1 Haiguang Li 01. Dec Real-World Data Is Dirty Data Cleansing and the Merge/Purge Problem Hernandez & Stolfo: Columbia University Class Presentation by Haiguang Li, 01. Dec 2011

2 Haiguang Li 01. Dec TOPICS Introduction A Basic Data Cleansing Solution Test & Real World Results Incremental Merge Purge w/ New Data Conclusion Recap

3 Haiguang Li 01. Dec Introduction

4 Haiguang Li 01. Dec The problem: Some corporations acquire large amounts of information every month The data is stored in many large databases (DB) These databases may be heterogeneous Variations in schema The data may be represented differently across the various datasets Data in these DB may simply be inaccurate

5 Haiguang Li 01. Dec Requirement of the analysis The data mining needs to be done Quickly Efficiently Accurately

6 Haiguang Li 01. Dec Examples of real-world applications Credit card companies Assess risk of potential new customers Find false identities Match disparate records concerning a customer Mass Marketing companies Government agencies

7 Haiguang Li 01. Dec A Basic Data Cleansing Solution

8 Haiguang Li 01. Dec Duplicate Elimination Sorted-Neighborhood Method (SNM) This is done in three phases Create a Key for each record Sort records on this key Merge/Purge records

9 Haiguang Li 01. Dec SNM: Create key Compute a key for each record by extracting relevant fields or portions of fields Example: FirstLastAddressIDKey SalStolfo123 First Street STLSAL123FRST456

10 Haiguang Li 01. Dec SNM: Sort Data Sort the records in the data list using the key in step 1 This can be very time consuming O(NlogN) for a good algorithm, O(N 2 ) for a bad algorithm

11 Haiguang Li 01. Dec SNM: Merge records Move a fixed size window through the sequential list of records. This limits the comparisons to the records in the window

12 Haiguang Li 01. Dec SNM: Considerations What is the optimal window size while Maximizing accuracy Minimizing computational cost Execution time for large DB will be bound by Disk I/O Number of passes over the data set

13 Haiguang Li 01. Dec Selection of Keys The effectiveness of the SNM highly depends on the key selected to sort the records A key is defined to be a sequence of a subset of attributes Keys must provide sufficient discriminating power

14 Haiguang Li 01. Dec Example of Records and Keys FirstLastAddressIDKey SalStolfo123 First Street STLSAL123FRST456 SalStolfo123 First Street STLSAL123FRST456 SalStolpho123 First Street STLSAL123FRST456 SalStiles123 Forest Street STLSAL123FRST456

15 Haiguang Li 01. Dec Equational Theory The comparison during the merge phase is an inferential process Compares much more information than simply the key The more information there is, the better inferences can be made

16 Haiguang Li 01. Dec Equational Theory - Example Two names are spelled nearly identically and have the same address It may be inferred that they are the same person Two social security numbers are the same but the names and addresses are totally different Could be the same person who moved Could be two different people and there is an error in the social security number

17 Haiguang Li 01. Dec A simplified rule in English Given two records, r1 and r2 IF the last name of r1 equals the last 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

18 Haiguang Li 01. Dec The distance function A “distance function” is used to compare pieces of data (usually text) Apply “distance function” to data that “differ slightly” Select a threshold to capture obvious typographical errors. Impacts number of successful matches and number of false positives

19 Haiguang Li 01. Dec Examples of matched records SSNName (First, Initial, Last)Address Lisa Boardman Lisa Brown 144 Wars St. 144 Ward St Ramon Bonilla Raymond Bonilla 38 Ward St Diana D. Ambrosion Diana A. Dambrosion 40 Brik Church Av. 40 Brick Church Av Kathi Kason Kathy Kason 48 North St Kathy Kason Kathy Smith 48 North St.

20 Haiguang Li 01. Dec Building an equational theory The process of creating a good equational theory is similar to the process of creating a good knowledge- base for an expert system In complex problems, an expert’s assistance is needed to write the equational theory

21 Haiguang Li 01. Dec Transitive Closure In general, no single pass (i.e. no single key) will be sufficient to catch all matching records An attribute that appears first in the key has higher discriminating power than those appearing after them If an employee has two records in a DB with SSN and , it’s unlikely they will fall under the same window

22 Haiguang Li 01. Dec Transitive Closure To increase the number of similar records merged Widen the scanning window size, w Execute several independent runs of the SNM  Use a different key each time  Use a relatively small window  Call this the Multi-Pass approach

23 Haiguang Li 01. Dec Transitive Closure Each independent run of the Multi-Pass approach will produce a set of pairs of records Although one field in a record may be in error, another field may not Transitive closure can be applied to those pairs to be merged

24 Haiguang Li 01. Dec Multi-pass Matches Pass 1 (Lastname discriminates) KSNKAT48NRTH789 (Kathi Kason ) KSNKAT48NRTH879 (Kathy Kason ) Pass 2 (Firstname discriminates) KATKSN48NRTH789 (Kathi Kason ) KATKSN48NRTH879 (Kathy Kason ) Pass 3 (Address discriminates) 48NRTH879KSNKAT (Kathy Kason ) 48NRTH879SMTKAT (Kathy Smith )

25 Haiguang Li 01. Dec Transitive Equality Example IF A implies B AND B implies C THEN A implies C From example: Kathi Kason 48 North St. (A) Kathy Kason 48 North St. (B) Kathy Smith 48 North St. (C)

26 Haiguang Li 01. Dec Test Results

27 Haiguang Li 01. Dec Test Environment Test data was created by a database generator Names are randomly chosen from a list of real names The database generator provides a large number of parameters: size of the DB, percentage of duplicates, amount of error…

28 Haiguang Li 01. Dec Correct Duplicate Detection

29 Haiguang Li 01. Dec Time for each run

30 Haiguang Li 01. Dec Accuracy for each run

31 Haiguang Li 01. Dec Real-World Test Data was obtained from the Office of Children Administrative Research (OCAR) of the Department of Social and Health Services (State of Washington) OCAR’s goals How long do children stay in foster care? How many different homes do children typically stay in?

32 Haiguang Li 01. Dec OCAR’s Database Most of OCAR’s data is stored in one relation The DB contains 6,000,000 total records The DB grows by about 50,000 records per month

33 Haiguang Li 01. Dec Typical Problems in the DB Names are frequently misspelled SSN or birthdays are either missing or clearly wrong Case number often changes when the child’s family moves to another part of the state Some records use service provider names instead of the child’s No reliable unique identifier

34 Haiguang Li 01. Dec OCAR Equational Theory Keys for the independent runs Last Name, First Name, SSN, Case Number First Name, Last Name, SSN, Case Number Case Number, First Name, Last Name, SSN

35 Haiguang Li 01. Dec OCAR Results

36 Haiguang Li 01. Dec Incremental Merge/Purge w/ New Data

37 Haiguang Li 01. Dec Incremental Merge/Purge Lists are concatenated for first time processing Concatenating new data before reapplying the merge/purge process may be very expensive in both time and space An incremental merge/purge approach is needed: Prime Representatives method

38 Haiguang Li 01. Dec Prime-Representative: Definition A set of records extracted from each cluster of records used to represent the information in the cluster The “Cluster Centroid” or base element of equivalence class

39 Haiguang Li 01. Dec Prime-Representative creation Initially, no PR exists After the execution of the first merge/purge create clusters of similiar records Correct selection of PR from cluster impacts accuracy of results No PR can be the best selection for some clusters

40 Haiguang Li 01. Dec Strategies for Choosing PR Random Sample Select a sample of records at random from each cluster N-Latest Most recent elements entered in DB Syntactic Choose the largest or more complete record

41 Haiguang Li 01. Dec Important Assumption No data previously used to select each cluster’s PR will be deleted Deleted records could require restructuring of clusters (expensive) No changes in the rule-set will occur after the first increment of data is processed Substantial rule change could invalidate clusters.

42 Haiguang Li 01. Dec Results Cumulative running time for the Incremental Merge/Purge algorithm is higher than the classic algorithm PR selection methodology could improve cumulative running time Total running time of the Incremental Merge/Purge algorithm is always smaller

43 Haiguang Li 01. Dec Conclusion

44 Haiguang Li 01. Dec Cleansing of Data Sorted-Neighborhood Method is expensive due to the sorting phase the need for large windows for high accuracy Multiple passes with small windows followed by transitive closure improves accuracy and performance for level of accuracy increasing number of successful matches decreasing number of false positives

45 Haiguang Li 01. Dec major reasons merging large databases becomes a difficult problem: The databases are heterogeneous The identifiers or strings differ in how they are represented within each DB Questions 1?

46 Haiguang Li 01. Dec The 3 steps in SNM are: Creation of key(s) Sorting records on this key Merge/Purge records Questions 2?

47 Haiguang Li 01. Dec strategies for selecting a PR: Random Sample N-Latest Syntactic Questions 3?

48 Haiguang Li 01. Dec The End Thanks very much!