Abstract
We present an unsupervised approach for harvesting the data exposed by a set of structured and partially overlapping data-intensive web sources. Our proposal comes within a formal framework tackling two problems: the data extraction problem, to generate extraction rules based on the input websites, and the data integration problem, to integrate the extracted data in a unified schema. We introduce an original algorithm, WEIR, to solve the stated problems and formally prove its correctness. WEIR leverages the overlapping data among sources to make better decisions both in the data extraction (by pruning rules that do not lead to redundant information) and in the data integration (by reflecting local properties of a source over the mediated schema). Along the way, we characterize the amount of redundancy needed by our algorithm to produce a solution, and present experimental results to show the benefits of our approach with respect to existing solutions.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 805-816 |
| Number of pages | 12 |
| Journal | Proceedings of the VLDB Endowment |
| Volume | 6 |
| Issue number | 10 |
| DOIs | |
| State | Published - Aug 2013 |
| Externally published | Yes |
| Event | 39th International Conference on Very Large Data Bases, VLDB 2012 - Trento, Italy Duration: Aug 26 2013 → Aug 30 2013 |
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- General Computer Science