{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:21:23Z","timestamp":1742912483987,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030602895"},{"type":"electronic","value":"9783030602901"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-60290-1_13","type":"book-chapter","created":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T21:02:30Z","timestamp":1602622950000},"page":"164-175","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Parallel Variable-Length Motif Discovery in Time Series Using Subsequences Correlation"],"prefix":"10.1007","author":[{"given":"Chuitian","family":"Rong","sequence":"first","affiliation":[]},{"given":"Lili","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Chunbin","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Yuan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,14]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1152\/physiolgenomics.00027.2010","volume":"42","author":"SM Bugenhagen","year":"2010","unstructured":"Bugenhagen, S.M., Cowley Jr., A.W., Beard, D.A.: Identifying physiological origins of baroreflex dysfunction in salt-sensitive hypertension in the Dahl SS rat. Physiol. Genomics 42, 23\u201341 (2010)","journal-title":"Physiol. Genomics"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Castro, N., Azevedo, P.J.: Multiresolution motif discovery in time series. In: SIAM, pp. 665\u2013676 (2010)","DOI":"10.1137\/1.9781611972801.73"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Gao, Y., Lin, J.: Efficient discovery of variable-length time series motifs with large length range in million scale time series. CoRR abs\/1802.04883 (2018)","DOI":"10.1109\/ICDM.2017.8356939"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Gao, Y., Lin, J., Rangwala, H.: Iterative grammar-based framework for discovering variable-length time series motifs. In: ICMLA, pp. 7\u201312 (2016)","DOI":"10.1109\/ICMLA.2016.0011"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Li, Y., U, L.H., Yiu, M.L., Gong, Z.: Quick-motif: an efficient and scalable framework for exact motif discovery. In: ICDE. pp. 579\u2013590 (2015)","DOI":"10.1109\/ICDE.2015.7113316"},{"key":"13_CR6","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s10618-007-0064-z","volume":"15","author":"J Lin","year":"2007","unstructured":"Lin, J., Keogh, E., Li, W., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15, 107\u2013144 (2007). https:\/\/doi.org\/10.1007\/s10618-007-0064-z","journal-title":"Data Min. Knowl. Discov."},{"key":"13_CR7","unstructured":"Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding motifs in time series. In: Proceedings of 2nd Workshop on Temporal Data Mining at KDD, pp. 53\u201368 (2002)"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Mueen, A., Hamooni, H., Estrada, T.: Time series join on subsequence correlation. In: ICDM, pp. 450\u2013459 (2014)","DOI":"10.1109\/ICDM.2014.52"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Mueen, A.: Enumeration of time series motifs of all lengths. In: ICDM, pp. 547\u2013556 (2013)","DOI":"10.1109\/ICDM.2013.27"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Mueen, A., Keogh, E.J., Zhu, Q., Cash, S., Westover, M.B.: Exact discovery of time series motifs. In: SIAM, pp. 473\u2013484 (2009)","DOI":"10.1137\/1.9781611972795.41"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Nunthanid, P., Niennattrakul, V., Ratanamahatana, C.A.: Discovery of variable length time series motif. In: EEE, pp. 472\u2013475 (2011)","DOI":"10.1109\/ECTICON.2011.5947877"},{"key":"13_CR12","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/s10994-008-5093-3","volume":"74","author":"U Rebbapragada","year":"2009","unstructured":"Rebbapragada, U., Protopapas, P., Brodley, C.E., Alcock, C.: Finding anomalous periodic time series. Mach. Learn. 74, 281\u2013313 (2009). https:\/\/doi.org\/10.1007\/s10994-008-5093-3","journal-title":"Mach. Learn."},{"issue":"9","key":"13_CR13","doi-asserted-by":"publisher","first-page":"e5622","DOI":"10.1002\/cpe.5622","volume":"32","author":"C Rong","year":"2020","unstructured":"Rong, C., Chen, L., Silva, Y.N.: Parallel time series join using spark. Concurr. Comput. Pract. Exp. 32(9), e5622 (2020)","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"13_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1007\/978-3-662-44845-8_37","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"P Senin","year":"2014","unstructured":"Senin, P., et al.: GrammarViz 2.0: a tool for grammar-based pattern discovery in time series. In: Calders, T., Esposito, F., H\u00fcllermeier, E., Meo, R. (eds.) ECML PKDD. LNCS, vol. 8726, pp. 468\u2013472. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-44845-8_37"},{"key":"13_CR15","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/s10994-005-5829-2","volume":"58","author":"Y Tanaka","year":"2005","unstructured":"Tanaka, Y., Iwamoto, K., Uehara, K.: Discovery of time-series motif from multi-dimensional data based on MDL principle. Mach. Learn. 58, 269\u2013300 (2005). https:\/\/doi.org\/10.1007\/s10994-005-5829-2","journal-title":"Mach. Learn."},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Yeh, C.C.M., Yan, Z., Ulanova, L., Begum, N., Keogh, E.: Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In: ICDM, pp. 1317\u20131322 (2016)","DOI":"10.1109\/ICDM.2016.0179"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Zimmerman, Z., Senobari, N.S., et al.: Matrix profile II: exploiting a novel algorithm and gpus to break the one hundred million barrier for time series motifs and joins. In: ICDM, pp. 739\u2013748 (2016)","DOI":"10.1109\/ICDM.2016.0085"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60290-1_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T12:12:57Z","timestamp":1619266377000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-60290-1_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030602895","9783030602901"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60290-1_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"14 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.tjudb.cn\/apwebwaim2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"259","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"68","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was organized as a fully online conference.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}