Name
Abstract | ||
|---|---|---|
AbstractData analysts often engage in data exploration tasks to discover interesting data patterns, without knowing exactly what they are looking for. Such exploration tasks can be very labor-intensive because they often require the user to review many results of ad-hoc queries and adjust the predicates of subsequent queries to balance the tradeoff between collecting all interesting information and reducing the size of returned data. In this demonstration we introduce AIDE, a system that automates these exploration tasks. AIDE steers the user towards interesting data areas based on her relevance feedback on database samples, aiming to achieve the goal of identifying all database objects that match the user interest with high efficiency. In our demonstration, conference attendees will see AIDE in action for a variety of exploration tasks on real-world datasets. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.14778/2824032.2824112 | Hosted Content |
Field | DocType | Volume |
Data mining,Relevance feedback,Data patterns,Data exploration,Computer science,Navigation system,Database | Journal | 8 |
Issue | ISSN | Citations |
12 | 2150-8097 | 2 |
PageRank | References | Authors |
0.37 | 4 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanlei Diao | 1 | 2234 | 108.95 |
| Kyriaki Dimitriadou | 2 | 90 | 5.17 |
| Zhan Li | 3 | 2 | 0.37 |
| Wenzhao Liu | 4 | 8 | 2.25 |
| Olga Papaemmanouil | 5 | 431 | 27.21 |
| Kemi Peng | 6 | 2 | 0.37 |
| Liping Peng | 7 | 4 | 0.74 |