Abstract
Entity resolution is a fundamental problem in data integration dealing with the combination of data from different sources to a unified view of the data. Entity resolution is inherently an uncertain process because the decision to map a set of records to the same entity cannot be made with certainty unless these are identical in all of their attributes or have a common key. In the light of recent advancement in data accumulation, management, and analytics landscape (known as big data) the tutorial re-evaluates the entity resolution process and in particular looks at best ways to handle data veracity. The tutorial ties entity resolution with recent advances in probabilistic database research, focusing on sources of uncertainty in the entity resolution process.
| Original language | English |
|---|---|
| Pages (from-to) | 1711-1712 |
| Number of pages | 2 |
| Journal | Proceedings of the VLDB Endowment |
| Volume | 7 |
| Issue number | 13 |
| DOIs | |
| State | Published - 2014 |
| Event | Proceedings of the 40th International Conference on Very Large Data Bases, VLDB 2014 - Hangzhou, China Duration: 1 Sep 2014 → 5 Sep 2014 |
Keywords
- Big data
- Data integration
- Entity resolution
- Uncertainty management
All Science Journal Classification (ASJC) codes
- Computer Science (miscellaneous)
- General Computer Science
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