  In the last twenty years, data series similarity search has emerged as a
fundamental operation at the core of several analysis tasks and applications
related to data series collections. Many solutions to different mining problems
work by means of similarity search. In this regard, all the proposed solutions
require the prior knowledge of the series length on which similarity search is
performed. In several cases, the choice of the length is critical and sensibly
influences the quality of the expected outcome. Unfortunately, the obvious
brute-force solution, which provides an outcome for all lengths within a given
range is computationally untenable. In this Ph.D. work, we present the first
solutions that inherently support scalable and variable-length similarity
search in data series, applied to sequence/subsequences matching, motif and
discord discovery problems.The experimental results show that our approaches
are up to orders of magnitude faster than the alternatives. They also
demonstrate that we can remove the unrealistic constraint of performing
analytics using a predefined length, leading to more intuitive and actionable
results, which would have otherwise been missed.
