{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T12:59:42Z","timestamp":1760014782987,"version":"3.37.3"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Science Foundation of Zhejiang Sci-Tech University","award":["22232264-Y"],"award-info":[{"award-number":["22232264-Y"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00521-023-09291-5","type":"journal-article","created":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T15:02:21Z","timestamp":1701529341000},"page":"3389-3403","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Speeding up pattern matching in streaming time-series via block vector and multilevel lower bound"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8946-1273","authenticated-orcid":false,"given":"Haowen","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,2]]},"reference":[{"issue":"4","key":"9291_CR1","doi-asserted-by":"publisher","first-page":"2923","DOI":"10.1109\/COMST.2018.2844341","volume":"20","author":"M Mohammadi","year":"2018","unstructured":"Mohammadi M, Al-Fuqaha A, Sorour S, Guizani M (2018) Deep learning for IoT big data and streaming analytics: a survey. IEEE Commun Surv Tutor 20(4):2923\u20132960","journal-title":"IEEE Commun Surv Tutor"},{"issue":"2","key":"9291_CR2","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1145\/3373464.3373470","volume":"21","author":"HM Gomes","year":"2019","unstructured":"Gomes HM, Read J, Bifet A, Barddal JP, Gama J (2019) Machine learning for streaming data: state of the art, challenges, and opportunities. ACM SIGKDD Explor Newsl 21(2):6\u201322","journal-title":"ACM SIGKDD Explor Newsl"},{"issue":"5","key":"9291_CR3","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1007\/s11222-016-9684-8","volume":"27","author":"DA Bodenham","year":"2017","unstructured":"Bodenham DA, Adams NM (2017) Continuous monitoring for changepoints in data streams using adaptive estimation. Stat Comput 27(5):1257\u20131270","journal-title":"Stat Comput"},{"key":"9291_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.envc.2021.100328","volume":"5","author":"B Butler","year":"2021","unstructured":"Butler B, Pearson RG, Birtles RA (2021) Water-quality and ecosystem impacts of recreation in streams: monitoring and management. Environ Chall 5:100328","journal-title":"Environ Chall"},{"issue":"1","key":"9291_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41688-020-00041-3","volume":"4","author":"S Henning","year":"2020","unstructured":"Henning S, Hasselbring W (2020) Scalable and reliable multi-dimensional sensor data aggregation in data streaming architectures. Data-Enabled Discov Appl 4(1):1\u201312","journal-title":"Data-Enabled Discov Appl"},{"key":"9291_CR6","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.ins.2017.11.012","volume":"430","author":"H Lin","year":"2018","unstructured":"Lin H, Wu S, Kou NM, Gao Y, Lu D et al (2018) Finding the hottest item in data streams. Inf Sci 430:314\u2013330","journal-title":"Inf Sci"},{"issue":"1","key":"9291_CR7","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.ins.2011.09.004","volume":"183","author":"L Chen","year":"2012","unstructured":"Chen L, Zou L-J, Tu L (2012) A clustering algorithm for multiple data streams based on spectral component similarity. Inf Sci 183(1):35\u201347","journal-title":"Inf Sci"},{"key":"9291_CR8","doi-asserted-by":"crossref","unstructured":"Wu J, Wang P, Pan N, Wang C, Wang W, Wang J (2019) Kv-match: a subsequence matching approach supporting normalization and time warping. In: 2019 IEEE 35th international conference on data engineering (ICDE), pp 866\u2013877. IEEE","DOI":"10.1109\/ICDE.2019.00082"},{"key":"9291_CR9","doi-asserted-by":"crossref","unstructured":"Alghamdi N, Zhang L, Zhang H, Rundensteiner EA, Eltabakh MY (2020) Chainlink: indexing big time series data for long subsequence matching. In: 2020 IEEE 36th international conference on data engineering (ICDE), pp 529\u2013540. IEEE","DOI":"10.1109\/ICDE48307.2020.00052"},{"key":"9291_CR10","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.eswa.2018.09.011","volume":"116","author":"X Gong","year":"2019","unstructured":"Gong X, Fong S, Si Y-W (2019) Fast fuzzy subsequence matching algorithms on time-series. Expert Syst Appl 116:275\u2013284","journal-title":"Expert Syst Appl"},{"key":"9291_CR11","doi-asserted-by":"crossref","unstructured":"Peng B, Fatourou P, Palpanas T (2021) Fast data series indexing for in-memory data. VLDB J, 1\u201327","DOI":"10.1109\/TKDE.2020.2975180"},{"issue":"6","key":"9291_CR12","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1007\/s00778-020-00619-4","volume":"29","author":"M Linardi","year":"2020","unstructured":"Linardi M, Palpanas T (2020) Scalable data series subsequence matching with ULISSE. VLDB J 29(6):1449\u20131474","journal-title":"VLDB J"},{"issue":"4","key":"9291_CR13","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1109\/TKDE.2008.184","volume":"21","author":"X Lian","year":"2008","unstructured":"Lian X, Chen L, Yu JX, Han J, Ma J (2008) Multiscale representations for fast pattern matching in stream time series. IEEE Trans Knowl Data Eng 21(4):568\u2013581","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"5","key":"9291_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1409060.1409079","volume":"27","author":"K Zhou","year":"2008","unstructured":"Zhou K, Hou Q, Wang R, Guo B (2008) Real-time kd-tree construction on graphics hardware. ACM Trans Graph (TOG) 27(5):1\u201311","journal-title":"ACM Trans Graph (TOG)"},{"key":"9291_CR15","unstructured":"Ciaccia P, Patella M, Zezula P (1997) M-tree: an efficient access method for similarity search in metric spaces. In: Vldb, vol 97, pp 426\u2013435. Citeseer"},{"key":"9291_CR16","doi-asserted-by":"crossref","unstructured":"Beygelzimer A, Kakade S, Langford J (2006) Cover trees for nearest neighbor. In: Proceedings of the 23rd international conference on machine learning, pp 97\u2013104","DOI":"10.1145\/1143844.1143857"},{"key":"9291_CR17","doi-asserted-by":"crossref","unstructured":"Guttman A (1984) R-trees: a dynamic index structure for spatial searching. In: Proceedings of the 1984 ACM SIGMOD international conference on management of data, pp 47\u201357","DOI":"10.1145\/971697.602266"},{"issue":"1","key":"9291_CR18","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/TKDE.2015.2460735","volume":"28","author":"AM Almalawi","year":"2015","unstructured":"Almalawi AM, Fahad A, Tari Z, Cheema MA, Khalil I (2015) $$k$$ NNVWC: an efficient $$k$$-nearest neighbors approach based on various-widths clustering. IEEE Trans Knowl Data Eng 28(1):68\u201381","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9291_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105088","volume":"189","author":"Y Pan","year":"2020","unstructured":"Pan Y, Pan Z, Wang Y, Wang W (2020) A new fast search algorithm for exact k-nearest neighbors based on optimal triangle-inequality-based check strategy. Knowl-Based Syst 189:105088","journal-title":"Knowl-Based Syst"},{"key":"9291_CR20","doi-asserted-by":"crossref","unstructured":"Wang X (2011) A fast exact k-nearest neighbors algorithm for high dimensional search using k-means clustering and triangle inequality. In: The 2011 international joint conference on neural networks, pp 1293\u20131299. IEEE","DOI":"10.1109\/IJCNN.2011.6033373"},{"key":"9291_CR21","doi-asserted-by":"crossref","unstructured":"Camerra A, Palpanas T, Shieh J, Keogh E (2010) isax 2.0: Indexing and mining one billion time series. In: 2010 IEEE international conference on data mining, pp 58\u201367. IEEE","DOI":"10.1109\/ICDM.2010.124"},{"issue":"5","key":"9291_CR22","first-page":"2151","volume":"33","author":"B Peng","year":"2020","unstructured":"Peng B, Fatourou P, Palpanas T (2020) Paris+: data series indexing on multi-core architectures. IEEE Trans Knowl Data Eng 33(5):2151\u20132164","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"10","key":"9291_CR23","doi-asserted-by":"publisher","first-page":"793","DOI":"10.14778\/2536206.2536208","volume":"6","author":"Y Wang","year":"2013","unstructured":"Wang Y, Wang P, Pei J, Wang W, Huang S (2013) A data-adaptive and dynamic segmentation index for whole matching on time series. Proc VLDB Endow 6(10):793\u2013804","journal-title":"Proc VLDB Endow"},{"issue":"6","key":"9291_CR24","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1007\/s00778-016-0442-5","volume":"25","author":"K Zoumpatianos","year":"2016","unstructured":"Zoumpatianos K, Idreos S, Palpanas T (2016) ADS: the adaptive data series index. VLDB J 25(6):843\u2013866","journal-title":"VLDB J"},{"key":"9291_CR25","doi-asserted-by":"crossref","unstructured":"Shieh J, Keogh E (2008) $$i$$SAX: indexing and mining terabyte sized time series. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, pp 623\u2013631","DOI":"10.1145\/1401890.1401966"},{"key":"9291_CR26","doi-asserted-by":"crossref","unstructured":"Keogh E, Chakrabarti K, Pazzani M, Mehrotra S (2001) Locally adaptive dimensionality reduction for indexing large time series databases. In: Proceedings of the 2001 ACM SIGMOD international conference on management of data, pp 151\u2013162","DOI":"10.1145\/375663.375680"},{"key":"9291_CR27","doi-asserted-by":"crossref","unstructured":"Peng J, Wang H, Li J, Gao H (2016) Set-based similarity search for time series. In: Proceedings of the 2016 international conference on management of data, pp 2039\u20132052","DOI":"10.1145\/2882903.2882963"},{"issue":"2","key":"9291_CR28","doi-asserted-by":"publisher","first-page":"439","DOI":"10.3233\/IDA-194876","volume":"25","author":"H Zhang","year":"2021","unstructured":"Zhang H, Dong Y, Li J, Xu D (2021) An efficient method for time series similarity search using binary code representation and hamming distance. Intell Data Anal 25(2):439\u2013461","journal-title":"Intell Data Anal"},{"issue":"2","key":"9291_CR29","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1007\/s10115-018-1264-0","volume":"60","author":"Y Ye","year":"2019","unstructured":"Ye Y, Jiang J, Ge B, Dou Y, Yang K (2019) Similarity measures for time series data classification using grid representation and matrix distance. Knowl Inf Syst 60(2):1105\u20131134","journal-title":"Knowl Inf Syst"},{"key":"9291_CR30","doi-asserted-by":"crossref","unstructured":"Hwang Y, Baek M, Kim S, Han B, Ahn H-K (2018) Product quantized translation for fast nearest neighbor search. In: Proceedings of the AAAI conference on artificial intelligence, vol 32","DOI":"10.1609\/aaai.v32i1.11752"},{"key":"9291_CR31","doi-asserted-by":"crossref","unstructured":"Hwang Y, Han B, Ahn H-K (2012) A fast nearest neighbor search algorithm by nonlinear embedding. In: 2012 IEEE conference on computer vision and pattern recognition, pp 3053\u20133060. IEEE","DOI":"10.1109\/CVPR.2012.6248036"},{"key":"9291_CR32","doi-asserted-by":"crossref","unstructured":"Jeong S, Kim S-W, Kim K, Choi B-U (2006) An effective method for approximating the euclidean distance in high-dimensional space. In: International conference on database and expert systems applications, pp 863\u2013872. Springer","DOI":"10.1007\/11827405_84"},{"key":"9291_CR33","doi-asserted-by":"crossref","unstructured":"Li M, Zhang Y, Sun Y, Wang W, Tsang IW, Lin X (2018) An efficient exact nearest neighbor search by compounded embedding. In: International conference on database systems for advanced applications, pp 37\u201354. Springer","DOI":"10.1007\/978-3-319-91452-7_3"},{"key":"9291_CR34","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.ins.2018.07.005","volume":"465","author":"Y Liu","year":"2018","unstructured":"Liu Y, Wei H, Cheng H (2018) Exploiting lower bounds to accelerate approximate nearest neighbor search on high-dimensional data. Inf Sci 465:484\u2013504","journal-title":"Inf Sci"},{"key":"9291_CR35","unstructured":"Bottesch T, B\u00fchler T, K\u00e4chele M (2016) Speeding up k-means by approximating Euclidean distances via block vectors. In: International conference on machine learning, pp 2578\u20132586. PMLR"},{"key":"9291_CR36","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1002\/int.22692","volume":"37","author":"H Zhang","year":"2021","unstructured":"Zhang H, Dong Y, Xu D (2021) Accelerating exact nearest neighbor search in high dimensional Euclidean space via block vectors. Int J Intell Syst 37:1697\u20131722","journal-title":"Int J Intell Syst"},{"issue":"1","key":"9291_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2379776.2379788","volume":"45","author":"P Esling","year":"2012","unstructured":"Esling P, Agon C (2012) Time-series data mining. ACM Comput Surv (CSUR) 45(1):1\u201334","journal-title":"ACM Comput Surv (CSUR)"},{"key":"9291_CR38","unstructured":"Berndt DJ, Clifford J (1996) Finding patterns in time series: a dynamic programming approach. In: Advances in knowledge discovery and data mining, pp 229\u2013248"},{"key":"9291_CR39","doi-asserted-by":"crossref","unstructured":"Chen L, \u00d6zsu MT, Oria V (2005) Robust and fast similarity search for moving object trajectories. In: Proceedings of the 2005 ACM SIGMOD international conference on management of data, pp 491\u2013502","DOI":"10.1145\/1066157.1066213"},{"issue":"2","key":"9291_CR40","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1109\/TPAMI.2008.76","volume":"31","author":"P-F Marteau","year":"2008","unstructured":"Marteau P-F (2008) Time warp edit distance with stiffness adjustment for time series matching. IEEE Trans Pattern Anal Mach Intell 31(2):306\u2013318","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"9291_CR41","doi-asserted-by":"publisher","first-page":"1425","DOI":"10.1109\/TKDE.2012.88","volume":"25","author":"A Stefan","year":"2012","unstructured":"Stefan A, Athitsos V, Das G (2012) The move-split-merge metric for time series. IEEE Trans Knowl Data Eng 25(6):1425\u20131438","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9291_CR42","doi-asserted-by":"crossref","unstructured":"Rakthanmanon T, Campana B, Mueen A, Batista G, Westover B, Zhu Q, Zakaria J, Keogh E (2012) Searching and mining trillions of time series subsequences under dynamic time warping. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, pp 262\u2013270","DOI":"10.1145\/2339530.2339576"},{"key":"9291_CR43","unstructured":"Kim S-W, Park S, Chu WW (2001) An index-based approach for similarity search supporting time warping in large sequence databases. In: Proceedings 17th international conference on data engineering, pp 607\u2013614. IEEE"},{"issue":"3","key":"9291_CR44","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1007\/s10115-004-0154-9","volume":"7","author":"E Keogh","year":"2005","unstructured":"Keogh E, Ratanamahatana CA (2005) Exact indexing of dynamic time warping. Knowl Inf Syst 7(3):358\u2013386","journal-title":"Knowl Inf Syst"},{"key":"9291_CR45","unstructured":"Yi B-K, Faloutsos C (2000) Fast time sequence indexing for arbitrary Lp norms"},{"issue":"3","key":"9291_CR46","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/PL00011669","volume":"3","author":"E Keogh","year":"2001","unstructured":"Keogh E, Chakrabarti K, Pazzani M, Mehrotra S (2001) Dimensionality reduction for fast similarity search in large time series databases. Knowl Inf Syst 3(3):263\u2013286","journal-title":"Knowl Inf Syst"},{"key":"9291_CR47","doi-asserted-by":"crossref","unstructured":"Dau HA, Keogh E, Kamgar K, Yeh C-CM, Zhu Y, Gharghabi S, Ratanamahatana CA, Yanping, Hu B, Begum N, Bagnall A, Mueen A, Batista G, Hexagon-ML (2018) The UCR time series classification archive. https:\/\/www.cs.ucr.edu\/~eamonn\/time_series_data_2018\/","DOI":"10.1109\/JAS.2019.1911747"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09291-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09291-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09291-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T19:45:37Z","timestamp":1730749537000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09291-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,2]]},"references-count":47,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["9291"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09291-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,12,2]]},"assertion":[{"value":"7 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there are no conflicts of interest regarding the publication of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}