A lot of multimedia data are being created nowadays, which can#N#only be searched by content since no searching metadata are#N#available for them. To make the content search efficient,#N#similarity indexing structures based on the metric-space model#N#can be used. In our work, we focus on a scenario where the#N#similarity search is used in the context of stream processing.#N#In particular, there is a potentially infinite sequence#N#(stream) of query objects, and a query needs to be executed for#N#each of them. The goal is to maximize the throughput of#N#processed queries while maintaining an acceptable delay. We#N#propose an approach based on dynamic reordering of the incoming#N#queries combined with caching of recent results. We were able#N#to achieve up to 3.7 times higher throughput compared to the#N#base case when no reordering and caching is used.
['Filip Nalepa', 'Michal Batko', 'Pavel Zezula']
Enhancing Similarity Search Throughput by Dynamic Query Reordering