{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:56:18Z","timestamp":1742957778362,"version":"3.40.3"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031320408"},{"type":"electronic","value":"9783031320415"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-32041-5_17","type":"book-chapter","created":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T21:56:29Z","timestamp":1683755789000},"page":"317-338","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Overcoming Weak Scaling Challenges in\u00a0Tree-Based Nearest Neighbor Time Series Mining"],"prefix":"10.1007","author":[{"given":"Amir","family":"Raoofy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roman","family":"Karlstetter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Schreiber","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carsten","family":"Trinitis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Schulz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,10]]},"reference":[{"issue":"6","key":"17_CR1","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1145\/293347.293348","volume":"45","author":"S Arya","year":"1998","unstructured":"Arya, S., et al.: An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J. ACM 45(6), 891\u2013923 (1998)","journal-title":"J. ACM"},{"issue":"6096","key":"17_CR2","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1038\/324446a0","volume":"324","author":"J Barnes","year":"1986","unstructured":"Barnes, J., Hut, P.: A hierarchical O(N log N) force-calculation algorithm. Nature 324(6096), 446\u2013449 (1986)","journal-title":"Nature"},{"key":"17_CR3","unstructured":"Cools, S., et al.: Improving strong scaling of the conjugate gradient method for solving large linear systems using global reduction pipelining. ArXiv abs\/1905.06850 (2019)"},{"key":"17_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/978-3-319-25087-8_7","volume-title":"Similarity Search and Applications","author":"RR Curtin","year":"2015","unstructured":"Curtin, R.R.: Faster dual-tree traversal for nearest neighbor search. In: Amato, G., Connor, R., Falchi, F., Gennaro, C. (eds.) SISAP 2015. LNCS, vol. 9371, pp. 77\u201389. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-25087-8_7"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Dau, H.A., Keogh, E.: Matrix profile V: a generic technique to incorporate domain knowledge into motif discovery. In: 23rd ACM SIGKDD, pp. 125\u2013134 (2017)","DOI":"10.1145\/3097983.3097993"},{"key":"17_CR6","unstructured":"Eamonn Keogh: Electrocardiography Dataset. https:\/\/www.cs.ucr.edu\/~eamonn\/ECG_one_day.zip. Accessed 15 Aug 2022"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Gharghabi, S., et al.: Domain agnostic online semantic segmentation for multi-dimensional time series. In: Data Mining and Knowledge Discovery (2018)","DOI":"10.1007\/s10618-018-0589-3"},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Heldens, S., et al.: Rocket: efficient and scalable all-pairs computations on heterogeneous platforms. In: Proceedings of SC 2020. IEEE Press (2020)","DOI":"10.1109\/SC41405.2020.00105"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Jirkovsk\u00fd, V., et al.: Big data analysis for sensor time-series in automation. In: IEEE Emerging Technology and Factory Automation (ETFA), pp. 1\u20138 (2014)","DOI":"10.1109\/ETFA.2014.7005183"},{"issue":"38","key":"17_CR10","doi-asserted-by":"publisher","first-page":"15679","DOI":"10.1073\/pnas.1107769108","volume":"108","author":"PW Jones","year":"2011","unstructured":"Jones, P.W., et al.: Randomized approximate nearest neighbors algorithm. Proc. Natl. Acad. Sci. 108(38), 15679\u201315686 (2011)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Ju, Y., et al.: Exploiting reduced precision for GPU-based Time series mining. In: IEEE IPDPS, pp. 124\u2013134 (2022)","DOI":"10.1109\/IPDPS53621.2022.00021"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Karlstetter, R., et al.: Turning dynamic sensor measurements from gas turbines into insights: a big data approach. In: Turbo Expo, vol. 6 (2019)","DOI":"10.1115\/GT2019-91259"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Karlstetter, R., et al.: Living on the edge: efficient handling of large scale sensor data. In: 2021 IEEE\/ACM CCGrid 2021, pp. 1\u201310 (2021)","DOI":"10.1109\/CCGrid51090.2021.00010"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Linardi, M., et al.: Matrix profile X: VALMOD - scalable discovery of variable-length motifs in data series. In: ACM SIGMOD, p. 1053\u20131066 (2018)","DOI":"10.1145\/3183713.3183744"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Lu, Y., et al.: Matrix profile XXIV: scaling time series anomaly detection to trillions of datapoints and ultra-fast arriving data streams. In: ACM SIGKDD (2022)","DOI":"10.1145\/3534678.3539271"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Mercer, R., et al.: Matrix profile XXIII: contrast profile: a novel time series primitive that allows real world classification. In: IEEE ICDM (2021)","DOI":"10.1109\/ICDM51629.2021.00151"},{"issue":"11","key":"17_CR17","doi-asserted-by":"publisher","first-page":"2227","DOI":"10.1109\/TPAMI.2014.2321376","volume":"36","author":"M Muja","year":"2014","unstructured":"Muja, M., Lowe, D.G.: Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2227\u20132240 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR18","doi-asserted-by":"publisher","unstructured":"Netti, A.: HPC-ODA dataset collection (2020). https:\/\/doi.org\/10.5281\/zenodo.3701440","DOI":"10.5281\/zenodo.3701440"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Patwary, M.M.A., et al.: PANDA: extreme scale parallel k-nearest neighbor on distributed architectures. CoRR abs\/1607.08220 (2016)","DOI":"10.1109\/IPDPS.2016.57"},{"key":"17_CR20","unstructured":"Pfeilschifter, G.: time series analysis with matrix profile on HPC systems. Master thesis, Technische Universit\u00e4t M\u00fcnchen (2019)"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Raksha, S., et al.: Weather forecasting framework for time series data using intelligent learning models. In: 5th ICEECCOT 2021, pp. 783\u2013787 (2021)","DOI":"10.1109\/ICEECCOT52851.2021.9707971"},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"Rakthanmanon, T., et al.: Searching and mining trillions of time series subsequences under dynamic time warping. In: ACM SIGKDD, pp. 262\u2013270 (2012)","DOI":"10.1145\/2339530.2339576"},{"key":"17_CR23","doi-asserted-by":"crossref","unstructured":"Ram, P., Sinha, K.: Revisiting KD-tree for nearest neighbor search. In: KDD 2019, pp. 1378\u20131388. Association for Computing Machinery, New York (2019)","DOI":"10.1145\/3292500.3330875"},{"key":"17_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1007\/978-3-030-50743-5_6","volume-title":"High Performance Computing","author":"A Raoofy","year":"2020","unstructured":"Raoofy, A., Karlstetter, R., Yang, D., Trinitis, C., Schulz, M.: Time series mining at petascale performance. In: Sadayappan, P., Chamberlain, B.L., Juckeland, G., Ltaief, H. (eds.) ISC High Performance 2020. LNCS, vol. 12151, pp. 104\u2013123. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-50743-5_6"},{"issue":"2","key":"17_CR25","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/0021-9991(85)90002-6","volume":"60","author":"V Rokhlin","year":"1985","unstructured":"Rokhlin, V.: Rapid solution of integral equations of classical potential theory. J. Comput. Phys. 60(2), 187\u2013207 (1985)","journal-title":"J. Comput. Phys."},{"key":"17_CR26","doi-asserted-by":"crossref","unstructured":"Schall-Zimmerman, Z., et al.: Matrix profile XVIII: time series mining in the face of fast moving streams using a learned approximate matrix profile. In: IEEE ICDM, pp. 936\u2013945 (2019)","DOI":"10.1109\/ICDM.2019.00104"},{"issue":"9","key":"17_CR27","doi-asserted-by":"publisher","first-page":"1779","DOI":"10.14778\/3538598.3538602","volume":"15","author":"S Schmidl","year":"2022","unstructured":"Schmidl, S., et al.: Anomaly detection in time series: a comprehensive evaluation. Proc. VLDB Endow. 15(9), 1779\u20131797 (2022)","journal-title":"Proc. VLDB Endow."},{"key":"17_CR28","unstructured":"Shakibay Senobari, et al.: Using the similarity matrix profile to investigate foreshock behavior of the 2004 parkfield earthquake. In: AGU Fall Meeting Abstracts, vol. 2018, pp. S51B\u201303 (2018)"},{"key":"17_CR29","unstructured":"Steinbusch, B., et al.: A massively parallel barnes-hut tree code with dual tree traversal. In: PARCO (2015)"},{"key":"17_CR30","doi-asserted-by":"publisher","unstructured":"Thill, M., et al.: MarkusThill\/MGAB: The Mackey-glass anomaly benchmark (2020). https:\/\/doi.org\/10.5281\/zenodo.3760086","DOI":"10.5281\/zenodo.3760086"},{"issue":"1","key":"17_CR31","first-page":"3221","volume":"15","author":"L Van Der Maaten","year":"2014","unstructured":"Van Der Maaten, L.: Accelerating T-SNE using tree-based algorithms. J. Mach. Learn. Res. 15(1), 3221\u20133245 (2014)","journal-title":"J. Mach. Learn. Res."},{"issue":"5","key":"17_CR32","doi-asserted-by":"publisher","first-page":"S667","DOI":"10.1137\/15M1026377","volume":"38","author":"B Xiao","year":"2016","unstructured":"Xiao, B., Biros, G.: Parallel algorithms for nearest neighbor search problems in high dimensions. SIAM J. Sci. Comput. 38(5), S667\u2013S699 (2016)","journal-title":"SIAM J. Sci. Comput."},{"key":"17_CR33","doi-asserted-by":"crossref","unstructured":"Yeh, C.M., et al.: Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In: IEEE ICDM, pp. 1317\u20131322 (2016)","DOI":"10.1109\/ICDM.2016.0179"},{"key":"17_CR34","doi-asserted-by":"crossref","unstructured":"Yeh, C.M., et al.: Matrix profile III: the matrix profile allows visualization of salient subsequences in massive time series. In: IEEE ICDM, pp. 579\u2013588 (2016)","DOI":"10.1109\/ICDM.2016.0069"},{"key":"17_CR35","doi-asserted-by":"crossref","unstructured":"Yu, C.D., et al.: Performance optimization for the K-nearest neighbors kernel on X86 architectures. In: ACM SC (2015)","DOI":"10.1145\/2807591.2807601"},{"key":"17_CR36","doi-asserted-by":"publisher","unstructured":"Zheng, X., et al.: PSML: a multi-scale time-series dataset for machine learning in decarbonized energy grids (dataset) (2021). https:\/\/doi.org\/10.5281\/zenodo.5130612","DOI":"10.5281\/zenodo.5130612"},{"key":"17_CR37","doi-asserted-by":"crossref","unstructured":"Zhu, Y., et al.: Matrix profile XI: SCRIMP++: time series motif discovery at interactive speeds. In: IEEE ICDM, pp. 837\u2013846 (2018)","DOI":"10.1109\/ICDM.2018.00099"},{"key":"17_CR38","doi-asserted-by":"crossref","unstructured":"Zhu, Y., et al.: Matrix profile VII: time series chains: a new primitive for time series data mining. In: 2017 IEEE ICDM 2017, pp. 695\u2013704 (2017)","DOI":"10.1109\/ICDM.2017.79"},{"key":"17_CR39","doi-asserted-by":"crossref","unstructured":"Zhu, Y., et al.: Matrix profile II: exploiting a novel algorithm and GPUs to break the one hundred million barrier for time series motifs and joins. Knowl. Inf. Syst. 54(1) (2018)","DOI":"10.1007\/s10115-017-1138-x"},{"key":"17_CR40","doi-asserted-by":"crossref","unstructured":"Zhu, Y., et al.: The swiss army knife of time series data mining: ten useful things you can do with the matrix profile and ten lines of code. In: KDD 2020, vol. 34, pp. 949\u2013979 (2020)","DOI":"10.1007\/s10618-019-00668-6"},{"key":"17_CR41","doi-asserted-by":"crossref","unstructured":"Zimmerman, Z., et al.: Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond. In: ACM SoCC, pp. 74\u201386 (2019)","DOI":"10.1145\/3357223.3362721"}],"container-title":["Lecture Notes in Computer Science","High Performance Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-32041-5_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T22:02:10Z","timestamp":1683756130000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-32041-5_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031320408","9783031320415"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-32041-5_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"10 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISC High Performance","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on High Performance Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hamburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 May 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 May 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"38","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"supercomputing2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.isc-hpc.com\/","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":"Linklings","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"78","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":"21","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":"0","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":"27% - 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":"3.74","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":"4.49","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)"}}]}}