=Paper=
{{Paper
|id=Vol-1135/paper11
|storemode=property
|title=Detection and Resolution of Data Inconsistencies, and Data Integration using Data Quality Criteria
|pdfUrl=https://ceur-ws.org/Vol-1135/paper11.pdf
|volume=Vol-1135
|dblpUrl=https://dblp.org/rec/conf/quatic/AngelesM04
}}
==Detection and Resolution of Data Inconsistencies, and Data Integration using Data Quality Criteria==
https://ceur-ws.org/Vol-1135/paper11.pdf
1
Detection and Resolution of Data Inconsistencies,
and Data Integration using Data Quality Criteria
Pilar Angeles, Lachlan M. MacKinnon
Abstract — In the processes and optimization of information integration, such as query processing, query planning and hierarchical
structuring of results to the user, we argue that user quality priorities, data inconsistencies and data quality differences among the
participating sources have not been fully addressed. We propose the development of a Data Quality Manager (DQM) to establish
communication between the process of integration of information, the user and the application, to deal with semantic heterogeneity and data
quality. DQM will contain a Reference Model, a Measurement Model, and an Assessment Model to define the quality criteria, the metrics and
the assessment methods. DQM will also help in query planning by considering data quality estimations to find the best combination for the
execution plan. After query execution, and detection of inconsistent data, data quality might also be used to perform data inconsistency
resolution. Integration and ranking of query results using quality criteria defined by the user will be an outcome of this process.
Index Terms — Data Quality, Heterogeneous Databases, Information Integration, Information Quality, Semantic Integration.
—————————— ——————————
1 INTRODUCTION
T he problems of data inconsistency in data integration have
been widely discussed and researched for a number of
years, and a large number of these have been resolved, as
The aim of this paper is to establish the context and back-
ground on data quality for information retrieval and propose a
Data Quality Manager to deal with data integration and data
described in our own work [35], [36]. However, the combina- inconsistencies through the use of data quality properties.
tion of these solutions, and the resolution of the remaining is- This paper is organized as follows: in Section 2 the back-
sues, still remains an open issue. This has exacerbated as the ground on the establishment of data quality criteria, models
development of Information Systems, network communica- and assessment is discussed. In Section 3 some issues are pre-
tions and the World Wide Web, has permitted widespread ac- sented in order to help measuring data quality in Heterogene-
cess to autonomous, distributed and heterogeneous data ous Databases. In Section 4 the elements of the Data Quality
sources. An increasing number of databases, especially those Manager are presented, and how it interacts with data integra-
published on the Web, are becoming available to external us- tion and data fusion processes. Finally Section 5 concludes this
ers. User requests are converted to queries over several data paper identifying main points of this paper.
sources with different data quality, but the quality of the data
sources utilised is not a feature of the process.
2 BACKGROUND
Integration of schemas on existing databases into a global
unified schema is an approach developed over 20 years ago, 2.1 Data Integration in Heterogeneous Database
[4]. However information quality can not be guaranteed after Systems
integration, because data quality is dependent on the design of Data integration is the process of extracting and merging data
the data and its provenance [31], [5]. Even greater levels of in- from multiple heterogeneous sources to be loaded into an inte-
consistency exist when data is retrieved from different data grated information resource [4]. Solving structural, syntactical
sources. and semantic heterogeneities between source and target data
On the other hand, different expectations exist on the qual- has been a complex problem for data integration for a number
ity of the information, depending on the user. A casual user on of years [28], [4],[35], [36].
the Web does not expect complete and precise information One solution to this problem has been developed through
[21], but close to his selection condition. A professional user the use of a single global database schema that represents the
expects accuracy and completeness of the information re- integrated information with mappings from global schema to
trieved in order to make a decision irrespective of the time it local schemas, where each query to the global schema is trans-
could take to retrieve the data, although speed is still likely to lated to queries to the local databases using these mappings
be a lesser priority. [4]. The use of domain ontology, metadata, transformation
User priorities, data inconsistencies and data quality differ- rules, user, and system constraints have resolved the majority
ences among the participating sources have not been fully ad- of the problems of domain mismatch associated with schematic
dressed in the processes and optimizations of information in- integration and global schematic approaches. However, even
tegration, such as query processing, query planning and hier- when all the mappings, semantic and structure heterogeneity
archical structuring of results to the user. are solved in the global schema, consistency may not have
been achieved, because the data provided by the sources may
————————————————
be mutually inconsistent. This problem has remained because
• P. Angeles, School of Mathematical and Computer Sciences, Heriot Watt
University Edinburgh,U.K.EH14 4AS. E-mail: pilar@macs.hw.ac.uk.
it is impossible to capture all the information and avoid null
• L.M. MacKinnon, School of Mathematical and Computer Sciences, Heriot values. At the same time, each autonomous component data-
Watt University Edinburgh,U.K.EH14 4AS. E-mail: lach- base deals with its own properties or domain constraints on
lan@macs.hw.ac.uk. data, such as accuracy, reliability, availability, timeliness and
2 QUATIC’2004 PROCEEDINGS
cost of data access. on multi-database integration, or data inconsistency detection
Several approaches to solve inconsistency between data- TABLE 1
bases have been implemented: CLASSIFICATION BASED ON INTERNAL OR EXTERNAL VIEW [31]
1. By reconciliation of data, also known as data fusion:
different values become just one using a fusion func- Dimensions
tion (i.e. average, highest, and majority), depending on Internal view Data- related:
the data semantic [16]. (design operation) Accuracy, reliability, timeliness, com-
2. On the basis of individual data properties: associated pleteness, currency, consistency, pre-
with each data source (i.e. cost of retrieving data, how cision
recent is the data, level of authority associated with System-related:
this source, or accuracy and completeness of data). Reliability
These properties can be specified at different levels:
External view Data-related:
the global schema design level, the query itself or in
(use, value) Timeliness, relevance, content, impor-
the profile of the user [2].
tance, sufficiency, usableness, useful-
Some definitions of data quality criteria, metrics and meas-
ness, clarity, conciseness, freedom of
urement methods are presented in the following sections.
bias, informativeness, level of detail,
2.2 Data Quality (DQ) vs. Information Quality (IQ) quantitativeness, scope, interpretabil-
ity, understandability
“High data quality has been defined as data that is fit for use
System-related:
by data consumers and is treated independent of the context in
Timeliness, flexibility, format, efficiency
which data is produced and used” [29].
Data quality has been characterized by quality criteria or
dimensions such as accuracy, completeness, consistency and TABLE 2
timeliness [31], [16], [8], [22], [29], [25] and [20]. However there
is no general agreement on data quality dimensions [32], [14]. CLASSIFICATION BASED ON DATA-CONSUMER PERSPECTIVE [29]
There has not been a specific differentiation between IQ and
DQ, because the terms data and information are often used DQ DQ concerns Causes DQ Dimen-
synonymously. However, Data quality is related to accuracy Category sions
and integrity and on the other hand, Information Quality is Intrinsic Mismatches Multiple sources Accuracy
concern with data quality in context, and is related to how the among sources of of same data. Objectivity
information is produced and interpreted. the same data are Judgment in- Believability
common cause of volved in data Reputation
2.3 Data Quality Classifications
intrinsic DQ con- production.
A definition of quality dimensions and a framework for analy- cerns
sis of data quality as a research area was first proposed by
Accessibility Lack of computing Systems difficult Accessibility
Richard Wang et al. [32]. An ontologically based approach
resources. to access. Access Security
was developed by Yair Wand et al. [31], this model analyzed
Problems on pri- Must protect
data quality based on discrepancies between the representa-
vacy and confiden- confidentiality.
tion mapping from real world (RW) to information system
tiality: Representational
(IS) and vice versa, through design and operation activities
Interpretability. DQ dimensions
involved in the construction of an information system as an
Understandability. are causes of
internal view. A real world system is said to be properly rep-
Data representa- inaccessibility.
resented if there exists an exhaustive mapping, and no two
tion
states in RW are mapped into the same state in IS. Four in-
Contextual Operational Data Incomplete data. Relevancy
trinsic data quality dimensions were identified: complete,
production prob- Inconsistent Value Added
unambiguous, meaningful and correct. Additionally mapping
lems: representation. Timeliness
problems and data deficiency repairs were suggested. The
Changing data Inadequately Completeness
analysis produced a classification of data quality dimensions as
consumers needs. defined or Amount of
related to the internal or external views. Data Quality meas-
Distributed com- measured data. Data
urement method was not addressed (See table 1).
puting. Data results not
A different classification of data quality dimension was de-
properly aggre-
veloped by Diane Strong et al. in [29] is based on a data-
gated.
consumer perspective. Data quality categories were identified
as intrinsic, accessibility, contextual and representational. Data Represent- Computerizing and Data inaccessi- Interpretability
quality measurement method was not addressed. Each cate- ational data analyzing ble because: Ease of un-
gory was directly addressed to different data quality dimen- Multiple interpre- derstanding
sions (See table 2). tations across Concise and
In Total Data Quality Management (TDQM) [33] the con- multiple speciali- Consistent
ties and limited representation
cepts, principles and procedures are presented as a method-
capacities to Timeliness
ology which defines the following life cycle: define, measure,
summarize Amount of
analyze and improve data as essential activities to ensure
across image. data
high quality, managing data as a product. There is no focus
PILAR ANGELES ET AL.: DETECTION AND RESOLUTION OF DATA INCONSISTENCIES, AND DATA INTEGRATION USING INFORMATION QUALITY CRITERIA 3
TABLE 3
QUALITY DIMENSIONS DEFINITIONS, DETERMINANT FACTORS AND METRICS BY AUTHOR [9], [10], [16], [25], [31].
Dimension Concern Author Factors Metric
“Inaccuracy implies that Information System (IS) represents Wand /Wang RW/IS states
Accuracy a Real World (RW) state different from the one that should
have been represented”
“Whether the data available are the true values (correct- Motro/Rakov
ness, precision accuracy or validity)” Data values
“The degree of correctness and precision with which real Gertz
world data of interest to an application domain are repre-
sented in an information system.
Precision Ambiguity: Improper representation: multiple RW states Wand /Wang RW/IS states
mapped to the same IS state
“Ability of an IS to represent every meaningful state of the Wand/Wang RW/IS states
Completeness represented real world system. Thus is not tied to data-
related concepts such as attributes, variables, or values”
“The extent to which data is not missing and does not have Data model
sufficient breadth and depth for the task at hand” Pipino/Wang (table, row, 1 – ( #incomplete
“All values for a certain variable are recorded” attribute, classes) items / #total items)
“Whether all the data are available” Ballou schema
“The degree to which all data relevant to an application Motro column
domain have been recorded in an information system.” Gertz population
“The IS state may be mapped back into a meaningful state, Wand/Wang RW/IS states
the correct one”
Correctness “The extend to which data is correct and reliable” Pipino/Wang 1 - (# errors /
# total)
Timeliness “Whether the data is out of date, An availability of output on Wand/Wang Currency
time” Volatility
“The extent to which data is sufficiently up to date for the Pipino/Wang Max (0,
task at hand” 1 - (# currency /
The degree to which the recorded data are up-to-date” Gertz #volatility))
“How fast the IS state is updated after the real world sys- Wand/Wang
Currency tem changes.”
Age: of data, when first received by the system Pipino/Wang Age Age + delivery time –
Delivery time: when data is delivered by the user Delivery time input time
Input time: When data is received by the system. Input time
“Whether the data are up to date, reflecting the most recent Motro
values”
Volatility “The rate of change of the real world.” Wand/Wang
Time data invalid
“Refers to the length of time data remains valid.” Pipino/Wang Time - Time start valid
Consistency “Refers to several aspects of data. In particular, to values Wand/Wang RW/IS states
of data inconsistency would mean that the representation Values of data on
mapping is one to many. This is not considered a defi- Integrity constraints
ciency.” Data representation. 1–
“The extent to which data is presented in the same format” Pipino/Wang Physical rep. data ( #inconsistent /
as consistent representation Values of data on #total consistency
“Often referred as integrity constraints state the proper Motro Integrity constraints checks)
relationships among different data elements”
“The degree to which the data managed in an information Gertz
system satisfy specified constraints and business rules.”
Believability “The extent to which data is regarded as true and credible” Pipino/Wang Source of data S
Accepted stand. A
Previ. experience P Min(A,S,P)
Accessibility “The extent to which data is available, or easily and quickly Pipino/Wang Time request TR Max (0,
retrievable” Time delivery TD 1 – (TR – TD /
Time no longer useful TR – TN))
TN.
Data path A. Min (A,B,C)
Structure B
Path lengths C
or database retrieval solutions.
There are just definitions, and in the best cases, measure- 2.4 The assessment methods for information quality
criteria
ment of data quality aspects.
Information Quality (IQ) criteria have been classified in an as-
In table 3, the different quality dimension definitions are
sessment-oriented model by F. Naumann in [20], where for
presented with the relevant factors on each dimension and
each criterion an assessment method is identified.
the proposed metric by author.
4 QUATIC’2004 PROCEEDINGS
In this classification the user, the data and the query process two main problems, intensional and extensional inconsisten-
are considered as sources of information quality by themselves, cies. Intensional are related to resolving the schematic differ-
(see Table 4.) ences between the component databases, this issue is also
TABLE 4 known as semantic heterogeneity. Extensional inconsistencies
are related to reconciling the data differences among the par-
AN ASSESSMENT-ORIENTED CLASSIFICATION [20] ticipating databases [16]. Information integration is the process
of merging multiple query results into a single response to the
Assessment IQ Criterion Assessment Method
user. There are several important areas of related work to con-
Class
sider in the following approaches.
Source IQ of
metadata 3.1 Data Integration Techniques based on Data Quality
Believability User experience Aspects
Concise represent. User Sampling Data integration techniques have been developed by Gertz [8],
Subject Criteria Interpretability User sampling [9] based on data quality aspects within an object oriented data
Relevancy Continuous assessment model, and data quality information stored in metadata. Qual-
Reputation User experience ity aspects such as timeliness, accuracy and completeness were
User Understandability User sampling considered in the process of database integration. The main
Value-added Continuous assessment aspect was the assumption that quality of the data stored at
Completeness Continuous assessment different sites can be different and the quality varies over time.
Customer Support Parsing, sampling Query language extensions were necessary to support the
Object Criteria Documentation Parsing specification of data quality goals for global queries and thus
Objectivity Expert input data integration. In the case of data conflicts between semanti-
Price Contract cally equivalent objects, the object with best data quality must
Information/ Reliability Continuous assessment be chosen. If no conflicts exist between objects but their quality
Data Security Parsing level is different, the integrated objects need to be grouped to
Timeliness Parsing allow the ranking of the results.
Verifiability Expert input
3.2 Multiplex
Accuracy Sampling, cleansing
The project MULTIPLEX directed by Motro and Rakov [16],
Process Criteria Amount of Data Continuous assessment
addressed the problem of extensional inconsistencies and a
Availability Continuous assessment
Data Quality Model for Relational Databases. MULTIPLEX
Consistent repress. Parsing
was based on accuracy and completeness as quality criteria,
Query Process Latency Continuous assessment
this model assigned a quality specification for each instance of
Response time Continuous assessment
a relation, and these quality specifications were calculated by
extending the relational algebra. The quality of answers was
The AIM Quality Methodology (AIMQ) [34] is a practical
calculated by the measure of arbitrary queries from the overall
tool for assessing and benchmarking IQ organizations, with
quality specification of the database [16]. In the case of multi-
three components: PSP/IQ Model which presents a quality
ple sets of records as possible answers to one query, each set of
dimension classification by product quality and service quality
records has an individual quality specification. A voting
using information consumer perspective, and consolidates the
scheme, using probabilistic arguments, identifies the best set of
dimensions into four quadrants: sound, dependable, useful,
records to provide a complete and sound answer and ranking
and usable information, these quadrants are relevant to IQ im-
of tuples in the answer space. The conflict resolution strategy,
provement decisions. IQA instrument measures IQ for each IQ
and the quality estimates are addressed by the multidatabase
dimension. In a pilot study, using questionnaires answered by
designer.
information collectors, information consumers, and IS profes-
sionals in six companies, these measures are average for the 3.3 Fusionplex
four quadrants and the scale used in assessing each item An enhancement of the Multiplex system FUSIONPLEX [2], [3]
ranged from 0 “not at all” to 10 “completely” and the IQ Gap stores information features or quality criteria scores in meta-
Analysis Techniques assess the information quality for each of data, the considered quality dimensions are timestamp, accu-
the four quadrants. These gap assessments are the basis for racy, availability, clearance and cost of retrieval. Inconsisten-
focusing IQ improvement efforts. This methodology uses ques- cies are resolved by data fusion, allowing the user to define
tionnaires as main measurement method, taking a very prag- data quality estimation on a vector of features weights, per-
matic approach regarding IQ. formance thresholds and a fusion function at attribute level, as
In the following section we will present some approaches required. This approach reconciles the conflicting values at
demonstrating how a data quality model, assessment methods attribute level using an intermediate result named polyin-
and user priorities, based on the work discussed above, can stance, which contains the inconsistencies. First the polyin-
help in the process of data integration. stance is divided in polytuples, and using the feature weights
and the threshold, members of each polytuple are discarded.
3 MEASURING DATA QUALITY IN HETEROGENEOUS Second each polytuple is separated into mono-attribute poly-
DATABASES tuples using the primary key, assuming that the same value of
the primary key between databases refers to the same object
Database integration is divided by Motro and Rakov [16] in but with different data values, and attribute values are dis-
PILAR ANGELES ET AL.: DETECTION AND RESOLUTION OF DATA INCONSISTENCIES, AND DATA INTEGRATION USING INFORMATION QUALITY CRITERIA 5
carded based on corresponding feature values. Finally the frameworks and models of reference have been developed as
mono-attribute tuples are joined back together resulting in sin- standards, such as ISO 15504 [12] and CMMI [1], [7].
gle tuples. Here, the general objective is to establish good practices for
software engineering and to be able to talk the same language
3.4 Information Quality Reasoning during software processes, no matter the architecture or im-
Information Quality reasoning is defined as the integration of plementation methodology. The same challenge need to be
information quality aspects, to the process of planning and taken up in the Data Quality area, based on the following:
optimizing queries against databases and information systems 1. It is essential to identify a framework that establishes the
by F. Naumann in [21]. Such aspects are related through the models corresponding to the criteria of quality, methods of
establishment of information quality criteria, assessment measurement, assessment and improvement, and considers the
methods and measure. data quality life cycle.
Selection of data sources, and optimization of query plan- This framework can be used as good practice during infor-
ning by considering user priorities has been also addressed in mation system development, integration, capture and tracking
[21] by the definition of a quality model and a quality assess- of changes in data. Tracking changes should offer quality im-
ment method under the following assumptions: provement and data cleaning based on a feedback provided by
1. Query processing: Concerned with efficiently answer- the same information system or a set of recommendations to
ing a user query to a single or multi database. In this the information manager, and will help to achieve self regulat-
context efficiency means speed. ing systems.
2. Query planning: Is concerned with finding the best 2. This framework might be considered in heterogeneous
possible answer given some cost or time constraint. systems, before, during and after the integration of informa-
Query planning involves regarding many query exe- tion.
cution plans across different, autonomous sources that 3. We propose a Data Quality Manager as the mechanism to
together form the complete result. establish communication between the user, the application and
In this approach information sources were selected by using the process of integration of information, to deal with semantic
Data Envelopment Analysis method (DEA) [6], and the follow- heterogeneity problems, as part of the framework mentioned
ing quality dimensions: understandability, extent, availability, above (see Figure 1.)
time and price, discarding sources with poor quality before
executing the query.
However different sources have different quality scores and Selection of data sources
they must be fused to determine the best quality result, the
quality fusion can be done in two ways 1) applying a fusion
function per each quality criteria and find the best combination
to query [17] or 2) computing the information quality score Reference Query Planning
using different quality criteria such as availability, price, accu- Model
racy, completeness, amount response time for each plan and
thus a ranking of the plans using Simple Additive Weighting Measurement Detection and Fusion of
method (SAW) explained in [11]. Model Data inconsistencies
The completeness of the query result derived from different
sources is approached in [24] considering the number of results Quality
(coverage) and the number of attribute values in the result Metadata
Query Integration
(density). Completeness is calculated as the product between
the density and the coverage of the corresponding set of in- Assessment
formation sources. Model
Ranking query results
3.5 Data Quality on the Web
In this seminar, it was established that it is essential to first Data Quality Manager
concentrate on developing expressive data quality models, and Information Integration Process
once such models are in place, develop tools that help users
Fig. 1. Data Quality Manager in the process of information integration.
and IT managers to capture and analyze the state of data qual-
ity in an information system. [10].
4. The Data Quality Manager will contain the following
elements:
4 DATA QUALITY MANAGER • Reference Model: In this model the data quality cri-
Databases have traditionally been considered to be sources of teria will be defined depending on data sources,
information that are precise and complete. However the design users and application domain.
and implementation of such systems is carried out by human • Measurement Model: This will contain the defini-
beings, whose are imperfect, so during the whole software life tion of the metrics to be used to measure data qual-
cycle errors occur that are reflected in the quality of both soft- ity, also the definition of a quality metadata (QMD)
ware and information. Furthermore, when these sources of and the specification of data quality requirements
data come from different applications, distributed both physi- such as user profiles, query language.
cally and logically, these errors multiply. In the field of Infor- • Assessment Model: The quality scores definition is
mation Systems, this shortcoming has been realized and essential to establish how the quality indicators are
6 QUATIC’2004 PROCEEDINGS
going to be represented and interpreted. Fig. 4. Query Planning
5. The Data Quality Manager will establish the basis for tak-
ing decisions during the identification of data sources in het- • After query execution, and detection of inconsistent
erogeneous systems, such that: data, data quality might be used to perform data
• To classify the sources of data based on certain cri- fusion (see Figure 5).
teria of quality, depending on the application do-
main. The scores must be stored in a metadata for
every source of data (see Figure 2.) ResultX
Execute Data
Query ResultY Inconsistencies
Definition of Definition of Quality Meta-
Quality Metrics and data Definition Plan Detection
Criteria Indicators ResultZ
Quality user Inconsistent Consistent
QMD priorities Query Query
QMD Quality Assessment Result Result
Metadata of Data
Population Sources
Fig. 2. Data Quality Manager Components Definition. Data
Fusion
• The use of quality aspects previously stored in the
Fig. 5. Detection and Resolution of Data Inconsistencies.
metadata as a whole with the user priorities for the
selection of the best sources of information before
the execution of the queries, for example if the user • Integration of the information sources ranking with
prefers those sources of information that are more the quality criteria estimated by the user (see Figure
current with regard to those of major credibility 6)
(see Figure 3.)
Quality user
Mapping Data Sources ResultJ priorities
User Global/Local Involved in the
Query Schemas Query
Data ResultK
Fusion Query
QMD Query Result
Ranking of
ResultL Integration Ranking
best data
sources
Quality
User Pri- Consistent
orities Query QMD
Fig. 3. Selection of best data sources. Result
Fig. 6. Ranking of Query Result
• Help the query planning process by considering
data quality estimations to find the best combina-
tion for the execution plan (see Figure 4.) CONCLUSION
We have shown that, although there has been considerable
Quality past work in the resolution of semantic heterogeneity in multi
User Pri- data source systems over a number of years, expressive data
orities quality models and tools to utilise them remain to be devel-
oped [10]. The approach developed for Information Quality
reasoning [21] provides some mechanisms for data source se-
QueryA
lection, but does not address many of the data quality factors
User Top identified in Table 3. Accordingly, we propose a Data Quality
Global Query QueryB ranking Manager as a framework to deal with data inconsistencies and
Query Partition Query lack of quality due to different sources; presenting a continu-
QueryC
Plan ous process of data validation, such as definition of quality
criteria, selection of best data sources, ranking of query plan,
detection and fusion of data inconsistencies and ranking of
QMD query result considering quality of data sources and user ex-
pectations. This work is already under way and performance
reporting of the tools developed will appear in the next twelve
PILAR ANGELES ET AL.: DETECTION AND RESOLUTION OF DATA INCONSISTENCIES, AND DATA INTEGRATION USING INFORMATION QUALITY CRITERIA 7
months. [19] F. Naumann and C. Roker, ʺDo Metadata Models meet IQ Requirementsʺ,
Proceedings of the International Conference on Information Quality, MIT Cam‐
ACKNOWLEDGEMENT bridge, 1999.
[20] F. Naumann and C. Roker C., ʺAssessment Methods for Information
This work was supported by financial funding from Consejo Quality Criteriaʺ, Proceedings of the International Conference on Informa‐
Nacional de Ciencia y Tecnologia CONACYT, Mexico. tion Quality (IQ2000), Cambridge, Mass., 2000.
[21] F. Naumann, ʺFrom Databases to Information Systems‐Information
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[14] G. Tayi, D. Ballou and Guest Editors, ʺExamining Data Qualityʺ, Communica‐ Pilar Angeles obtained her first degree in computer engineering from the
tions of the ACM, vol. 41,no.2, pp.54‐57, 1998. Universidad Nacional Autonoma de Mexico (UNAM), in 1993,
Diploma in Expert Systems from The Instituto Tecnologico Autonomo de
[15] U. Leser and F. Naumann, ʺQuery Planning with Information Quality Mexico (ITAM) in 1994, Diploma in Telematic Systems from ITAM in 1995,
Boundsʺ, Proceedings of the 4th International Conference on Flexible Query An‐ and M.Sc. in Computer Science, regarding Quality in Software Enginnering
swering, (FQAS00), Warsaw Poland, 2000. from the UNAM in 2000. Since 1989 she has been working on Technical
[16] A. Motro and I. Rakov I, ʺEstimating the Quality of Databasesʺ, Proceedings Support for Databases in Casa de Bolsa Probursa, Nissan Mexicana,
of FQAS 98: Third International Conference on Flexible Query Answering Systems, Software AG, Sybase de Mexico and e-Strategy Mexico. Recent research
interests have included Data Quality and Heterogeneous Databases. She
T. Andreasen, H. Christiansen, and H.L. Larsen, ed., pp. 298‐307. Roskilde,
is a Funder Member of the “Quality in Software Engineering Mexican As-
Den.mark, Springer‐Verlag, Berlin, Germany, 1998. sociation” (AMCIS).
[17] F. Naumann, ʺData Fusion and Data Qualityʺ, Proceedings of the New Tech‐
niques & Technologies for Statistics Seminar. Surrent, Italy 1998. Lachlan M. MacKinnon is Reader in Computer Science, and Director of
[18] F. Naumann, ʺQuality‐driven Integration of Heterogeneous Informa‐ Postgraduate Study in Computer Science, at Heriot-Watt University. He has
tion Systemsʺ, Proceedings of the 25th Very Large Data Bases Conference a first degree in Computer Science, and a PhD in Intelligent Querying for
Heterogeneous Databases. He researches and consults widely in Data,
(VLDB99), Edinburgh, Scotland, 1999. Information and Knowledge Technologies. He is a member of the IEEE,
British Computer Society, ACM, AACE, immediate past Chair of the British
8 QUATIC’2004 PROCEEDINGS
National Conference on Databases, and upcoming Chair of the British HCI
Conference. He has over 50 conference and journal publications in this
area.