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Similarity search in high-dimensional spaces is a pivotal operation found a variety of database applications. Recently, there has been an increase interest in similarity search for online content-based multimedia services. Those services, however, introduce new challenges with respect to the very large volumes of data that have to be indexed/searched, and the need to minimize response times observed by the end-users. Additionally, those users dynamically interact with the systems creating fluctuating query request rates, requiring the search algorithm to adapt in order to better utilize the underline hardware to reduce response times. In order to address these challenges, the authors introduce hyper-curves, a flexible framework for answering approximate k-Nearest Neighbor (kNN) queries for very large multimedia databases, aiming at online content-based multimedia services.
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