  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-core and
multi-socket 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. 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.
