MDQM: Mediation Data Quality Model Aligned Data Quality Model

for Mediation Systems

Loubna Mimouni, Ahmed Zellou and Ali Idri

Mohammed V-Rabat University in Rabat-ENSIAS, Rabat, Morocco

Keywords: Data Quality, Mediation System, Data Quality Model.

Abstract: We are in age where data is provided from multiple heterogeneous and distributed sources hence the

usefulness of data integration systems (DIS). But given that the user is obliged to filter a large volume of

data to achieve the most satisfactory to his request and his information need, we can say that the approach

became more qualitative than quantitative. For this purpose, the main goal of this paper is to introduce the

data quality aspect concerning data retrieved from Mediation systems which is the virtual approach of data

integration. The paper will be finalized by establishing an attempt of a model of data quality criterions

classification in relation to Mediation system (MDQM).

1 INTRODUCTION

Progressively, systems need to integrate data

coming from multiple and heterogeneous data

sources, while allowing users a uniform access to

data without worrying about their types and their

formats. These systems are called ‘data integration

systems’ (DIS).

In our researches, we focus more on the

Mediation systems (Wiederhold, 1992) which

represents a virtual approach of DIS that offer a

uniform access to multiple data sources; the queries

are split and directed towards various data sources

through wrappers, and the results returned by the

sources are combined by a mediator and finally

sent to the users. Consumers who use the data

retrieved from this system need to be sure that the

data are sufficiently high quality.

This Quality of data (DoQ) is often defined as

the ability of a collection of data to meet user

requirement (Naumann and Rolker, 2002). It is

therefore important to provide high data quality in

mediation systems in such manner that these

information systems can be effective, useful and

helpful for data consumers. A model which

evaluate the data quality in the environment of

Mediation system is therefore necessary. For such

needs, various studies, (Naumann and Rolker,

2002), (Pipino et al., 2002) and (Wang, 1998)

shows that ensuring a good data quality for users is

an important challenge which is related to

information system success. In the case of virtual

Data integration (Mediation), the problem is

particularly complex since data is provided by

different sources, at different levels of quality.

In this work, we present a new contribution as a

classification of Data Quality Model specially

designed for mediation system called MDQM. This

attempt will be established through a reflection and

a discussion around a state of art in the domain.

In order to prove, this paper is organized as

follows. In section two we present the concept of

data quality. In section three we enumerate and

discuss the various approaches to modeling

classification of data quality criterions already

existing in literature. Section four presents an

overview of mediation systems architecture which

is the basic context of our researches to assess the

data quality. In section five we discuss also our

world view of modeling several data quality

criterions that impact mainly the data retrieved by

the mediation system. Finally, Section six

concludes the paper by mentioning some open

problems and our future visions to approach them

in the coming up works.

Mimouni, L., Zellou, A. and Idri, A.

MDQM: Mediation Data Quality Model Aligned Data Quality Model for Mediation Systems.

DOI: 10.5220/0006958403190326

In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 2: KEOD, pages 319-326

ISBN: 978-989-758-330-8

319

2 DATA QUALITY CONCEPT

2.1 Data Quality

Data quality is a perception or an assessment

of fitness to serve its purpose in a given context, i.e.,

the ability of a data collection to meet users’

requirements (Wand and Wang, 1996). DoQ is a

multidimensional and subjective concept since it is

usually evaluated by means of different criteria or

data quality dimensions and the selection and

assessment of the DoQ dimensions that better

describe users’ data quality requirements mainly

depend on the context of use (Pipino et al., 2002)

and (Strong, D. M et al., 1997).

For this reason, in the literature, there is no

general agreement on the identification of the most

important data quality dimensions. Anyway, it is

possible to distinguish a small set of DoQ

dimensions that are considered relevant in most of

the studies.

2.2 Data Quality (DoQ) vs Information

Quality (InQ)

To approach the concept of DoQ and in defining the

term, most of the literature uses the term Information

Quality and Data Quality interchangeably, however,

there is difference in meaning between data and

information. According to (ISO/IEC., 2008) data is

defined in the standard as “a reinterpretable

representation of information in a formalized

manner suitable for communication, interpretation,

or processing”.

Data can be considered as the base of

information and digital knowledge and takes into

account all data types, such as texts, numbers,

images and sounds, whereas information is

knowledge concerning objects, such as facts, events,

things, process, or ideas, including concepts, that

within a certain context have a particular meaning.

2.3 Data Quality in Data Integration

Systems

Research on data quality started abroad in the 1990s,

and many studies proposed different definitions of

data quality and division methods of quality

dimensions. Particularly, there has been quite a lot

of work on DoQ issues in the context of data

integration scenarios, especially the use of DoQ in

query formulation, processing (mediation) and

optimization e.g. (Wang, 1998), (Wang and Strong,

1996), (Weikum, 1996) (Redman, 1996). (Redman,

1998), (Redman,2001). Interesting aspects in general

data integration scenarios are how the quality of data

from different, heterogeneous and dynamic sources,

is assessed and measured and how DoQ dimensions

are represented to users and applications.

Several surveys have showed the importance of

data quality for end users, in particular, when

dealing with heterogeneous data coming from

distributed autonomous sources as mentioned

before. In what follows, we propose to give

an overview in chronological order of this researches

which provides from different view of data quality

criterions classification models used for specifying

requirements and evaluating of Data quality.

3 RELATED WORKS

3.1 Previous Works in DoQ

Classifications

3.1.1 Ballou and Pazer (1985)

The final sentence of a caption must end with a

period. Ballou and Pazer divided data quality into

four dimensions (Ballou and Pazer, 1985), as shown

in Table 1. They note that the accuracy dimension is

the easiest to evaluate, since this is merely the

difference between the correct value and what was

actually used.

They argue that the evaluation of timeliness can

be carried out in a similar manner. The evaluation of

the completeness dimension is also relatively

straight forward, as long as the focus is on whether

data are complete or not, rather than how many per

cent complete some data are. On the other hand, an

evaluation of consistency is a bit more complex,

since this requires two or more representation

schemes in order to be able to compare.

Table 1: DoQ classification by Ballou and Pazer.

DoQ Dimensions Meaning

Accuracy The recorded value is in

conformity with the actual value

Timeliness The recorded value is not out of

date

Completeness All values for a certain variable

are recorded

Consistency The representation of the data

value is the same in all cases

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3.1.2 R.Y. Wang & D.M. Strong TDQM

(1996)

Richard Y. Wang & Diane M. Strong propose a

consolidation of dimensions under TDQM «Total

Data Quality Management» (Wang, 1998) and

(Wang and Strong, 1996). This method allowed an

identifica-tion of 179 initial criteria and pass from

this large number to the classification of fifteen

quality criteria, considered as the most important in

this study.

Other aspect of this method is that it emanates

from a questionnaire handed to users, what seems

to be a good start to represent adequately

consumers’ view of point. Nevertheless, if users

have a little or no knowledge in Data Quality, this

could lead to a very important number of criteria,

which will require a lot of efforts to synthesize the

list.

In Figure 1, the authors classified data quality

criterions according to four dimensions «intrinsic

quality», «accessibility quality», «contextual

quality» and «representation quality».

Figure 1: Total Data Quality Model classification.

3.1.3 Redman T.C (1998)

Another perspective on data quality dimensions is

provided by Redman in (Redman, 1998) which

categorizes data quality issues in terms of: Data

view issues, Data value issues, Data presentation

issues, and other issues as illustrated below in

Table 2.

3.1.4 Levitin and Redman (1998)

Yet another perspective on data quality is data

properties, which is provided by Levitin and

Redman (Levitin, A. V., & Redman, 1998) who

argue that, since data production has many

similarities to processes that produce physical

products, data production could be viewed as

producing data products for data consumers. Thus,

Table 2: Redman.T.C Classification.

Dimensions Criterions

Data view

Relevancy

Granularity

Level of detail

Data value

Accuracy

Consistency

Currency

Completeness

Data presentation

The appropriateness of the format

ease of interpretation

Others

Privacy

Security

Ownership

Levitin and Redman discuss how thirteen basic

properties of organizational resources translate into

properties for data.

3.1.5 Wekium Model (1999)

Weikum (Weikum, 1999) developed a data quality

criteria classification: he distinguishes oriented

quality criteria process, system, and data. These

three categories may be directly matched with

queries processing steps for MBIS (information

system based on mediation).

Table 3: QOD criteria classification MBIS.

Category Criteria

Oriented System Reliability

Disponibility

Integrity

Security

Performance

Auditability

Oriented Process Security properties

Liveness properties

Oriented Data Exactitude

Exhaustiveness

Opportunity

Credibility

Effectiveness Cost

Latency

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3.1.6 Shanks and Corbitt (1999)

Building on existing literature, the author in (Shanks

and Corbitt, 1999) presents a data quality

classification, which is based in semiotic theory (the

use of symbols for conveying information) and

includes both product- and service-oriented aspects

of data quality. This is shown in the table below.

Table 4: Data Quality classification by Shanks and

Corbitt.

Semiotic

level

Meaning

Goal of the

representation

Syntactic

The form of

symbols rather

than their

meaning

Correct and

consistent

Semantic

The meaning

of symbols

Complete and

accurate at

particular

points in time

Pragmatic

The usage of

symbols

Useful and

usable

Social

The

understanding

of the meaning

of symbols

(different

stakeholders)

A shared

understanding

of the

representation

is achieved

3.1.7 Hogan and Wagner (2002)

The authors in (Hogan, W. R., 1997) provide a

model for examining data accuracy by assessing

correctness and completeness. The approach they

use enhances the concordance studies not only by

examining data in the clinical record but by

prospectively constructing a gold standard so that

the patient and care provider can be used as

information sources. In essence, this approach goes

further than concordance to ensure that the record is

a correct representation of the state of the patient.

3.1.8 ISO/IEC 25012 (2008)

ISO standard for data quality, namely ISO/IEC

25012 (Table 1), which defines a general data

quality model for data retained in a structured format

within a computer system and aims to support the

implementation of system's life cycle processes,

such as those defined in ISO/IEC 15288 (ISO/IEC.,

2008).

This data quality model categorizes quality

attributes into fifteen characteristics considering two

points of view: Inherent and system dependent. Each

characteristic can be considered in a specific context

of use. Each characteristic is of equal importance.

Table 5: ISO/IED 25012 classification Model.

Characteristics

Data Quality

Inherent

System

dependent

Accuracy x

Completeness x

Credibility x

Currentness x

Accessibility x x

Compliance x x

Confidentiality x x

Efficiency x x

Precision x x

Traceability x

Understandability x

Availability x

Portability x

As shown in table above, 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.

System dependent data quality refers to the

degree to which data quality is reached and

preserved within a computer system when data is

used under specified conditions; this lead us to say

that the domain in which data are used and exploited

impact strongly the manner of assessing its quality.

3.1.9 Haug et al. (2009)

The authors define three data quality categories:

intrinsic, accessibility and usefulness. The authors

argue that “representational data quality” as

mentioned above in the TDQM (Wang, 1998) can be

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perceived as a form of “accessibility data quality”

instead of a category of its own. Otherwise, the

authors also argue for the intrinsic dimensions

defined by (Haug and Arlbjørn, 2011).

- Discussion

The mentioned classifications above, were

undertaken with different goals in mind and

according to different context. Most projects have

avoided the difficult issue of quality assessment or

have only touched it briefly. This leads us to clearly

deduce that the area of data quality is widespread

debate and may differ from an application domain to

another, hence the interest to enroll in a specific

context.

- Our Motivation

The quantity of data handled by information systems

(IS) is increasing worldwide. Particularly in the case

of Mediation systems, where each data source is

independent and change frequently which may affect

the quality of data and consequently to the

satisfaction of the end user.

We choose to study and assess the quality of data

retrieved from the virtual data integration or so-

called 'the mediation’ also because we noticed that

despite the importance of this domain and the

importance of the end user satisfaction. Until now,

no study has conducted a quality assessment of data

retrieved by this type of system. This motivation will

make a good starting point as a problematic for our

work around.

In the next section, we take first the concept of

mediation up in order to have an idea about our

context of our research.

4 MEDIATION SYSTEM

In what follow, we present in the first Sub Section

the concept of mediation in general and in the

second sub section, we illustrates the process of

mediation.

4.1 The Concept of Mediation

The mediation concept dating back to 70’s, Levy

defines mediation as a transparent access service to a

huge number of autonomous, heterogeneous,

dynamics and distributed information sources (Levy

A. Y et al , 1996). Also, (A.Zellou, 2008) defines

mediation systems in as an intermediate tool

between a user or application, and a set of

information sources; this tool provides a transparent

access to sources by a unique interface and query

language.

We define the mediator more precisely as a

system that offers a common query interface to a set

of heterogeneous data sources which can (1) Accepts

the participation of different data sources (2)

Contains information about the contents of the data

sources; (3) Integrates the different data sources by

means of a unifying, global or mediated schema; (4)

Receives queries from users that are expressed in the

language of the global schema; (5) Collects data

from sources upon request; at query time;(6) In

order to answer global queries, it sends appropriate

queries to the sources; (7) Combines the answers

received from the sources to build up the final

answer to the user.

Mediation information has several advantages. It

provides a uniform, unique and multi-source access

through a unique interface. It produces an integrated

answer by exploiting relations between sources. It

offers an independence between applications users

and information sources in order to permit evolution

of applications and take into account sources

autonomy (INRIA, 2001). For us, an ideal mediation

system would sbe useful anytime and anywhere.

Intuitively, a Mediator cannot directly assess

queries which is directed to it, because it doesn’t

contain any form of data (Hadi, Zellou and

Bounabat, 2013). These data are stored in a

distributed way and in independent sources.

Mediator has only an abstract view of data stored in

these sources (Zellou, 2008).

Different integration systems based on mediators

have been proposed in literature. We find mainly:

MOMIS (Beneventano and Bergamaschi, 2004),

TSIMMIS (Garcia-Molina et al.1994 ), HERMES

(S. & Brink A. & Emery R. & Lu J. J. & Rajput A.

& Rogers T. J. & Ross R. & Ward C. .,1995),

Information Manifold (Weld, 1997), Internet Softbot

(Etzioni and Weld, 1994), Infomaster (Genesereth

M. R. et al. , 1997), OBSERVER (Mena et al. ,

1996) , PICSEL (Rousset et al.,2002) and WASSIT

(Zellou, 2008).

These systems above mentioned, were

distinguishable by the manner of mapping between

the global schema and the schema of data sources.

Abstraction of all this variety, we choose to present

hereunder, the processing of mediation as a

conventional global architecture of mediation with

three layers.

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5 OUR CONTRIBUTION: THE

CLASSIFICATION MODEL

FOR THE MEDIATION

SYSTEM-MDQM

In previous work (Mimouni et al., 2015) we have

initially listed exhaustively all of criteria that assess

the quality of data. Also, we have proposed a clear

and simple definition for each criterion as a recap

chart. This attempt leads us to classify, discuss it and

thereafter to deduce the most impacting quality

criteria concerning the data retrieved from

Mediation systems in this paper as a new

contribution, seeing that it is

Mediation systems in this paper as a new

contribution, seeing that it is our field of research.

Consequently, these criterions were sort by some

dimensions as a classification according to this

domain.

The principle quality key is that end users define

quality. End-users typically provide subjective

opinions such as 'I need the data to be right, and I

need to be able extract it ‘etc., Those Subjective

opinions must be translated into objective criteria on

the data.

5.1 Mediation Data Quality Model

MDQM

In the similar vein, the purpose of this sub section is

to create a classification model for the mediation

system by classifying data quality criteria which

impacts principally mediation system according to

six dimensions (Accessibility to data; Manipulation

of data, Representation of data, Value-added of data,

Usability of data, data sources) as shown below

(Table 6).

5.2 Semantic Categorization of MDQM

Dimensions

In this sub section, we attempt to classify a previous

dimension that we have suggest previously (in sub

section A) grouped by their semantic meaning and

we propose our own Model of Data quality

classification model called “WWHL”, according to

the four categorization which are;

- What: The content of this data and its

usefulness to the users.

- HOW: The manner and the profile allow to

access this data.

Table 6: MDQM Classification.

Criteria

Dimensions

Accessibility to DATA

Manipulation of DATA

Representation of DATA

Value-added of DATA

Usability of DATA

DATA SOURCES

Confidentiality x

Security x

Portability x

Relevancy x

Flexibility x

Traceability x

Availability x

Completeness x

Recoverability x

Consistency x

Concise

Representation

Consistent

Representation

Exactitude x

Precision x

Accuracy x

Currency x

Validity x

Verifiability x

Credibility x

Reputation x

Objectivity x

Attractiveness x

Readability x

Efficiency/

Effectiveness

Interpretability x

Amount of

DATA

Documentatio

Organization x

Specialization x

Novelty x

Reliability x

Interactive x

Freshness x

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- WHERE: Refers to data sources

- LIKE: In what form and what could we do

with this data.

This proposed model is illustrated as shown

below;

Table 7: The semantic categorization of dimensions.

Categorization Dimension Criteria

WHAT

Usability of

DATA

Validity

Understanbility

Interpretability

Readability

Reliability

Applicability

Value-added of

DATA

Completeness

Currency

Exactitude

Freshness

Specialization

Precision

Correctness

Accuracy

Recoverability

Efficiency/

Effectiveness

HOW

Accessibility to

DATA

Confidentiality

Security

Traceability

Relevancy

Novelty

WHERE

DATA SOURCES Availability

Credibility

Attractiveness

Objectivity

Reputation

Documentation

Representation of

DATA

Consistency

Consistent

Representation

Concise

Representation

Amount of

DATA

Organization

Manipulation of

DATA

Portability

Flexibility

Verifiability

Interactive

6 CONCLUSIONS

This paper has discussed the data quality in the

context of virtual data integration which is mediation

systems and offered an essay of modeling the

classification data quality criterions in the same

context. Given the importance attributed to quality

factors in our research, it is obvious that proceeding

to a selection of those most relevant is extremely

important. Especially, as the literature contains a

very large number of criteria of which the majority

one is described and quoted in the previous section

of this present paper.

These factors remain an essential asset to

proceed to a qualitative data analysis. Thus, the

selection would be a start vision of any process

associated to the selection of Data Quality in an

information mediation system. As prospects of this

work, we propose a study which is going to allow

measuring the impact and the importance of every

criterion of the data quality with regard to the

mediation.

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