Name
Abstract | ||
|---|---|---|
Entity Resolution is the process of determining if two information system records are referring to the same entities, and is a crucial part in Information Quality research. The ER process becomes exponentially more complex and time consuming as datasets approach Big Data volumes. Due to the special characters of transitive closure in Entity Resolution and high volume of input data, traditional ER pairwise matching algorithms are not able to solve the problem efficiently. This paper presents a methodology to perform Entity Resolution without pairwise matching using match keys. Transitive closure occurs when each input reference can potentially create more than one match key. This paper also introduces a novel distributed parallel transitive closure algorithm in Entity Resolution context and an optimized version, which applies the method on multiple match keys. The implementation of the methodology is built upon the Hadoop MapReduce for distributed computation. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICDMW.2015.197 | ICDM Workshops |
Keywords | Field | DocType |
Entity Resolution, Transitive Closure, Hadoop | Information system,Pairwise comparison,Metadata,Data mining,Algorithm design,XML,Computer science,Artificial intelligence,Transitive closure,Big data,Machine learning,Computation | Conference |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng Chen | 1 | 0 | 0.34 |
| Daniel Pullen | 2 | 0 | 3.04 |
| Reed H. Petty | 3 | 0 | 0.34 |
| John R. Talburt | 4 | 95 | 42.78 |