  Data series similarity search is an important operation and at the core of
several analysis tasks and applications related to data series collections.
Despite the fact that data series indexes enable fast similarity search, all
existing indexes can only answer queries of a single length (fixed at index
construction time), which is a severe limitation. In this work, we propose
ULISSE, the first data series index structure designed for answering similarity
search queries of variable length (within some range). Our contribution is
two-fold. First, we introduce a novel representation technique, which
effectively and succinctly summarizes multiple sequences of different length.
Based on the proposed index, we describe efficient algorithms for approximate
and exact similarity search, combining disk based index visits and in-memory
sequential scans. Our approach supports non Z-normalized and Z-normalized
sequences, and can be used with no changes with both Euclidean Distance and
Dynamic Time Warping, for answering both k-NN and epsilon-range queries. We
experimentally evaluate our approach using several synthetic and real datasets.
The results show that ULISSE is several times, and up to orders of magnitude
more efficient in terms of both space and time cost, when compared to competing
approaches. (Paper published in VLDBJ 2020)
