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All these applications require the detection to find<jats:italic>all<\/jats:italic>sequential anomalies possibly<jats:italic>fast<\/jats:italic>on potentially very<jats:italic>large<\/jats:italic>time series. In other words, the detection needs to be effective, efficient and scalable w.r.t. the input size. Series2Graph is an effective solution based on graph embeddings that are robust against re-occurring anomalies and can discover sequential anomalies of arbitrary length and works without training data. Yet, Series2Graph is no t scalable due to its single-threaded approach; it cannot, in particular, process arbitrarily large sequences due to the memory constraints of a single machine. In this paper, we propose our distributed anomaly detection system, short DADS, which is an efficient and scalable adaptation of Series2Graph. Based on the actor programming model, DADS distributes the input time sequence, intermediate state and the computation to all processors of a cluster in a way that minimizes communication costs and synchronization barriers. Our evaluation shows that DADS is orders of magnitude faster than S2G, scales almost linearly with the number of processors in the cluster and can process much larger input sequences due to its scale-out property.<\/jats:p>","DOI":"10.1007\/s00778-021-00657-6","type":"journal-article","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T12:00:57Z","timestamp":1616673657000},"page":"579-602","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Distributed detection of sequential anomalies in univariate time series"],"prefix":"10.1007","volume":"30","author":[{"given":"Johannes","family":"Schneider","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8942-4322","authenticated-orcid":false,"given":"Phillip","family":"Wenig","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4019-8221","authenticated-orcid":false,"given":"Thorsten","family":"Papenbrock","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,25]]},"reference":[{"key":"657_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/1475921710395811","volume":"2012","author":"Ali Abdul-Aziz","year":"2012","unstructured":"Abdul-Aziz, Ali, Woike, Mark R., Oza, Nikunj C., Matthews, Bryan L., lekki, John D.: Rotor health monitoring combining spin tests and data-driven anomaly detection methods. 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