  Data series similarity search is a core operation for several data series
analysis applications across many different domains. Nevertheless, even
state-of-the-art techniques cannot provide the time performance required for
large data series collections. We propose ParIS and ParIS+, the first
disk-based data series indices carefully designed to inherently take advantage
of multi-core architectures, in order to accelerate similarity search
processing times. Our experiments demonstrate that ParIS+ completely removes
the CPU latency during index construction for disk-resident data, and for exact
query answering is up to 1 order of magnitude faster than the current state of
the art index scan method, and up to 3 orders of magnitude faster than the
optimized serial scan method. ParIS+ (which is an evolution of the ADS+ index)
owes its efficiency to the effective use of multi-core and multi-socket
architectures, in order to distribute and execute in parallel both index
construction and query answering, and to the exploitation of the Single
Instruction Multiple Data (SIMD) capabilities of modern CPUs, in order to
further parallelize the execution of instructions inside each core.
