Noname manuscript No. (will be inserted by the editor) Approximate Similarity Search for Online Multimedia Services on Distributed CPU–GPU Platforms

Abstract Similarity search in high-dimentional 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, we introduce hyper-curves, a flexible framework for answering approximate k-nearest neighbor (kNN) queries for very large multimedia databases, aiming E. Valle and R. Torres thank FAPESP for the finan-cial support to this work. Preprint — submitted for peer review