Name
Abstract | ||
|---|---|---|
AbstractThere is an increasing gap between fast growth of data and limited human ability to comprehend data. Consequently, there has been a growing demand of data management tools that can bridge this gap and help the user retrieve high-value content from data more effectively. In this work, we aim to build interactive data exploration as a new database service, using an approach called "explore-by-example". In particular, we cast the explore-by-example problem in a principled "active learning" framework, and bring the properties of important classes of database queries to bear on the design of new algorithms and optimizations for active learning-based database exploration. These new techniques allow the database system to overcome a fundamental limitation of traditional active learning, i.e., the slow convergence problem. Evaluation results using real-world datasets and user interest patterns show that our new system significantly outperforms state-of-the-art active learning techniques and data exploration systems in accuracy while achieving desired efficiency for interactive performance. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.14778/3275536.3275542 | Hosted Content |
DocType | Volume | Issue |
Journal | 12 | 1 |
ISSN | Citations | PageRank |
2150-8097 | 3 | 0.36 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enhui Huang | 1 | 3 | 0.36 |
| Liping Peng | 2 | 107 | 7.50 |
| Luciano Di Palma | 3 | 3 | 0.36 |
| Ahmed Abdelkafi | 4 | 3 | 0.36 |
| Anna Liu | 5 | 566 | 23.91 |
| Yanlei Diao | 6 | 2234 | 108.95 |