{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:13:54Z","timestamp":1779174834585,"version":"3.51.4"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T00:00:00Z","timestamp":1739404800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T00:00:00Z","timestamp":1739404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Sci. Eng."],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Time series data are pervasive, with a multitude of applications across various fields including science, industry, Entertainment, medicine, and biology. These data sets often encompass large volumes of information. In the context of databases containing time series data, the increasing frequency of query tasks, such as (i) point queries, (ii) range queries, and (iii) top-<jats:italic>k<\/jats:italic> similarity queries, necessitates the enhancement of query processing efficiency. The question thus arises: how can the efficiency of these data processing tasks be enhanced? The solution is twofold. Firstly, dimensionality reduction algorithms can be employed to reduce the complexity of the data. Secondly, we can focus on the optimization of query algorithms. Consequently, based on these ideas, in this paper, we present an optimized framework for efficiently executing time series queries in the openGauss database, called TSQ. The framework is composed of two primary components: a dimensionality reduction (DR) module and a similarity query (SQ) module. The DR module incorporates several algorithms based on space-filling curves, which, through our enhancements, are capable of handling both high and low precision time series data more effectively. In the context of the SQ module, we employ two main optimization strategies, Early Abandoning and Sliding Window, to greatly improve the efficiency of similarity queries. Experimental results on four real-world time series datasets show that our framework not only optimizes general query tasks but also significantly enhances the efficiency of similarity queries.<\/jats:p>","DOI":"10.1007\/s41019-024-00273-8","type":"journal-article","created":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T08:11:03Z","timestamp":1739434263000},"page":"296-313","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["TSQ: An Optimized Framework for Efficiently Answering Time Series Queries"],"prefix":"10.1007","volume":"10","author":[{"given":"Feifan","family":"Pu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0929-5234","authenticated-orcid":false,"given":"Jianqiu","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,13]]},"reference":[{"key":"273_CR1","doi-asserted-by":"publisher","unstructured":"Hao Y, Qin X, Chen Y, Li Y, Sun X, Tao Y, Du X (2021) Ts-benchmark: A benchmark for time series databases. In: 2021 IEEE International Conference on Data Engineering (ICDE), pp 588\u2013599. https:\/\/doi.org\/10.1109\/ICDE51399.2021.00057","DOI":"10.1109\/ICDE51399.2021.00057"},{"key":"273_CR2","doi-asserted-by":"publisher","unstructured":"Verleysen M, Fran\u00e7ois D (2005) The curse of dimensionality in data mining and time series prediction. In: 2005 International work-conference on artificial neural networks, pp 758\u2013770. https:\/\/doi.org\/10.1007\/11494669_93","DOI":"10.1007\/11494669_93"},{"key":"273_CR3","doi-asserted-by":"publisher","first-page":"42909","DOI":"10.1109\/ACCESS.2023.3269693","volume":"11","author":"M Ashraf","year":"2023","unstructured":"Ashraf M, Anowar F, Setu JH, Chowdhury AI, Ahmed E, Islam A, Al-Mamun A (2023) A survey on dimensionality reduction techniques for time-series data. IEEE Access 11:42909","journal-title":"IEEE Access"},{"key":"273_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74048-3_4","author":"M M\u00fcller","year":"2007","unstructured":"M\u00fcller M (2007) Dynamic time warping. Inf Retr Music Motion. https:\/\/doi.org\/10.1007\/978-3-540-74048-3_4","journal-title":"Inf Retr Music Motion"},{"key":"273_CR5","doi-asserted-by":"publisher","unstructured":"Bergroth L, Hakonen H, Raita T (2000) A survey of longest common subsequence algorithms. In: Proceedings Seventh International Symposium on String Processing and Information Retrieval, A Curuna, Spain, pp 39\u201348. https:\/\/doi.org\/10.1109\/SPIRE.2000.878178","DOI":"10.1109\/SPIRE.2000.878178"},{"issue":"4","key":"273_CR6","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1007\/s41019-022-00193-5","volume":"7","author":"CKJ Hou","year":"2022","unstructured":"Hou CKJ, Behdinan K (2022) Dimensionality reduction in surrogate modeling: a review of combined methods. Data Sci Eng 7(4):402\u2013427","journal-title":"Data Sci Eng"},{"issue":"4","key":"273_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3494565","volume":"16","author":"ML Zhang","year":"2022","unstructured":"Zhang ML, Wu JH, Bao WX (2022) Disambiguation enabled linear discriminant analysis for partial label dimensionality reduction. ACM Trans Knowl Discov Data 16(4):1\u201318","journal-title":"ACM Trans Knowl Discov Data"},{"key":"273_CR8","doi-asserted-by":"publisher","unstructured":"Dong H, Chen X, Dusmanu M, Larsson V, Pollefeys M, Stachniss C (2023) Learning-based dimensionality reduction for computing compact and effective local feature descriptors. In: Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, pp 6189\u20136195. https:\/\/doi.org\/10.1109\/ICRA48891.2023.10161381","DOI":"10.1109\/ICRA48891.2023.10161381"},{"issue":"6","key":"273_CR9","doi-asserted-by":"publisher","first-page":"7118","DOI":"10.1007\/s10489-022-03409-3","volume":"53","author":"S Su","year":"2023","unstructured":"Su S, Zhu G, Zhu Y, Ge B, Liang X (2023) Coupled locality discriminant analysis with globality preserving for dimensionality reduction. Appl Intell 53(6):7118\u20137131. https:\/\/doi.org\/10.1007\/s10489-022-03409-3","journal-title":"Appl Intell"},{"key":"273_CR10","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1016\/j.ins.2022.10.036","volume":"614","author":"J Wang","year":"2022","unstructured":"Wang J, Shao Z, Huang X, Lu T, Zhang R, Chen X (2022) Deep locally linear embedding network. Inf Sci 614:416\u2013431","journal-title":"Inf Sci"},{"key":"273_CR11","doi-asserted-by":"publisher","first-page":"103828","DOI":"10.1016\/j.engappai.2020.103828","volume":"95","author":"P Arena","year":"2020","unstructured":"Arena P, Patan\u00e8 L, Spinosa AG (2020) Robust modelling of binary decisions in laplacian eigenmaps-based echo state networks. Eng Appl Artif Intell 95:103828","journal-title":"Eng Appl Artif Intell"},{"issue":"3","key":"273_CR12","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1007\/s11390-016-1644-4","volume":"31","author":"T Lin","year":"2016","unstructured":"Lin T, Liu Y, Wang B, Wang L, Zha H (2016) Nonlinear dimensionality reduction by local orthogonality preserving alignment. J Comput Sci Technol 31(3):512\u2013524. https:\/\/doi.org\/10.1007\/s11390-016-1644-4","journal-title":"J Comput Sci Technol"},{"issue":"1","key":"273_CR13","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1109\/TKDE.2010.99","volume":"23","author":"X Zhu","year":"2010","unstructured":"Zhu X, Zhang S, Jin Z, Zhang Z, Xu Z (2010) Missing value estimation for mixed-attribute data sets. IEEE Trans Knowl Data Eng 23(1):110\u2013121. https:\/\/doi.org\/10.1109\/TKDE.2010.99","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"273_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42452-020-2870-5","volume":"2","author":"JPV Verma","year":"2020","unstructured":"Verma JPV, Mankad SH, Garg S (2020) GeoHash tag based mobility detection and prediction for trafficmanagement. SN Appl Sci 2:1\u201313. https:\/\/doi.org\/10.1007\/s42452-020-2870-5","journal-title":"SN Appl Sci"},{"key":"273_CR15","unstructured":"Bader M (2012) Space-filling curves: an introduction with applications in scientific computing (Vol. 9). Springer Science & Business Media"},{"key":"273_CR16","doi-asserted-by":"crossref","unstructured":"Li J, Wang Z, Cong G, Long C, Kiah H M, Cui B (2023) Towards Designing and Learning Piecewise Space-Filling Curves. In: Proceedings of the VLDB Endowment, 16(9): 2158\u20132171","DOI":"10.14778\/3598581.3598589"},{"key":"273_CR17","unstructured":"Morton G M (1966) A computer oriented geodetic data base and a new technique in file sequencing"},{"issue":"1","key":"273_CR18","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1007\/s42979-022-01320-9","volume":"4","author":"HK Dai","year":"2023","unstructured":"Dai HK, Su HC (2023) Clustering analyses of two-dimensional space-filling curves: hilbert and z-order curves. SN Comput Sci 4(1):8. https:\/\/doi.org\/10.1007\/s42979-022-01320-9","journal-title":"SN Comput Sci"},{"key":"273_CR19","unstructured":"Berchtold S, Keim D A, Kriegel H P (1996) The X-tree: An index structure for high-dimensional data. In: Proceedings of 22th International Conference on Very Large Data Bases, pp 28\u201339"},{"key":"273_CR20","doi-asserted-by":"publisher","unstructured":"Yue Y, Wang S. Kernel principal component analysis based on semi-supervised dimensionality reduction and its application on protein subnuclear localization. In: 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp 1\u20136. https:\/\/doi.org\/10.1109\/CISP-BMEI.2017.8302284","DOI":"10.1109\/CISP-BMEI.2017.8302284"},{"key":"273_CR21","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 searchin large time series databases. Knowl Inf Syst 3:263\u2013286. https:\/\/doi.org\/10.1007\/PL00011669","journal-title":"Knowl Inf Syst"},{"key":"273_CR22","doi-asserted-by":"publisher","unstructured":"Wu Y L, Agrawal D, El Abbadi A (2000) A comparison of DFT and DWT based similarity search in time series databases. In: Proceedings of the ninth international conference on Information and knowledge management, pp 488\u2013495. https:\/\/doi.org\/10.1145\/354756.354857","DOI":"10.1145\/354756.354857"},{"issue":"2","key":"273_CR23","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1145\/191843.191925","volume":"23","author":"C Faloutsos","year":"1994","unstructured":"Faloutsos C, Ranganathan M, Manolopoulos Y (1994) Fast subsequence matching in time-series databases. ACM SIGMOD Rec 23(2):419\u2013429. https:\/\/doi.org\/10.1145\/191843.191925","journal-title":"ACM SIGMOD Rec"},{"key":"273_CR24","unstructured":"Berndt D J, Clifford J (1994) Using dynamic time warping to find patterns in time series. In: Proceedings of the 3rd international conference on knowledge discovery and data mining, pp. 359\u2013370"},{"key":"273_CR25","doi-asserted-by":"crossref","unstructured":"Ding H, Trajcevski G, Scheuermann P, Wang X, Keogh E (2008) Querying and mining of time series data: experimental comparison of representations and distance measures. In: Proceedings of the VLDB Endowment 1(2): 1542\u20131552","DOI":"10.14778\/1454159.1454226"},{"key":"273_CR26","doi-asserted-by":"publisher","unstructured":"Keogh E J, Pazzani M J (2000) Scaling up dynamic time warping for datamining applications. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 285\u2013289. https:\/\/doi.org\/10.1145\/347090.347153","DOI":"10.1145\/347090.347153"},{"key":"273_CR27","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":"273_CR28","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s11704-018-7234-6","volume":"13","author":"Z Zhang","year":"2019","unstructured":"Zhang Z, Qi X, Wang Y et al (2019) Distributed top-k similarity query on big trajectory streams. Front Comput Sci 13:647\u2013664. https:\/\/doi.org\/10.1007\/s11704-018-7234-6","journal-title":"Front Comput Sci"},{"key":"273_CR29","doi-asserted-by":"crossref","unstructured":"Vlachos M, Kollios G, Gunopulos D (2002) Discovering similar multidimensional trajectories. In: Proceedings 18th international conference on data engineering, pp 673\u2013684","DOI":"10.1109\/ICDE.2002.994784"},{"key":"273_CR30","doi-asserted-by":"crossref","unstructured":"Chen L, \u00d6zsu M T, 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"},{"key":"273_CR31","doi-asserted-by":"crossref","unstructured":"Chen L, Ng R (2004) On the marriage of lp-norms and edit distance. In: Proceedings of the Thirtieth international conference on Very large data bases-Volume 30, pp 792-803","DOI":"10.1016\/B978-012088469-8\/50070-X"},{"key":"273_CR32","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1007\/s41019-023-00216-9","volume":"8","author":"Y Huang","year":"2023","unstructured":"Huang Y, Luo F, Wang X, Di Z, Li B, Luo B (2023) A one-size-fits-three representation learning framework for patient similarity search. Data Sci Eng 8:306\u2013317. https:\/\/doi.org\/10.1007\/s41019-023-00216-9","journal-title":"Data Sci Eng"},{"key":"273_CR33","doi-asserted-by":"crossref","unstructured":"Kieu T, Yang B, Guo C, Jensen C S, Zhao Y, Huang F, Zheng K (2022) Robust and explainable autoencoders for unsupervised time series outlier detection. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE), pp 3038\u20133050","DOI":"10.1109\/ICDE53745.2022.00273"},{"key":"273_CR34","doi-asserted-by":"publisher","first-page":"171601","DOI":"10.1007\/s11704-021-1080-7","volume":"17","author":"B Qiao","year":"2023","unstructured":"Qiao B, Wu Z, Ma L, Zhou Y, Sun Y (2023) Effective ensemble learning approach for SST field predictionusing attention-based PredRNN. Front Comput Sci 17:171601. https:\/\/doi.org\/10.1007\/s11704-021-1080-7","journal-title":"Front Comput Sci"},{"key":"273_CR35","doi-asserted-by":"crossref","unstructured":"Zerveas G, Jayaraman S, Patel D, Bhamidipaty A, Eickhoff C (2021) A transformer-based framework for multivariate time series representation learning. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp 2114\u20132124","DOI":"10.1145\/3447548.3467401"},{"key":"273_CR36","doi-asserted-by":"publisher","first-page":"105524","DOI":"10.1016\/j.asoc.2019.105524","volume":"97","author":"D Singh","year":"2020","unstructured":"Singh D, Singh B (2020) Investigating the impact of data normalization on classification performance. Appl Soft Comput 97:105524","journal-title":"Appl Soft Comput"},{"key":"273_CR37","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1186\/1687-6180-2012-181","volume":"2012","author":"P Costa","year":"2012","unstructured":"Costa P, Barroso J, Fernandes H et al (2012) Using Peano-Hilbert space filling curves for fast bidimensional ensemble EMD realization. EURASIP J Adv Signal Process 2012:181. https:\/\/doi.org\/10.1186\/1687-6180-2012-181","journal-title":"EURASIP J Adv Signal Process"}],"container-title":["Data Science and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41019-024-00273-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41019-024-00273-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41019-024-00273-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T08:57:51Z","timestamp":1749200271000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41019-024-00273-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,13]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["273"],"URL":"https:\/\/doi.org\/10.1007\/s41019-024-00273-8","relation":{},"ISSN":["2364-1185","2364-1541"],"issn-type":[{"value":"2364-1185","type":"print"},{"value":"2364-1541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,13]]},"assertion":[{"value":"28 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2025","order":4,"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 they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}