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Improving Data Quality
Methodology and Techniques
Telcordia Contact:
Paolo Missier
[email protected]
February, 2001
An SAIC Company
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within Telcordia Technologies, except with written permission of Telcordia Technologies.
Presentation outline
 Rationale for Data Quality
– Canonical horror stories plus some good numbers
 Process and Root Causes Analysis for quality improvement
 Data error types
 Data Cleaning
– The record matching problem. Searching and matching
– Distance functions
– Approaches to record matching
 Example: The Italian Ministry of Finances case study
 Analysis toolkits
– The INRIA Extensible framework
– The Telcordia Extensible Data Analysis tool
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Data Quality –AIPA Feb. 01– 2
Data Quality in the telecomm
 Large US phone companies suffer from various DQ-related problems:
– Dispatching errors cost money: e.g. sending the wrong technicians to fix a
problem, or the right technician to the wrong place.
 It is estimated that a 1% reduction in dispatching errors would result in $3M savings
/year
– For at least one application, one telco company threw away 40% of the data
in the DB because it was too expensive to use or attempt to clean it
– Customers may receive service before the they are entered in the billing
system  they get months worth of service for free
– Bad provisioning leads to wasted resources (lines allocated to no one) and to
allocation of the same lines to multiple customers
– The same company cannot tell which wireline customers also subscribe to
their own wireless service (the two customer DBs are not integrated)
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Data Quality –AIPA Feb. 01– 3
Data Quality in the Italian Public Administration
– Taxpayers’ residence data can be unreliable (out of date or
incorrect): a sizable fraction of taxpayers cannot be reached by
regular mail
– Administrative offices may force input values that violate data
constraints
– Some taxpayers are assigned more than one SSN, resulting in
multiple personae for tax purposes
– Taxpayers can be misidentified in the DB because their personal
data is incorrect
– Part of the information provided by taxpayers in their tax returns is
lost and unused once it flows into the system
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Data Quality –AIPA Feb. 01– 4
Data Contains Errors
Expected error rates
by industry segment
Source: Innovative Systems, 1999
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Data Quality –AIPA Feb. 01– 5
The main DQ agenda
 Top-level agenda:
– Correct current known errors in the data: Data Cleaning
 Techniques are borrowed from Statistics, Machine Learning, Text Retrieval, large
Databases
– Determine the cause for errors and prevent their occurrence: root cause
analysis and process analysis
 Specifically:
–
–
–
–
Assess the current state of the data, for various quality dimensions
Predict the future state of the data (what gets dirty, and how fast)
Define improvement goals (estimate ROI(*) figures)
Identify priorities (which data gives me the highest ROI after cleaning)?
 E.g. “wireline DB is dirty but its data are less important that wireless…”
 “Given a limited budget, cleaning wireless is simpler and more beneficial”
– Define data cleaning plan (methods, tools, extent)
Ref.: P.Missier, G.Lalk, V.Verykios , F.Grillo, T.Lorusso, P.Angeletti,
Improving Data Quality in Practice: A Case Study in the Italian Public Administration,
Distributed and Parallel Databases Journal, Kluwer Academic Publishers, 2001, to appear
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Data Quality –AIPA Feb. 01– 6
Data Quality starts from process analysis
 “What processes manipulate which data?”
 Without data flow analysis, data cleaning is only a temporary solution:
– New data will still be dirty and will contaminate the system
 Enforcing Data Quality requires enforcing control over the Data Flows
 Suppose data flows F1 and F2 carry logically related data:
– When do the flows meet? Where in the system do they meet?
Which procedures are involved?
Is it too late to cross-reference the related data?
Can they be matched earlier? How much would it cost?
–
–
–
–
Which entity is responsible for which data?
How is data supplied?
How do duplicates propagate?
Are related entries cross-validated?
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Data Quality –AIPA Feb. 01– 7
Data Flow and Process Analysis
 Rationale:
– Unpredictability in Data Quality is a consequence of poor control and
documentation over the processes that manipulate the data
 Questions:
– Who can update which data value? On which version? When do
synchronization and validation occur?
 Goals:
– Identify the processes and systems that manipulate the data that is
subject to quality control
– Determine how that data flows through those processes and
systems
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Data Quality –AIPA Feb. 01– 8
Root Cause Analysis: Identify causes of poor data
quality
 Data-oriented process analysis. Ex.:
 When data flows F1 and F2 meet at time t, mismatches about related
data records are found. Can data be matched earlier? How much would it
cost?
 How many times is this address entered, and into how many systems?
Are related entries cross-checked?
 Mine data on current state of quality. Infer conclusions such as:
 “most errors are due to manual data entry, and mostly come from
processing centers with specific characteristics”
 “systematic data currency errors in system B occur for data that is derived
from system A”
 “There is a strong correlation between this data being entered late, and
the number of spelling errors it contains”
 “Quality levels for subscribers’ address data have improved by x% on
average after processes A,B,C were re-engineered”
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Data Quality –AIPA Feb. 01– 9
Data flow: Trigger Events, State changes
Pub. Admin.
System
Workflow /
System Transactions
Pub. Admin.
Information System
Real World
Official
paperwork
’
A  A’
’
Administrative
Procedures
Real world
State change Event
Direct
Triggers
t1
System State change
(multiple DBs)
Indirect
Triggers
t2
Real world events propagate to Information System
through various processes
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Data Quality –AIPA Feb. 01– 10
Process Analysis - Example (Italian P.A.)
Trigger
Input
System
Procedure
Birth.
Notification to
local town
Newborn’s
vital data
Integrated or
manual
procedure for
generation of
new SSN
Finances’ office
online (TP)
Birth.
Notification to
finances office
Parents’
request(selfcertified)
Taxpay er’s
direct request
Taxpay er’s
request
Finances’ office
online (TP)
Occasional
realignment
with Data
Steward
Data Batch
from Data
Steward
Ad hoc
validation proc.
for the specific
batch
Expected Data Quality
Correctness
Currency
High is procedure is integrated.
Medium is manual data entry required
Max three months lag (by
law)
Low is cross-reference with DS not
available.
One dat entry step alway s required.
High if DS immediately certifies data
presented by parents
Determined by ID used.
One dat entry step alway s required.
Determined by parents’
timeliness. Negligible
sy stem processing time
High for validated data.
No data entry required.
Coerenza
formato
Determined by taxpay er.
Negligible sy stem
processing time
Arbitrarily low.
Subsequent further
misalignments are
inevitable..
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High for
validated
data.
Data Quality –AIPA Feb. 01– 11
Indicators
 Quantities that describe the degree of correctness and currency
of a population of data values, e.g.
– % of values that are incorrect
– % of values that are obsolete
 When Computed:
– at fixed intervals (scheduled monitoring)
– on demand
– when updates are available
 Computed based on: sample or entire population
 How:
– aggregate over the validation state associated with the data
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Data Quality –AIPA Feb. 01– 12
Indicators example
 I1: % duplicate SSN
 I2: % obsolete residence data
 I3: % truncated values in field xyz
 I4: % null values in field xyz
 I5: % gender values inconsistent with SSN
 Given attribute A with values in {a1,…,an}, indicators can also be
aggregated on A (e.g. A = set of administrative regions), e.g.
– I1i: % duplicate SSN for group ai,, 1  i  n
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Data Quality –AIPA Feb. 01– 13
Quality Constraints
 Constraints are predicates over the indicators, e.g.
– I2 < Y at the end of each month
– I5 < Z for values generated before Dec. 31, 1997
–…
 Aggregated on A = {a1,…,an}:
– I1i < Xi with 1  i  n, Xi possibly different for each ai
 Parameters in the constraints are usually subject to negotiation
between Data Provider and Data Customer;
 Trade -off: cost of constraints satisfaction, resulting data quality
– more stringent constraints => more expensive, better quality
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Data Quality –AIPA Feb. 01– 14
Classification of data error types
 Syntactic correctness: the value is legal and it conforms to format
constraints. Ex.:
– a multi-part address (town, street, zip code, etc.)
– Internal consistency of a credit card number
– Misspelling errors
 Semantic correctness: “how well does the data represent the true
state of the world?”
– Ex.: the residence data for a citizen is semantically correct if the address in
the record corresponds, to some degree, to the address where the citizen
currently resides.
– Format and Value Consistency: How many acceptable formats and
spelling exist for the same data item (think of all the known abbreviations in
street addresses)
– Accuracy: we can measure by how much a format and spelling for a street
deviates from a defined standard
– Value Completeness and Precision: ex.: how many optional elements are
present for an address?
– Currency:
 Is data obsolete? Does it represent the current state of the world?
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Data Quality –AIPA Feb. 01– 15
Correcting data errors: Data Cleaning
 Data Cleaning is the process of correcting data errors.
 Different techniques are used to deal with each error type
 Research work on data quality has traditionally focused on the
record matching problem
 The goal in the record matching problem is to identify records
in the same or different data sources/databases that refer to
the same real world entity, even if the records do not match
completely.
 Record matching techniques only address the non-temporal
error types
 Challenges:
 Accuracy: need to quantify the errors incurred by a cleaning technique
 Efficiency: algorithms must be suitable to handle large databases (millions
of records)
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Data Quality –AIPA Feb. 01– 16
Record Matching Across Data Sources
DB B
DB A
Name Lastname DOB POB Address SSN
Non-key, approximate match
Key match
Name Lastname Phone Address SSN
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Data Quality –AIPA Feb. 01– 17
The Record Matching Problem
 Given a pair of records A and B in two data sources (could be
the same source!), the matching algorithms attempt to
determine if:
– A and B represent the same entity: Match
– A and B represent two different entities: Non-match
 Within a single database, a duplicate is a record that
represents the same entity as another record in the same
database.
 Common record identifiers such as names, addresses, and
code numbers (SSN, object identifier), are the matching
variables that are used to identify matches.
Ref.: Verykios, V.S., Elmagarmid, A.K., Elfeky, M., Cochinwala, M., and Dalal, S.,
A Survey on Record Linkage Techniques, unpublished.
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Data Quality –AIPA Feb. 01– 18
Steps in record matching
 Data preparation
– Normalize values
 Bring field values to a standard format, based on the consistency rules
 Searching
– Build pairs of records that are potential matches within the
databases
– Challenges: How to avoid building all n2 potential pairs
 Matching
– Determine whether or not a pair of records is actually a match
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Data Quality –AIPA Feb. 01– 19
Values normalization - example
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Data Quality –AIPA Feb. 01– 20
Searching
How many record pairs do I need to consider?
S OSVALDO 16
FONDO D ORTO 7
FONDO D ORTO 9
NAPOLI 281
COGNULO 20
BRACCO 7
A.DE GASPERI 108
LO MARGI MONTE
SANTO 5
DRUSO 1
FONDO DORTO 9
FONDO DORTO 7
TRAV. LATTARO 2
TRAV. LATTARO 2
BENEDETTO BRIN 79
CSA ALCIDE DE GASPERI 105B
CDA MARGI-MONTE
DON BOSCO 5A
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Data Quality –AIPA Feb. 01– 21
Complexity reduction in Searching
 Brute-force approach is quadratic in the number of records
 Cluster based on the values of some grouping variables or some apriori knowledge. Ex.:
– If I know the “place of birth” is clean, then I can build as many clusters as
there are cities.
 Sorting according to a comparison key will bring “similar” records close
to each other.
– Blocking: only compare records that agree on the comparison key
– Windowing: only compare records that lie within a window of given size after
sorting
 Complexity reduction incurs the risk of missing valid matches.
 Metrics have been developed to evaluate the accuracy of a clustering
and sorting technique
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Data Quality –AIPA Feb. 01– 22
Matching: field values distance functions
 First, we define functions that measure the distance between
two values. Different functions are used for different value types.
 The distance function definition depends on the specific data
domain:
– Standard Strings Comparison functions: There are many ways to
define the distance between two strings
 Which one is more appropriate?
– Other data types require ad hoc comparison functions:
 Ex.: “Codice Fiscale”: what is the distance between two CFs?
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Data Quality –AIPA Feb. 01– 23
Simple Examples of String Distance
 Simple edit distance:
– d(A,B) = number of single-char insert/delete required to map string A
to string B
– d(“smith”, “mitch”) = 2
– d(“mitch’, “mitcheli”) = 3
(“mitch” +’s’ -‘c’ = “smith”)
 Weighted edit distances: different chars have different weights:
– if blanks only count .5, then
d(“lungotevere Massimo”, “lungo tevere Masimo”) = 1.5
 Different fields contribute different weights:
d([piazza, Toti], [via, Toti]) < 6
because d(“piazza”, “via”) < 6 in this context.
 Distance can also be a discrete function, e.g.:
d(piazza, via) = X, d(via, strada) = Y, ...
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Data Quality –AIPA Feb. 01– 24
Basic Field Matching Algorithm
 Degree of match in two composite fields:
– “number of matching atomic strings” / “avg. number of atomic
strings”
– atomic strings S1, S2 match if:
 S1 = S2, or S1 = S2 + 
 Ex.:
A = “Comput Sci Eng Dept University of California San Diego”
B= “Department of Computer Science Univ Calif San Diego"
– k = 6 strings in A match some string in B:
Comput, Sci, San, Diego, Univ, and Calif.
– The overall matching score is k / (|A| + |B|)/2) = 0.8.
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Data Quality –AIPA Feb. 01– 25
Other string comparison functions
 The type of string comparison used depends on the specific usage
context. Here are four examples:
Source: W. E. Winkler, Advanced Methods for record linkage,
US Census Bureau TechReport, RR94/05, 1994.
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Data Quality –AIPA Feb. 01– 26
“Group distance” – example of correct matches
Anagrafe Tributaria: Comparing Archivio Anagrafico with data from Comuni
BBNLRA45C58I787D
BBTDNC39M45C129M
BBRMHL71R04C129F
BBNMSM67E14E730T
U.DELLA RAGGIOLA 3
R VINI 33
TRAVERSA IOVINE 18
CARRARA PASETTI I 1 - BELRICET
UGUCCIONE DELLA FAGGIUOLA 3
RAFFAELE VIVIANI 33
TRAV. IOVINO 18
CARR. PASETTI I 1
Anagrafe Tributaria: Comparing Archivio Anagrafico with Dichiarazioni
CCCGPP55B65F258D
CCHRSR45E30H163N
CLDDRC24P57E730R
CRRMRA61H64L219G
CSCGNN22A22H163Y
DLLRSN26R58E730C
CSSGNZ60A31H163V
S.SCRIBANO 1
G NNA SORTINO SCRIBANO 1
C SA VITTORIO VENETO 629
VITT VENETO 629
FIUME VECCHIO2
S LORENZO FIUME VECCHIO
F ROSSITTO 6
FELICIANO ROSSITTO 6 SCALA D
P.MONTECHIARO 52
PALMA DI MONTECHIARO N.52
VILLA S.MARTINO PROV.BAGNARA 113 PROV BAGNARA 79 VILLA S MARTIN
G.CADORNA 4
GEN CADORNA LOTTO 95 4
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Data Quality –AIPA Feb. 01– 27
“Group distance” – correctly identified mismatches
Anagrafe Tributaria: Archivio Anagrafico  Comuni: group distance = 7
BBDSDA57A41Z330Q
BBGNNT63T54L245R
BBGRFL67L49C129D
BBLGRL56B27C129P
BBLLCU95S09C129V
BBLNTN73S07C129P
BBLVCN58P19C129L
BBTGRG46R01F258O
BBTVCN25B18C129F
S OSVALDO 16
FONDO D ORTO 7
FONDO D ORTO 9
NAPOLI 281
COGNULO 20
BRACCO 7
A.DE GASPERI 108
LO MARGI MONTE
SANTO 5
DRUSO 1
FONDO DORTO 9
FONDO DORTO 7
TRAV. LATTARO 2
TRAV. LATTARO 2
BENEDETTO BRIN 79
CSA ALCIDE DE GASPERI 105B
CDA MARGI-MONTE
DON BOSCO 5A
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Data Quality –AIPA Feb. 01– 28
Other String comparison functions
 Many other functions can be used to define the distance between two
strings, i.e.:
 Smith-Waterman: Given two strings, the Smith-Waterman algorithm
uses dynamic programming to find the lowest cost series of changes
that converts one string to another.
 N-grams: Letter n-grams, including trigrams, bigrams, and/or
unigrams, have been used in a variety of ways in text recognition and
spelling correction techniques
 Jaro algorithm: a string comparison algorithm that accounts for
insertions, deletions, and transpositions.
 Russell Soundex code is a phonetic coding, based on the assignment
of code digits which are the same for any of a phonetically similar
group of consonants. It is used most frequently to bring together variant
spellings of what are essentially the same names.
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Data Quality –AIPA Feb. 01– 29
Comparison Vectors
 For each pair of records identified in the search phase, we compare
their field values using the distance functions.
 Example:
– A= [CF1, Nome1, Cognome1, DataNascita1, LuogoNascita1, Sesso1],
– B= [CF2, Nome2, Cognome2, DataNascita2, LuogoNascita2, Sesso2],
– Compare:





CF with CF  d = 3.2
Nome1 + Cognome1 with Nome2 + Cognome2  d = 2.4
DataNascita1 with DataNascita2  d = 10
LuogoNascita1 with LuogoNascita2  d = 3.5
Sesso1 with Sesso2  d = 0
 The resulting vector of comparison values is called the comparison
vector:
– Final vector = [ 3.2, 2.4, 10, 3.5, 0]
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Data Quality –AIPA Feb. 01– 30
Using Comparison Vectors: Approaches to record
matching
 We now have a potentially very large collection of comparison
vectors.
 For each vector, we need to determine if it represents: a match,
a non-match, or an undecided pair
– This is a classification problem
 Probabilistic approach:
– Hypothesis testing: “pick the match status that minimizes cost or
error”
 Equational theory model / the merge-purge algorithm (S. Stolfo)
– Introduce knowledge that can be used to make inferences about the
match status of two records
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Data Quality –AIPA Feb. 01– 31
Examples of equational rules
/* RULE: compare-addresses */
similar_addrs = compare_addresses(person1stname,person2stname);
/* RULEs: closer-addresses-use-zips and closer-address-use-states*/
very_similar_addrs =
(similar_addrs && similar_city &&(similar_state || similar_zip));
/* RULEs: same-ssn-and-address and same-name-and-address */
if ((similar_ssns || similar_names) && very_similar_addrs) {
merge_tuples(person1, person2);
continue;
}
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Data Quality –AIPA Feb. 01– 32
Examples of correct matches in merge/purge
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Data Quality –AIPA Feb. 01– 33
Accuracy of merge/purge
 Accuracy measured in % duplicates detected on synthetic data
and % false positives
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Data Quality –AIPA Feb. 01– 34
The 1999 Ministry of Finance study on the Quality
of its data
Specific Goals:
 To assess the quality of addresses in DB Archivio Anagrafico
with respect to:
– currency (how many records do not reflect the real taxpayer’s
address)
– correctness (how many addresses are spelled out correctly)
 To determine the better reference source for cleaning the
Archivio Anagrafico
 To detect unknown duplicate Codici Fiscali (CFs) in DB Archivio
Anagrafico
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Data Quality –AIPA Feb. 01– 35
Approach
 Addresses:
– sample AA (about 275,000 records) according to various criteria (mainly,
availability of corresponding Comuni data)
– match addresses against recent, official residence data (Comuni) for the
same taxpayers (same CF)
– match addresses against recent data from Dichiarazioni for the same
taxpayers (same CF)
 Duplicate CFs:
– sample about 600,000 records from three Comuni (Bari, Carbonara di Bari,
Chioggia)
– from the population, a set of 34,244 records (5.71%) are known duplicates
(“connessi”)
– Detect patterns of anomalies that lead to known duplicate CFs, using
“connessi” as the training set. Induce heuristics.
– Process entire population automatically using the induced heuristics
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Data Quality –AIPA Feb. 01– 36
Analysis of known duplicates (“connessi”) - TS
mismatching field count
frequency
cognome
4104
11.98%
nome
6116
17.86%
anno nascita
3403
9.94%
mese nascita
2435
7.11%
giorno nascita
8754
25.56%
sesso
2152
6.28%
comune nascita
4419
12.90%
piu di un campo
2861
8.35%
tot connessi
34244 100.00%
cognome
nome
anno nascita
mese nascita
giorno nascita
sesso
comune nascita
piu di un campo
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Data Quality –AIPA Feb. 01– 37
General Classification approach: Supervised
Learning
 Described in M. Cochinwala, V. Kurien, G. Lalk, and D. Shasha.
Efficient Data Reconciliation, Bellcore Research, February 1998.
 Use of machine learning and statistical techniques for reducing the
matching complexity for large datasets.
 Use a machine learning algorithm (CART, a well-known decision tree
inducer) to generate matching rules.
 The system generates rules with very high accuracy, and low
complexity. The automatically reported rules increased the accuracy of
the matching process from 63% to 90%.
 Follow-up Research work still in progress
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Data Quality –AIPA Feb. 01– 38
Prototype Data Analysis and Cleaning Toolkits
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Data Quality –AIPA Feb. 01– 39
An Extensible Framework for Data Cleaning
-- INRIA, France
 Apply a set of transformations to the source data
 Framework: a platform offering data manipulation services:
 Data Transformation for standardization of attribute values
 Multi-table Matching, Approximate Join, similarity functions, clustering
 Duplicate Elimination
– Implementation:
 SQL-like Macro-operators for:




Data Mapping (CREATE MAPPING)
Data Matching (CREATE MATCH)
Data Clustering (CREATE CLUSTER)
Data Decision (CREATE DECISION)
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Data Quality –AIPA Feb. 01– 40
Key contributions
 Open framework with extensible library of user-defined functions
for
– attribute value similarity matching
– clustering strategy
 Expressive SQL-like macro commands.
– Cartesian product semantics for tuple matching
 Optimizations:
– mixed execution supported by regular SQL engine + homegrown
extensions
– improve upon the baseline Cartesian-product complexity of the
matching for large datasets (>100,000 tuples)
Ref.: H.Galhardas, D. Florescu, D. Shasha, E.Simon,
An Extensible Framework for Data Cleaning, Procs. EDBT, 1999
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Data Quality –AIPA Feb. 01– 41
Telcordia’s Data Analysis Toolkit
Toolkit Demo
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Data Quality –AIPA Feb. 01– 42
Framework
 Rules and solutions differ from domain to domain
 Flexibility in resolution rules is essential
 Preprocessing and cleaning varies with the nature of data
 However, in most cases a common pattern emerges:
– Data is handled in stages,
– Each stage is responsible for one step of transformation (matching, merging,
cleaning, etc.) and
– Stages can be chained together to produce an overall data flow.
 We define a framework that allows
– Any number of custom-made processing blocks
– Combination of those blocks in various ways
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Data Quality –AIPA Feb. 01– 43
Approach
 Data-flow architecture
– The framework enforces a simple producer-consumer model
between pairs of functional blocks
– Workflow-style, graphical design of ad hoc analysis processes
– Flexibility and extensibility:
 Functional Blocks are independent components that interoperate through
a data-flow infrastructure
 Specific user-defined matching, clustering, and other analysis algorithms
are incapsulated and can be plugged in
Toolkit Demo
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Data Quality –AIPA Feb. 01– 44
Summary and conclusions
 Data Quality improvement calls for a comprehensive methodology that
includes
– Process analysis and re-engineering
– Data analysis and cleaning
 Data cleaning and data analysis automation are still more an art than a
craft:
– A variety of sophisticated techniques has been used to tackle the cleaning
problem (record matching), borrowing from Statistics, Machine Learning, Text
Retrieval
– Current commercial tools are expensive and only solve part of the problem
– Developing cleaning solutions that include re-engineering requires
experience and domain espertise
 The “open toolkit” approach begins to enter the DQ tools space
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Data Quality –AIPA Feb. 01– 45
References
 Verykios, V.S., Elmagarmid, A.K., Elfeky, M., Cochinwala, M., and Dalal, S., On the
Completeness and Accuracy of the Record Matching Process. In Proceedings of the 2000
Conference on Information Quality, pp. 54-69, October 2000, Boston, MA.
 M. A. Hernadez and S. J. Stolfo. The merge-purge problem for large databases, In Proc. of
the 1995 ACM SIGMOD Conference, pages 127--138, 1995.
 H.Galhardas, D. Florescu, D. Shasha, E.Simon, An Extensible Framework for Data
Cleaning, Procs. EDBT, 1999.
 M. Cochinwala, V. Kurien, G. Lalk, and D. Shasha. Efficient Data Reconciliation, Bellcore
Research, February 1998.
 Jeremy A. Hylton, Identifying and Merging Related Bibliographic Records, Master's thesis,
Department of Electrical Engineering and Computer Science, Massachusetts Institute of
Technology, 1996.
 Matthew A. Jaro, Advances in Record-Linkage Methodology as Applied to Matching the
1985 Census of Tampa, Florida, Journal of the American Statistical Association 84 (1989),
no. 406, 414-420.
 Alvaro E. Monge and Charles P. Elkan, The Field Matching Problem: Algorithms and
Applications, Second International Conference on Knowledge Discovery and Data Mining
(1996), 267-270, AAAI Press.
 W.E. Winkler, Advanced Methods for Record Linking, Proceedings of the Section on Survey
Research Methods (American Statistical Association), 1994, pp. 467-472.
 W. E. Winkler, The State of Record Linkage and Current Research Problems, Proceedings
of the Survey Methods Section (1999), 73-79.
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Data Quality –AIPA Feb. 01– 46