Orateur
Description
Very large amounts of high-dimensional data are now omnipresent
(ranging from traditional multidimensional data to time series and deep
embeddings), and the performance requirements (i.e., response-time and
accuracy) of a variety of applications that need to process and analyze
these data have become very stringent and demanding. In the past years, high-dimensional similarity search has been studied in its many
flavors. Similarity search algorithms for exact and approximate, one-off
and progressive query answering. Approximate algorithms with and without
(deterministic or probabilistic) quality guarantees. Solutions for
on-disk and in-memory data, static and streaming data. Approaches based
on multidimensional space-partitioning and metric trees, random
projections and locality-sensitive hashing (LSH), product quantization
(PQ) and inverted files, k-nearest neighbor graphs and optimized linear
scans. Surprisingly, the work on data-series (or time-series) similarity
search has recently been shown to achieve the state-of-the-art
performance for several variations of the problem, on both time-series
and general high-dimensional vector data. In this talk, we will touch
upon the different aspects of this interesting story, and present some of the state-of-the-art solutions.
