QuEval: Beyond High-Dimensional Indexing a La Carte

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Provided by: VLD Digital
Topic: Big Data
Format: PDF
In 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, the authors present QuEval, an open-source framework that can be flexibly extended w.r.t. index structures, distance metrics, and data sets.
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