daisy-exact-search 1.0.2

DaiSy: A Library for Scalable Data Series Similarity Search

DaiSy

A Library for Scalable Data Series Similarity Search

Francesca Del Gaudio, Manos Chatzakis, Gayathiri Ravendirane, Botao Peng, Themis Palpanas

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. We present DaiSy (Data series similarity Search library), 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. The library supports interfaces in both C++ and Python, enabling, researchers and practitioners to easily integrate its functionality in a variety of tasks.

ALPHA VERSION: Currently, DaiSy is experimental. The library is still under active development. We welcome suggestions and bug reports.

Supported State-of-the-Art algorithms

We currently support several algorithms for exact similarity search, each optimized for specific use cases and environments. The following table summarizes the key features of each algorithm:

Algorithm	Description
Bruteforce	Naive parallel similarity search implementation
Lower Bound Bruteforce	Optimized bruteforce with lower bounding for the distance calculations
MESSI	In-memory parallel similarity search
PARIS	Disk-based parallel similarity search
SING	GPU-accelerated in-memory parallel similarity search
Odyssey	Distributed and parallel in-memory similarity search

Quickstart

Dependencies

• Operating System: Linux, macOS, or Windows
• C++ Compiler: C++14 or higher (GCC 6+, Clang 3.4+, MSVC 2015+)
• CMake: Version 3.15 or higher

Optionally,

• Python: 3.10-3.12
• MPI: Required for Odyssey distributed computing algorithm
• CUDA: Required for SING GPU acceleration algorithm

Installation

To download DaiSy, use:

git clone https://github.com/MChatzakis/daisy.git

cd daisy
git submodule update --init --recursive

Based on the available hardware, you can specify the below arguments to enable/disable features.

Flag	Description	Default	Dependencies
BUILD_PYTHON	Enable Python bindings	OFF	Python 3.10+
BUILD_BENCHMARK	Build benchmarking tools	OFF	GoogleBenchmark
BUILD_TESTS	Build test suite	OFF	GoogleTest
BUILD_DEMO	Build demonstration applications	ON	Core library
BUILD_ODYSSEY	Enable MPI for distributed computing	OFF	OpenMPI/MPICH
BUILD_SING	Enable CUDA for GPU acceleration	OFF	CUDA Toolkit
DEBUG_MSG	Enable debug output	OFF	None

To compile:

mkdir build && cd build

cmake ..
make

DaiSy with Python

If you intent to use only the Python interface, you can install the library directly from PyPI using pip:

pip install daisy-exact-search

If you want to use Odyssey, you will need to install mpi:

pip install daisy-exact-search[mpi]

Others

We provide several usage examples in both C++ and Python under demos/, demonstrating how to utilize the library for various similarity search tasks. We provide several troubleshooting guides and extra resources in the docs/ directory. In this directory, we also provide useful information about how to contribute to the project, and how to implement new algorithms.

About

DaiSy is developed by researchers at the diNo research group, LIPADE, Université Paris Cité.

It is provided with no warranty, and we encourage contributions from the community to enhance its capabilities and performance. For questions, issues, or contributions, please open an issue or submit a pull request on GitHub. DaiSy licensed under the MIT License.

For questions and suggestions through mail, you can contact us at [e-mail].

The logo of DaiSy was designed by Eva Chamilaki.