Fast data series indexing for in-memory data

Odyssey: A Journey in the Land of Distributed Data Series Similarity Search

“…This process outputs a hopefully small subset of the initial DS collection, containing series that need to be further examined. These series are often stored in (one or more) priority queues Peng et al [2020bPeng et al [ , 2021a. Multiple threads process, concurrently, the elements of the priority queues, calculating real distances (if needed), and updating the BSF each time a new minimum is met (see Figure 2).…”

“…However, the unprecedented growth in size that data series collections experience nowadays, renders even SotA parallel data series indexes inadequate Palpanas [2017], Echihabi et al [2018Echihabi et al [ , 2019, Palpanas and Beckmann [48(3], Bagnall et al [9(7], Gogolou et al [2019], , mainly due to the large number of random disk page reads required for exact query answering Echihabi et al [2018]. To address these issues, fast in-memory solutions have been proposed Peng et al [2020bPeng et al [ , 2021a. However, these solutions do not take advantage of distributed systems, and hence, are limited by the amount of memory of a single machine.…”

“…Supporting the components for efficient distributed computation that Odyssey provides, on top of an index that exploits the computation power of a single node as efficiently as SotA parallel indexes Peng et al [2020bPeng et al [ , 2021a, was one more challenging task we undertook while designing Odyssey. A simple approach of using an instance of the SotA MESSI index Peng et al [2021a] in each node did not result in good performance mainly due to two reasons.…”

“…Supporting the components for efficient distributed computation that Odyssey provides, on top of an index that exploits the computation power of a single node as efficiently as SotA parallel indexes Peng et al [2020bPeng et al [ , 2021a, was one more challenging task we undertook while designing Odyssey. A simple approach of using an instance of the SotA MESSI index Peng et al [2021a] in each node did not result in good performance mainly due to two reasons. First, different data series queries may exhibit variable degrees of locality (revealed only at runtime), resulting in low pruning in some of the nodes, and thus, in severe load balancing problems and performance degradation.…”

ProS: data series progressive k-NN similarity search and classification with probabilistic quality guarantees

“…We note that other lower bounds for DTW can be used as well, such as LB Improved[77]. Even though LB Improved can produce tighter bounds, previous experiments have resulted in higher query answering times due to the additional computations it involves[105].…”