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In the last twenty years, data series similarity search has emerged as afundamental operation at the core of several analysis tasks and applicationsrelated to data series collections. Many solutions to different mining problemswork by means of similarity search. In this regard, all the proposed solutionsrequire the prior knowledge of the series length on which similarity search isperformed. In several cases, the choice of the length is critical and sensiblyinfluences the quality of the expected outcome. Unfortunately, the obviousbrute-force solution, which provides an outcome for all lengths within a givenrange is computationally untenable. In this Ph.D. work, we present the firstsolutions that inherently support scalable and variable-length similaritysearch in data series, applied to sequence/subsequences matching, motif anddiscord discovery problems.The experimental results show that our approachesare up to orders of magnitude faster than the alternatives. They alsodemonstrate that we can remove the unrealistic constraint of performinganalytics using a predefined length, leading to more intuitive and actionableresults, which would have otherwise been missed.

Effective and Efficient Variable-Length Data Series Analytics