Data Duplication Elimination and BSN Method: Record linkage, Merge, purge, Entity reconciliation, List washing and data cleansing

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Introduction to Data Quality Management: Intrinsic, Realistic, Orrâ€™s Laws of Data Quality, TQM >>

Lecture-20

Data Duplication Elimination & BSN Method

The duplicate elimination task is typically performed after most other transformation and

cleaning steps have taken place, especially after having cleaned single -source errors and

conflicting representations. It is performed either on two cleaned sources at a time or on a single

already integrated data set. Duplicate elimination requires to first identify (i.e. match) simi lar

records concerning the same real world entity. In the second step, similar records are merged into

one record containing all relevant attributes without redundancy. Furthermore, redundant records

are removed.

Why data duplicated?

A data warehouse is created from heterogeneous sources, with heterogeneous databases (different

schema/representation) of the same entity.

The data coming from outside the organization owning the DWH can have even lower quality

data i.e. different representation for same entity, transcription or typographical errors.

A data warehouse is created by merging large databases acquired from different sources with

heterogeneous representations of information. This raises the issue of data quality, the foremost

being identification and elimination of duplicates, crucial for accurate statistical analyses. Other

than using own historical/transactional data, it is not uncommon for large businesses to acquire

scores of databases each month, with a total size of hundreds of millions t o over a billion records

that need to be added to the warehouse. The fundamental problem is that the data supplied by

various sources typically include identifiers or string data that are either different among different

datasets or simply erroneous due to a variety of reasons including typographical or transcription

errors or purposeful fraudulent activity, such as aliases in the case of names.

Problems due to data duplication

Data duplication, can result in costly errors, such as:

§ False frequency distributions.

Incorrect aggregates due to double counting.

Difficulty with catching fabricated identities by credit card companies.

Without accurate identification of duplicated information frequency distributions and various

other aggregates will produce false or misleading statistics leading to perhaps untrustworthy new

knowledge and bad decisions. Thus this has become an increasingly important and complex

problem for many organizations that are in the process of establishing a data warehouse or

updating the one already in existence.

Credit card companies routinely assess the financial risk of potential new customers who may

purposely hide their true identities and thus their history or manufacture new ones.

The sources of corruption of data are many. To name a few, errors due to data entry mistakes,

faulty sensor readings or more malicious activities provide scores of erroneous datasets that

propagate errors in each successive generation of data.

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Data Duplication: Non-Unique PK

Duplicate Identification Numbers

Multiple Customer Numbers

Name

Phone Number

Cust. No.

M. Ismail Siddiqi

021.666.1244

780701

M. Ismail Siddiqi

021.666.1244

780203

M. Ismail Siddiqi

021.666.1244

780009

Multiple Employee Numbers

Bonus Date

Name

Department

Emp. No.

Jan. 2000

Khan Muhammad

213 (MKT)

5353536

Dec. 2001

Khan Muhammad

567 (SLS)

4577833

Mar. 2002

Khan Muhammad

349 (HR)

3457642

Unable to determine customer relationships (CRM)

Unable to analyze employee benefits trends

What if the customers are divided amon g sales people to make telephone calls? Maybe the three

records of the same person go to three telesales people or maybe to the same one, but arranged as

per Cust.No. The point is that no telesales person would know that a single customer is going to

get multiple calls from the company, resulting in wastage of time and resources of the company,

and at the same time annoying the customer.

In the other case, the employee is same, who has been moving among different departments, and

possibly during the process got new employee numbers. He has also been getting bonuses

regularly, maybe because every time he appears to be an employee who has never got a bonus.

This resulting in loss to the company and an inability of the company to do any meaningful

employee benefit analysis.

Data Duplication: House Holding

Group together all records that belong to the same household.

......

S. Ahad

440, Munir Road, Lahore

...

......

................

....................................

...

......

No. 440, Munir Rd, Lhr

Shiekh Ahad

...

......

................

....................................

...

......

Shiekh Ahed

House # 440, Munir Road, Lahore

...

Why

bother?

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Why bother?

In a joint family system, many working people live at the same house, but have different bank

accounts and ATM cards. If the bank would like to introduce a new service and wants to get in

touch with its customers, it would be much better if a single letter is sent to a single household,

instead of sending multiple letters. The problem is difficult because multiple persons living at the

same house may have written the same address differently even worse, one person with multiple

accounts at the same bank may have written his/her name and address differently.

One month is a typical business cycle in certain direct marketing operations. This means that

sources of data need to be identified, acquired, conditioned and then correlated or merged within

a small portion of a month in order to prepare mailings and response analyses. With tens of

thousands of mails to be sent, reducing the numbers of duplicate mails sent to the same household

can result in a significant savings.

Data Duplication: Individualization

Identify multiple records in each household which represent the same individual

.........

M. Ahad

440, Munir Road, Lahore

.........

................

....................................

.........

Maj Ahad

440, Munir Road, Lahore

Address field is standardized, is this by coincidence? This could be your luck data, but don't

count on it i.e. this is very unlikely to be the default case.

Formal definition & Nomenclature

Problem statement:

§ "Given two databases, identify the potentially matched records Efficiently and

Effectively"

Many names, such as:

§ Record linkage

§ Merge/purge

§ Entity reconciliation

§ List washing and data cleansing.

Current market and tools heavily centered towards customer lists.

Within the data warehousing field, data cleansing is applied especially when several databases are

merged. Records referring to the same entity are represented in different formats in the different

data sets or are represented erroneously. Thus, duplicate records will appear in the merged

database. The issue is to identify and eliminate these duplicates. The problem is known as the

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merge/purge problem. Instances of this problem appearing in literature are called record linkage,

semantic integration, instance identification, or o bject identity problem.

Data cleansing is much more than simply updating a record with good data. Serious data

cleansing involves decomposing and reassembling the data. One can break down the cleansing

into six steps: elementizing, standardizing, verifyin g, matching, house holding, and documenting.

Although data cleansing can take many forms, the current marketplace and the current technology

for data cleansing are heavily focused on customer lists.

Need & Tool Support

Logical solution to dirty data is to clean it in some way.

Doing it manually is very slow and prone to errors.

Tools are required to do it "cost" effectively to achieve reasonable quality.

Tools are there, some for specific fields, others for specific cleaning phase.

Since application specific, so work very well, but need support from other tools for

broad spectrum of cleaning problems.

For existing data sets, the logical solution to the dirty data problem is to attempt to cleanse the

data in some way. That is, explore the data set for possible problems and endeavor to correct the

errors. Of course, for any real world data set, doing this task "by hand" is completely out of the

question given the amount of man hours involved. Some organizations spend millions of dollars

per year to detect data errors. A manual process of data cleansing is also laborious, time

consuming, and itself prone to errors. The need for useful and powerful tools that automate or

greatly assist in the data cleansing process are necessary and may be the only practical and cost

effective way to achieve a reasonable quality level in an existing data set.

A large variety of tools is available in the market to support data transformation and data

cleaning tasks, in particular for data warehousing. Some tools concentrate on a specific domain,

such as cleaning name and address data, or a specific cleaning phase, such as data analysis or

duplicate elimination. Due to their restricted domain, specialized tools typically perform very

well but must be complemented by other tools to address the broad spectrum of transformation

and cleaning problems.

Overview of the Basic Concept

In its simplest form, there is an identifying attribute (or combination) per record for

identification.

Records can be from single source or multiple sources sharing same PK or other common

unique attributes.

Sorting performed on identifying attributes and neighboring records checked.

What if no common attributes or dirty data?

The degree of similarity measured numerically, different attributes may contribute

differently.

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In the simplest case, there is an identifying attribute or attribute combination per record that can

be used for matching records, e.g., if different sources share the same primary key or if there are

other common unique attributes. For example people in income tax department get customer

details from the phone company and the Electricity Company and want to identify customers who

have high electricity and high telephone bill but do not pay tax accordingly. Would be the

common field, NID, they have changed over time, how about addresses, too many ways to write

addresses so have to figure out common attributes.

Instance matching between different sources is then achieved by a standard equi-join on the

identifying attribute(s), if you are very, very lucky. In the case of a single data set, matches can be

determined by sorting on the identifying attribute and checking if neighboring records match. In

both cases, efficient implementations can be achieved even for large data sets . Unfortunately,

without common key attributes or in the presence of dirty data such straightforward approaches

are often too restrictive. For example, consider a rule that states person records are likely to

correspond if name and portions of the address match.

The degree of similarity between two records, often measured by a numerical value between 0

and 1, usually depends on application characteristics. For instance, different attributes in a

matching rule may contribute different weight to the overall degree of similarity.

Basic Sorted Neighborhood (BSN) Method

Concatenate data into one sequential list of N records

Steps 1: Create Keys

§ Compute a key for each record in the list by extracting relevant fields or portions

of fields

Effectiveness of the this method highly depends on a properly chosen key

Step 2: Sort Data

§ Sort the records in the data list using the key of step 1

Step 3: Merge

§ Move a fixed size window through the sequential list of records limiting the

comparisons for matching records to those records in the window

If the size of the window is w records then every new record entering the window

is compared with the previous w-1 records.

1. Create Keys: Compute a key for each record in the list by extracting relevant fields or

portions of fields. The choice of the key depends upon an error model that may be viewed as

knowledge intensive and domain specific for the effectiveness of the sorted neighborhood

method. This highly depends on a properly chosen key with the assumption that common but

erroneous data will have closely matching keys .

2. Sort Data: Sort the records in the data list using the key of step -1.

3. Merge: Move a fixed size window through the sequential list of records limiting the

comparisons for matching records to those records in the window. If the size of the window is

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w records as shown in Figure 20.1, then every new record entering the window is compared

with the previous w - 1 records to find "matching" records. The first record in the window

slides out of the window.

Note that in the absence of a window, the comparison would cover all the sorted records i.e. a

pair-wise comparison, resulting in O (n2) comparisons.

BSN Method : Sliding Window

Current window

of records

window

of records

Figure-20.1: BSN Method: Sliding Window

Figure -20.1 shows the outcome of the sliding window i.e. the IDs sorted after completion of the

run. The outcome will also depend on the window size, as it may so happen that when the

window size is relatively small and the keys are dispersed then some (or most o f the keys) may

not land in their corresponding neighborhood, thus defeating the purpose of the BSN method.

One way of overcoming this problem is working with different window sizes in a multipass

approach.

Complexity Analysis of BSN Method

Time Complexi ty: O(n log n)

§ O (n) for Key Creation

§ O (n log n) for Sorting

§ O (w n) for matching, where w ≤ 2 ≤ n

§ Constants vary a lot

At least three passes required on the dataset.

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For large sets disk I/O is detrimental.

Complexity or rule and window size detrimental.

When this procedure is executed serially as a main memory based process the create keys phase is

an O(n) operation the sorting phase is O(n log n) and the merging phase is O( wn) where n is the

number of records in the database. Thus, the total time complexity of this method is O (n log n) if

w < log n , O(wn) otherwise. However the constants in the equations differ greatly. It could be

relatively expensive to extract relevant key values from a record during the create key phase.

Sorting requires a few machine instructions to compare the keys. The merge phase requires the

application of a potentially large number of rules to compare two records and thus has the

potential for the largest constant factor.

Notice that w is a parameter of the window scanning procedure. The legitimate values of w may

range from 2 (whereby only two consecutive elements are compared) to n (whereby each element

is compared to all others). The latter case pertains to the full quadratic O (n2) time process at the

maximal potential accuracy. The former case (where w may be viewed as a small constant

relative to n) pertains to optimal time performance (only O (n) time) but at minimal accuracy.

Note however that for very large databases the dominant cost is likely disk I/O and hence the

number of passes over the data set. In this case at least three passes would be needed one pass for

conditioning the data and preparing keys at least a second pass likely more for a high speed sort,

and a final pass for window processing and application of the rule program for each record

entering the sliding window. Depending upon the complexity of the rule program and window

size w the last pass may indeed be the dominant cost.

BSN Method: Selection of Keys

Selection of Keys

§ Effectiveness highly dependent on the key selected to sort the records middle

name vs. last name,

A key is a sequence of a subset of attributes or sub -strings within the attributes

chosen from the record.

The keys are used for sorting the entire dataset with the intention that matched

candidates will appear close to each other.

First

Middle Address

NID

Key

MuhammedAhmad 440 Munir Road

34535322AHM440MUN345

MuhammadAhmad 440 Munir Road

34535322AHM440MUN345

MuhammedAhmed 440 Munir Road

34535322AHM440MUN345

MuhammadAhmar 440 Munawar Road 34535334AHM440MUN345

Table-20.1: BSN Method: Selection of Keys

The key consists of the concatenation of several ordered fields or attributes in the data. The first

three letters of a middle name are concatenated with the first three letters of the first name field

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followed by the address number field and all of the consonants of the road name. This is followed

by the first three digits of the National ID field as shown in Table 20.1.

These choices are made since the key designer determined that family names are not very

discriminating (Khan, Malik andCheema). The first names are also not very discriminating and

are typically misspelled (Muhammed, Muhammad, Mohamed) due to mistakes in vocalized

sounds vowels but middle names are typically more uncommon and less prone to being

misunderstood and hence less likely to be recorded incorrectly.

The keys are now used for sorting the entire dataset with the intention that all equivalent or

matching data will appear close to each other in the final sorted list. Notice in table 20.1, how the

first and second records are exact duplicates while the third is likely to be the same person but

with a misspelled middle name i.e. Ahmed instead of Ahmad. We would expect that this

phonetically bas ed mistake will be caught by a reasonable equational theory. However the fourth

record although having the exact same key as the prior three records appears unlikely to be the

same person.

BSN Method: Problem with keys

Since data is dirty, so keys WILL also be dirty, and matching records will not come

together.

Data becomes dirty due to data entry errors or use of abbreviations. Some real examples

are as follows:

Technology

Tech.

Techno.

Tchnlgy

Solution is to use external standard source files to validate the data and resolve any data

conflicts.

Since the data is dirty and the keys are extracted directly from the data then the keys for sorting

will also be dirty. Therefore the process of sorting the records to bring matching records togethe r

will not be as effective. A substantial number of matching records may not be detected in the

subsequent window scan.

Data can become dirty due to data entry errors. To speed -up data entry abbreviations are also

used (all taken from the telephone directory.), such as:

Tehcnology, Tech. Tehcno. Tchnlgy

The above is a typical case of a non-PK error of same entity with multiple representations. To

ensure the correctness of data in the database, solution is using external source files to validate

the data and resolve any data conflicts.

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BSN Method: Problem with keys (e.g.)

If contents of fields are not properly ordered, similar records will NOT fall in the same window.

Example: Records 1 and 2 are similar but will occur far apart.

Name

Address

Gender

1 N. Jaffri, SyedNo. 420, Street 15, Chaklala 4, Rawalpindi M

2 S. Noman

420, Scheme 4, Rwp

3 Saiam Noor Flat 5, Afshan Colony, Saidpur Road, Lahore F

Solution is to TOKENize the fields i.e. break them further. Use the tokens in different fields for

sorting to fix the error.

Example: Either using the name or the address field records 1 and 2 will fall close.

Name

Address

Gender

Syed N Jaffri

420 15 4 Chaklala No Rawalpindi Street

Syed Noman

420 4 Rwp Scheme

Saiam Noor

5 Afshan Colony Flat Lahore Road Saidpur

We observe that characters in a string can be grouped into meaningful pieces. We can often

identify important components or tokens within a Name or Address field by using a set of

delimiters such as space and punctua tions. Hence we can first tokenize these fields and then sort

the tokens within these fields. Records are then sorted on the select key fields; note that this

approach is an improvement over the standard BSN method.

BSN Method: Matching Candidates

Mergin g of records is a complex inferential process.

Example-1: Two persons with names spelled nearly but not identically, have the exact same

address. We infer they are same person i.e. Noma Abdullah and Noman Abdullah.

Example-2: Two persons have same National ID numbers but names and addresses are

completely different. We infer same person who changed his name and moved or the records

represent different persons and NID is incorrect for one of them.

Use of further information such as age, gender etc. can alter the decision.

Example-3: Noma-F and Noman-M we could perhaps infer that Noma and Noman are siblings

i.e. brothers and sisters. Noma-30 and Noman-5 i.e. mother and son.

The comparison of records during the merge phase to determine their equivalenc e is a complex

inferential process that considers and requires much more information in the compared records

than the keys used for sorting. For example suppose two person names are spelled nearly but not

identically, and have the exact same address. We might infer they are the same person. For

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example Noma Abdullah and Noman Abdullah could be the same person if they have the same

address.

On the other hand suppose two records have exactly the same National ID numbers but the names

and addresses are completely different. We could either assume the records represent the same

person who changed his name and moved or the records represent different persons and the NID

number field is incorrect for one of them. Without any further information we may perhaps

assume the latter. The more information there is in the records the better inferences can be made.

For example, if gender and age information is available in some field of the data (Noma-F,

Noman-M) we could perhaps infer that Noma and Noman are siblings.

BSN Method: Equational Theory

To specify the inferences we need equational Theory.

Logic is NOT based on string equivalence.

Logic based on domain equivalence.

Requires declarative rule language.

To specify the inferences as discussed above we need an equational theory in which the logic is

based not on the string equivalence but on the domain equivalence. A natural approach to

specifying an equational theory and making it practical too would be the use of a declarative rule

language. Rule languages have been effectively used in a wide range of applications requiring

inference over large data sets. Much research has been conducted to provide efficient means for

their compilation and evaluation and this technology can be exploited here for purposes of data

cleansing efficiently.

BSN Method: Rule Language

Rule Language Example

Given two records r1 and r2

IF the family_name of r1 equals the family_name of r2

AND the first names differ slightly

AND the address of r1 equals the address of r2

THEN r1 is equivalent to r2

This is rather self explanatory. The rule merely states that if for two records the last names and

the addresses are same, but the first names differ slightly, then both the records are same. This

makes sense, as using default values l st names can be easily fixed, and sorting the address and

then using default values can fix most part of the address too, as the addresses are repeating.

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BSN Method: Mis -Matching Candidates

Transposition of names

How do you specify "Differ Slightly"?

§ Calculate on the basis of a distance function applied to the family_name fields of

two records

Multiple options for distance functions

Notice that rules do not necessarily need to compare values from the same attribute or same

domain. For instance to detect a transposition in a persons name we could write a rule that

compares the first name of one record with the last name of the second record and the last name

of the first record with the first name of the second record.

BSN Method: Distance Metrics

Distance Functions

§ Phonetic distance

§ Typewriter distance

§ Edit Distance

A widely used metric to define string similarity

§ Ed(s1,s2)= minimum # of operations (insertion, deletion, substitution) to change

s1 to s2

Example:

s1: Muhammad Ehasn

s2: Muha mmed Ahasn

ed(s1,s2) = 2

The implementation is based upon the computation of a distance function applied to the first

name fields of two records and the comparison of its results to a threshold to capture obvious

typographical errors that may occur in the data. The selection of a distance function and a proper

threshold is also a knowledge intensive activity that demands experimental evaluation. An

improperly chosen threshold will lead to either an increase in the number of falsely matched

records or to a decrease in the number of matching records that should be merged. A number of

alternative distance functions for typographical mistakes are possible, including distances based

upon (i) edit distance (ii) phonetic distance and (iii) typewriter distance.

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Limitations of BSN Method

BSN Method Limitations

§ No single key is sufficient to catch all matching records.

Fields that appear first in the key have higher discriminating power than those

appearing after them.

If NID number is first attribute in the key 81684854432 and 18684854432 are

highly likely to fall in windows that are far apart.

Two Possible Modifications to BSN Method

§ Increase w, the size of window

§ Multiple passes over the data set

In general no single key will be sufficient to catch all matching records. The attributes or fields

that appear first in the key have higher discriminating power than those appearing after them.

Hence if the error in a record occurs in the particular field or portion of the field that is the most

important part of the key there may be little chance a record will end up close to a matching

record after sorting. For instance if an employee has two records in the database one with NID

number 81684854432 and another with NID number 18684854432 (the first two numbers were

transposed) and if the NID number is used as the principal field of the key then it is very unlikely

both records will fall under the same window i.e. the two records with transposed NID numbers

will be far apart in the sorted list and hence they may not be merged. As it was empirically

established that the number of matching records missed by one run of the sorted neighborhood

method can be large unless the neighborhood grows very large.

Increasing w

Increases the computational complexity.

Quality does not increase dramatically.

Not useful unless the window spans the entire database.

§ W spanning the entire set of rows is infeasible under strict time and cost

constraints.

What would be the computational complexity?

O(w2)

Increasing w clearly this increases the computational complexity and as discussed in the next

section does not increase dramatically the number of similar records merged in the test cases we

ran unless of course the window spans the entire database which we have presumed is infeasible

under strict time and cost constraints.

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Multi-pass Approach

Several independent runs of the BSN method each time with a different key and a

relatively small window.

Each independent run will produce a set of pairs of records which can be merged (takes

care of transposition errors)

Apply the transitive closure property to those pairs of records.

§ If records a and b are found to be similar and at the same time records b and c are

also found to be similar the transitive closure step ca n mark a and c to be similar.

The results will be a union of all pairs discovered by all independent runs with no

duplicates plus all those pairs that can be inferred by transitivity of equality.

The alternative strategy we implemented is to execute sev eral independent runs of the sorted

neighborhood method each time using a different key and a relatively small window. We call this

strategy the multipass approach. For instance in one run we use the address as the principal part

of the key while in another run we use the last name of the employee as the principal part of the

key. Each independent run will produce a set of pairs of records which can be merged. We then

apply the transitive closure to those pairs of records.

The results will be a union of a ll pairs discovered by all independent runs with no duplicates plus

all those pairs that can be inferred by transitivity of equality. The reason this approach works for

the test cases explored here has much to do with the nature of the errors in the data. Transposing

the first two digits of the NID number leads to nonmergeable records as we noted. However in

such records the variability or error appearing in another field of the records may indeed not be so

large. Therefore although the NID numbers in two records are grossly in error the name fields

may not be. Hence first sorting on the name fields as the primary key will bring these two records

closer together, lessening the negative effects of a gross error in the social security field.

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