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We present CORE, a novel COmplex event Recognition Engine that focuses on the efficient evaluation of a large class of complex event queries, including time windows as well as the partition-by event correlation operator. This engine uses a novel automaton-based evaluation algorithm that circumvents the super-linear partial match problem: under data complexity, it takes constant time per input event to maintain a data structure that compactly represents the set of partial matches and, once a match is found, the query results may be enumerated from the data structure with output-linear delay. We experimentally compare CORE against state-of-the-art CER systems on real-world data. We show that (1) CORE's performance is stable with respect to both query and time window size, and (2) CORE outperforms the other systems by up to five orders of magnitude on different workloads.<\/jats:p>","DOI":"10.14778\/3538598.3538615","type":"journal-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T13:12:31Z","timestamp":1658927551000},"page":"1951-1964","source":"Crossref","is-referenced-by-count":14,"title":["CORE"],"prefix":"10.14778","volume":"15","author":[{"given":"Marco","family":"Bucchi","sequence":"first","affiliation":[{"name":"PUC Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alejandro","family":"Grez","sequence":"additional","affiliation":[{"name":"PUC Chile and IMFD Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9s","family":"Quintana","sequence":"additional","affiliation":[{"name":"PUC Chile and IMFD Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristian","family":"Riveros","sequence":"additional","affiliation":[{"name":"PUC Chile and IMFD Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stijn","family":"Vansummeren","sequence":"additional","affiliation":[{"name":"UHasselt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"[n.d.]. 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Esper Enterprise Edition website. http:\/\/www.espertech.com\/. Accessed on 2021-10-30.  [n.d.]. Esper Enterprise Edition website. http:\/\/www.espertech.com\/. Accessed on 2021-10-30."},{"key":"e_1_2_1_5_1","unstructured":"[n.d.]. FlinkCEP - Complex event processing for Flink. https:\/\/ci.apache.org\/projects\/flink\/flink-docs-release-1.13\/docs\/libs\/cep\/. Accessed on 2021-10-30.  [n.d.]. FlinkCEP - Complex event processing for Flink. https:\/\/ci.apache.org\/projects\/flink\/flink-docs-release-1.13\/docs\/libs\/cep\/. Accessed on 2021-10-30."},{"key":"e_1_2_1_6_1","unstructured":"[n.d.]. OpenCEP. https:\/\/research.redhat.com\/blog\/research_project\/complex-event-processing-2\/. Accessed on 2022-03-11.  [n.d.]. OpenCEP. https:\/\/research.redhat.com\/blog\/research_project\/complex-event-processing-2\/. Accessed on 2022-03-11."},{"key":"e_1_2_1_7_1","unstructured":"[n.d.]. Stock market data traces. https:\/\/davis.wpi.edu\/datasets\/Stock_Trace_Data\/. Accessed on 2022-03-11.  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