Fast Data Series Indexing for In-Memory Data

TL;DR

MESSI is a novel in-memory data series index that leverages modern hardware parallelization to enable real-time similarity search on large datasets, significantly outperforming previous methods.

Contribution

It introduces MESSI, the first in-memory index for data series that utilizes SIMD and multi-core architectures for fast similarity search.

Findings

Up to 4x faster index construction

Up to 11x faster query answering

Enables real-time similarity search on 100GB datasets

Abstract

Data series similarity search is a core operation for several data series analysis applications across many different domains. However, the state-of-the-art techniques fail to deliver the time performance required for interactive exploration, or analysis of large data series collections. In this work, we propose MESSI, the first data series index designed for in-memory operation on modern hardware. Our index takes advantage of the modern hardware parallelization opportunities (i.e., SIMD instructions, multi-socket and multi-core architectures), in order to accelerate both index construction and similarity search processing times. Moreover, it benefits from a careful design in the setup and coordination of the parallel workers and data structures, so that it maximizes its performance for in-memory operations. MESSI supports similarity search using both the Euclidean and Dynamic Time Warping (DTW) distances. Our experiments with synthetic and real datasets demonstrate that overall MESSI is up to 4x faster at index construction, and up to 11x faster at query answering than the state-of-the-art parallel approach. MESSI is the first to answer exact similarity search queries on 100GB datasets in ~50msec (30-75msec across diverse datasets), which enables real-time, interactive data exploration on very large data series collections.