defined for data quality. The quality design model incorporates the perspective of IP-MAP, which helps in

understanding the details associated with the manufacturing process of the information products.

Results

Shows a profile and a methodology for producing UML artifacts designed by the data quality profile.

Model

It uses an “Object Oriented” type of model.

Mentioned

Difficulties

None.

Table 9: Primary Study (Wang 1998).

Data Extraction of the Study

Publication

Richard Wang. February, 1998. A Product Perspective on Total Data Quality Management. In: Communications of the

ACM. U.S.A.

Objective Results of the Study

Proposal

This article presents the Total Data Quality Management (TDQM) methodology, whose main purpose is to deliver High

quality information products (IP) to information consumers, along with introducing the concepts of TDQM cycle and

information products. It explains the stages of the TDQM related to the information products: Definition, Measurement,

Analysis and Improvement, with particular emphasis on defining the characteristics of information products and quality

requirements of the information. The author also shows a software tool with which to conduct surveys to assess the

quality of information, where it may be possible to evaluate a list of quality dimensions defined by the author.

Results

It shows the TDQM methodology and illustrates how it can be put into practice in a wide range of organizations.

Model

It uses a “Relational” type of model.

Mentioned

Difficulties

None.

ENASE 2010 - International Conference on Evaluation of Novel Approaches to Software Engineering

Table 10: Primary Study (Caballero, Verbo et al. 2008).

Data Extraction of the Study

Publication

Ismael Caballero, Eugenio Verbo, Coral Calero y Mario Piattini . May, 2008. DQRDFS: Towards a Semantic Web

Enhanced with Data Quality. In: 4th. International Conference on Web Information Systems and Technologies,

WEBIST ´08. Portugal.

Objective Results of the Study

Proposal

This article introduces a new view of the Semantic Web, based on the concept of quantity of data quality (QDQ), where

DQ aspects are used as a base to enable machines to process the documents of the Semantic Web for different activities

such as information retrieval or filtering of documents. The Semantic Web is an extension of the current Web in which

the information is provided with a well-defined meaning, enabling computers and people to cooperate (Bernes-Lee,

Hendler et al. 2001). This article has a twofold goal: (1) it shows the readers a brief introduction to DQ, and (2)

showing how the DQ fundamentals have been applied with the aim of highlighting the quality of Web documents for

the Semantic Web. The first step in order to enable the DQ in the semantic web is to identify the set of elements that

need to be studied from the User Requirements Specification for the DQ (DQ-URS). The second step is to identify the

DQ dimensions and their related metadata. The third step is to obtain and record the values for the metadata. This

information is represented by using XML-type documents.

Results

It shows a proposal of the concept of QDQ oriented towards the Semantic Web.

Model

It uses the XML language (Extensible Markup Language) for its representation.

Mentioned

Difficulties

None.

Table 11: Primary Study (Missier, Embury et al. 2006).

Data Extraction of the Study

Publication

Paolo Missier, Suzanne Embury, Mark Greenwood, Alun Preece y Binling Jin. September, 2006. Quality Views:

Capturing and Exploiting the User Perspective on Data Quality. In: International Conference on Very large databases,

VLDB ´06. Korea.

Objective Results of the Study

Proposal

This article presents a quality user-centred model and a software environment, which domain experts can use to easily

and rapidly code and test their own heuristics quality criteria. As core of the model, they propose the concept of

"quality view", similar to customized "lenses”, through which the data can be observed. The main contributions of this

work are: (1) An extensible semantic model to the user for concepts of quality information in e-science. (2) A process

model and a declarative language for specifying abstract views of quality in terms of a few logical operators. (3) An

architecture for implementing quality views within many data processing environments.

Results

It proposes a framework for specifying requirements for quality processing by the user, called "quality views".

Model

It uses the XML language (Extensible Markup Language) for its representation.

Mentioned

Difficulties

None.

Table 12: Primary Study (Gomes, Farinha et al. 2007 ).

Data Extraction of the Study

Publication

Pedro Gomes, José Farinha, Maria José Trigueiros. February, 2007. A Data Quality Metamodel Extension to CWM. In:

. Asia-Pacific Conference on Conceptual Modelling, APCCM 2007. Australia.

Objective Results of the Study

Proposal

This paper proposes a metamodel for data quality and data cleaning, both concepts being applicable to the context of

data warehouses. This metamodel is integrated with the “Common Warehouse Metamodel” (OMG 2003), providing an

extension of this standard towards data quality. It also provides a set of modelling guidelines for the storage of formal

specifications of DQ rules. The main purpose of the metamodel is to provide support to profiling and data cleaning

activities, with rules that can be established with the aim of detecting data quality problems. It also establishes data

cleaning solutions. In relation to data cleaning, a “metadata” holder is provided, with the objective of enabling the

ultimate goal of achieving the highest level of automation possible. However, a metadata support is also provided when

the user‟s participation is required in the cleaning process.

Results

It displays a metamodel for quality and data cleaning, both concepts being applied to the context of data warehouses.

Model

It uses an “Object Oriented” type of model.

Mentioned

Difficulties

None.

A SYSTEMATIC LITERATURE REVIEW OF HOW TO INTRODUCE DATA QUALITY REQUIREMENTS INTO A

SOFTWARE PRODUCT DEVELOPMENT