Efficient Record Linkage in Large Data Sets

1. Efficient Record Linkage in Large Data Sets Liang Jin, Chen Li, Sharad Mehrotra University of California, Irvine DASFAA, Kyoto, Japan, March 2003

2. Motivation • Correlate data from different data sources (e.g., data integration) • Data is often dirty • Needs to be cleansed before being used • Example: • A hospital needs to merge patient records from different data sources • They have different formats, typos, and abbreviations

3. Example Table R Table S • Find records from different datasets that could be the same entity

4. Another Example • P. Bernstein, D. Chiu: Using Semi-Joins to Solve Relational Queries. JACM 28(1): 25-40(1981) • Philip A. Bernstein, Dah-Ming W. Chiu, Using Semi-Joins to Solve Relational Queries, Journal of the ACM (JACM), v.28 n.1, p.25-40, Jan. 1981

5. Record linkage Problem statement: “Given two relations, identify the potentially matched records • Efficiently and • Effectively”

6. Challenges • How to define good similarity functions? • Many functions proposed (edit distance, cosine similarity, …) • Domain knowledge is critical • Names: “Wall Street Journal” and “LA Times” • Address: “Main Street” versus “Main St” • How to do matching efficiently • Offline join version • Online (interactive) search • Nearest search • Range search

7. Outline • Motivation of record linkage • Single-attribute case: two-step approach • Multi-attribute linkage • Conclusion and related work

8. Single-attribute Case • Given • two sets of strings, R and S • a similarity function f between strings (metric space) • Reflexive: f(s1,s2) = 0 iff s1=s2 • Symmetric: f(s1,s2) = d(s2, s1) • Triangle inequality: f(s1,s2)+f(s2,s3) >= f(s1,s3) • a threshold k • Find: all pairs of strings (r, s) from R and S, such that f(r,s) <= k. R S

9. Nested-loop? • Not desirable for large data sets • 5 hours for 30K strings!

10. Our 2-step approach • Step 1: map strings (in a metric space) to objects in a Euclidean space • Step 2: do a similarity join in the Euclidean space

11. Advantages • Applicable to many metric similarity functions • Use edit distance as an example • Other similarity functions also tried, e.g., q-gram-based similarity • Open to existing algorithms • Mapping techniques • Join techniques

12. Step 1 Map strings into a high-dimensional Euclidean space Metric Space Euclidean Space

13. Example: 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: Tom Hanks s2: Ton Hank ed(s1,s2) = 2

14. Mapping: StringMap • Input: A list of strings • Output: Points in a high-dimensional Euclidean space that preserve the original distances well • A variation of FastMap • Each step greedily picks two strings (pivots) to form an axis • All axes are orthogonal

15. Can it preserve distances? • Data Sources: • IMDB star names: 54,000 • German names: 132,000 • Distribution of string lengths:

16. Can it preserve distances? • Use data set 1 (54K names) as an example • k=2, d=20 • Use k’=5.2 to differentiate similar and dissimilar pairs.

17. Choose Dimensionality d Increase d? • Good : • better to differentiate similar pairs from dissimilar ones. • Bad : • Step 1: Efficiency ↓ • Step 2: “curse of dimensionality”

18. # of pairs within distance w Cost= # of similar pairs Choose dimensionality d using sampling • Sample 1Kx1K strings, find their similar pairs (within distance k) • Calculate maximum of their new distances w • Define “Cost” of finding a similar pair:

19. Choose Dimensionality d d=15 ~ 25

20. Choose new threshold k’ • Closely related to the mapping property • Ideally, if ed(r,s) <= k, the Euclidean distance between two corresponding points <= k’. • Choose k’ using sampling • Sample 1Kx1K strings, find similar pairs • Calculate their maximum new distance as k’ • repeat multiple times, choose their maximum

21. New threshold k’ in step 2 d=20

22. Step 2: Similarity Join • Input: Two sets of points in Euclidean space. • Output: Pairs of two points whose distance is less than new threshold k’. • Many join algorithms can be used

23. Example • Adopted an algorithm by Hjaltason and Samet. • Building two R-Trees. • Traverse two trees, find points whose distance is within k’. • Pruning during traversal (e.g., using MinDist).

24. Final processing • Among the pairs produced from the similarity-join step, check their edit distance. • Return those pairs satisfying the threshold k

25. Running time

26. Recall • Recall: (#of found similar pairs)/(#of all similar pairs)

27. Outline • Motivation of record linkage • Single-attribute case: two-step approach • Multi-attribute linkage • Conclusion and related work

28. Multi-attribute linkage • Example: title + name + year • Different attributes have different similarity functions and thresholds • Consider merge rules in disjunctive format:

29. Evaluation strategies • Many ways to evaluate rules • Finding an optimal one: NP-hard • Heuristics: • Treat different conjuncts independently. Pick the “most efficient” attribute in each conjunct. • Choose the largest threshold for each attribute. Then choose the “most efficient” attribute among these thresholds.

30. Summary • A novel two-step approach to record linkage. • Many existing mapping and join algorithms can be adopted • Applicable to many distance metrics. • Time and space efficient. • Multi-attribute case studied

31. Related work • Learning similarity functions: [Sarawagi and Bhamidipaty, 2003] • Efficient merge and purge: [Hernandez and Stolfo, 1995] • String edit-distance join using DBMS: [Gravano et al, 2001]

32. The Flamingo Project on Data Cleansing