High-Dimensional Similarity Search for Scalable Data Science
Karima Echihabi, Kostas Zoumpatianos, Themis Palpanas
Abstract
Similarity search is a core operation of many critical data science applications, involving massive collections of high-dimensional objects. Similarity search finds objects in a collection close to a given query according to some definition of sameness. Objects can be data series, text, multimedia, graphs, database tables or deep network embeddings. In this tutorial, we revisit the similarity search problem in light of the recent advances in the field and the new big data landscape. We discuss key data science applications that require efficient high-dimensional similarity search, we survey the state-of-the-art high-dimensional similarity search approaches and share surprising insights about their strengths and weaknesses, and we discuss the challenges and open research problems in this area.