Name
Abstract | ||
|---|---|---|
AbstractIn the recent past, the amount of high-dimensional data, such as feature vectors extracted from multimedia data, increased dramatically. A large variety of indexes have been proposed to store and access such data efficiently. However, due to specific requirements of a certain use case, choosing an adequate index structure is a complex and time-consuming task. This may be due to engineering challenges or open research questions. To overcome this limitation, we present QuEval, an open-source framework that can be flexibly extended w.r.t. index structures, distance metrics, and data sets. QuEval provides a unified environment for a sound evaluation of different indexes, for instance, to support tuning of indexes. In an empirical evaluation, we show how to apply our framework, motivate benefits, and demonstrate analysis possibilities. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.14778/2556549.2556551 | Hosted Content |
Keywords | DocType | Volume |
High-dimensional index selection & tuning, evaluation framework | Journal | 6 |
Issue | ISSN | Citations |
14 | 2150-8097 | 8 |
PageRank | References | Authors |
0.44 | 21 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Schäler | 1 | 43 | 11.55 |
| Alexander Grebhahn | 2 | 150 | 9.11 |
| Reimar Schröter | 3 | 120 | 8.75 |
| Sandro Schulze | 4 | 259 | 23.43 |
| Veit Köppen | 5 | 115 | 18.69 |
| Gunter Saake | 6 | 3255 | 639.75 |