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Odyssey addresses a number of challenges in designing efficient and highly-scalable\n            <jats:italic>distributed<\/jats:italic>\n            data series index, including efficient scheduling, and load-balancing without paying the prohibitive cost of moving data around. It also supports a flexible partial replication scheme, which enables Odyssey to navigate through a fundamental trade-off between data scalability and good performance during query answering. Through a wide range of configurations and using several real and synthetic datasets, our experimental analysis demonstrates that Odyssey achieves its challenging goals.\n          <\/jats:p>","DOI":"10.14778\/3579075.3579087","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T17:10:26Z","timestamp":1678122626000},"page":"1140-1153","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Odyssey: A Journey in the Land of Distributed Data Series Similarity Search"],"prefix":"10.14778","volume":"16","author":[{"given":"Manos","family":"Chatzakis","sequence":"first","affiliation":[{"name":"EPFL"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Panagiota","family":"Fatourou","sequence":"additional","affiliation":[{"name":"FORTH, ICS &amp; University of Crete, CSD"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eleftherios","family":"Kosmas","sequence":"additional","affiliation":[{"name":"FORTH, ICS &amp; Hellenic Mediterranean University &amp; University of Crete, CSD"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Themis","family":"Palpanas","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris Cit\u00e9 &amp; IUF"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Botao","family":"Peng","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,3,6]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1006\/jpdc.1996.0107"},{"key":"e_1_2_1_2_1","unstructured":"2016. 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Yang Wang, Peng Wang, Jian Pei, Wei Wang, and Sheng Huang. 2013. A Data-adaptive and Dynamic Segmentation Index for Whole Matching on Time Series. PVLDB 6, 10 (2013)."},{"key":"e_1_2_1_70_1","volume-title":"Dumpy: A Compact and Adaptive Index for Large Data Series Collections. In SIGMOD.","author":"Wang Zeyu","year":"2023","unstructured":"Zeyu Wang , Qitong Wang , Peng Wang , Themis Palpanas , and Wei Wang . 2023 . Dumpy: A Compact and Adaptive Index for Large Data Series Collections. In SIGMOD. Zeyu Wang, Qitong Wang, Peng Wang, Themis Palpanas, and Wei Wang. 2023. Dumpy: A Compact and Adaptive Index for Large Data Series Collections. In SIGMOD."},{"key":"e_1_2_1_71_1","unstructured":"Warp10. 2018. Warp 10 - The Most Advanced Time Series Platform. https:\/\/www.warp10.io\/  Warp10. 2018. 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In ICDE."},{"key":"e_1_2_1_77_1","doi-asserted-by":"crossref","unstructured":"Kostas Zoumpatianos Stratos Idreos and Themis Palpanas. 2014. Indexing for interactive exploration of big data series. In SIGMOD.  Kostas Zoumpatianos Stratos Idreos and Themis Palpanas. 2014. Indexing for interactive exploration of big data series. In SIGMOD.","DOI":"10.1145\/2588555.2610498"},{"key":"e_1_2_1_78_1","doi-asserted-by":"crossref","unstructured":"Kostas Zoumpatianos Stratos Idreos and Themis Palpanas. 2016. ADS: the adaptive data series index. VLDB J. (2016).  Kostas Zoumpatianos Stratos Idreos and Themis Palpanas. 2016. ADS: the adaptive data series index. VLDB J. (2016).","DOI":"10.1007\/s00778-016-0442-5"},{"key":"e_1_2_1_79_1","doi-asserted-by":"crossref","unstructured":"Kostas Zoumpatianos Yin Lou Ioana Ileana Themis Palpanas and Johannes Gehrke. 2018. Generating data series query workloads. VLDB J. (2018).  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