Name
Title | ||
|---|---|---|
The Expected Optimal Labeling Order Problem for Crowdsourced Joins and Entity Resolution. |
Abstract | ||
|---|---|---|
In the SIGMOD 2013 conference, we published a paper extending our earlier work on crowdsourced entity resolution to improve crowdsourced join processing by exploiting transitive relationships [Wang et al. 2013]. The VLDB 2014 conference has a paper that follows up on our previous work [Vesdapunt et al., 2014], which points out and corrects a mistake we made in our SIGMOD paper. Specifically, in Section 4.2 of our SIGMOD paper, we defined the "Expected Optimal Labeling Order" (EOLO) problem, and proposed an algorithm for solving it. We incorrectly claimed that our algorithm is optimal. In their paper, Vesdapunt et al. show that the problem is actually NP-Hard, and based on that observation, propose a new algorithm to solve it. In this note, we would like to put the Vesdapunt et al. results in context, something we believe that their paper does not adequately do. |
Year | Venue | Field |
|---|---|---|
2014 | CoRR | Data mining,Joins,Name resolution,Mistake,Computer science,Very large database,Database,Transitive relation |
DocType | Volume | Citations |
Journal | abs/1409.7472 | 2 |
PageRank | References | Authors |
0.45 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiannan Wang | 1 | 1109 | 45.38 |
| Guoliang Li | 2 | 3077 | 154.70 |
| Tim Kraska | 3 | 2226 | 133.57 |
| Michael J. Franklin | 4 | 17423 | 1681.10 |
| Jianhua Feng | 5 | 2713 | 121.30 |