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A Parallel Approach to Discords Discovery in Massive Time Series Data

Computers, Materials & Continua 2021年第2期66卷 1867-1878页

作者： mikhail zymbler Alexander Grents Yana Kraeva Sachin Kumar Department of Computer Science South Ural State UniversityChelyabinsk454080Russian

A discord is a refinement of the concept of an anomalous subsequence of a time *** one of the topical issues of time series mining,discords discovery is applied in a wide range of real-world areas(medicine,astronomy,economics,climate modeling,predictive maintenance,energy consumption,etc.).In this article,we propose a novel parallel algorithm for discords discovery on high-performance cluster with nodes based on many-core accelerators in the case when time series cannot fit in the main *** assumed that the time series is partitioned across the cluster nodes and achieved parallelization among the cluster nodes as well as within a single *** a cluster node,the algorithm employs a set of matrix data structures to store and index the subsequences of a time series,and to provide an efficient vectorization of computations on the *** each node,the algorithm processes its own partition and performs in two phases,namely candidate selection and discord refinement,with each phase requiring one linear scan through the *** the local discords found are combined into the global candidate set and transmitted to each cluster ***,a node performs refinement of the global candidate set over its own partition resulting in the local true discord ***,the global true discords set is constructed as intersection of the local true discord *** experimental evaluation on the real computer cluster with real and synthetic time series shows a high scalability of the proposed algorithm.

关键词： Time series discords discovery computer cluster many-core accelerator vectorization

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