DaiSy: A Library for Scalable Data Series Similarity Search

TL;DR

DaiSy is a comprehensive, open-source library that unifies multiple algorithms for exact data series similarity search across various execution environments, including disk, memory, GPU, and distributed systems.

Contribution

It introduces DaiSy, the first library supporting exact similarity search across diverse environments with multi-language interfaces, enhancing flexibility and scalability.

Findings

Supports exact similarity search on large data series collections.

Enables use in various environments including GPU and distributed systems.

Provides interfaces in C++ and Python for easy integration.

Abstract

Exact similarity search over large collections of data series is a fundamental operation in modern applications, yet existing solutions are often fragmented, specialized, or tailored to specific execution environments. In this paper, we present DaiSy, a unified library for exact data series similarity search that integrates multiple state-of-the-art algorithms within a single, coherent framework. DaiSy is the first library to support exact similarity search across diverse execution environments, including implementations for disk-based, in-memory, GPU-accelerated, and distributed scalable similarity search. Although designed for data series, DaiSy is also directly applicable to exact similarity search over vector data, enabling its use in a broader range of applications. The library supports interfaces in both C++ and Python, enabling users to easily integrate its functionality into a variety of tasks. DaiSy is open-sourced and available at: https://github.com/MChatzakis/DaiSy.