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- W2148016837 abstract "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, 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." @default.
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- W2148016837 date "2013-09-01" @default.
- W2148016837 modified "2023-09-23" @default.
- W2148016837 title "QuEval" @default.
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- W2148016837 doi "https://doi.org/10.14778/2556549.2556551" @default.
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