{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T01:03:48Z","timestamp":1759971828748,"version":"build-2065373602"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Natural Science Foundation of Fujian Province of China","award":["2020J05146"],"award-info":[{"award-number":["2020J05146"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62006096"],"award-info":[{"award-number":["62006096"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100016808","name":"Natural Science Foundation of Xiamen Municipality","doi-asserted-by":"publisher","award":["3502Z202373035"],"award-info":[{"award-number":["3502Z202373035"]}],"id":[{"id":"10.13039\/100016808","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Fujian Province of China","award":["2024J01117"],"award-info":[{"award-number":["2024J01117"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s10586-025-05420-y","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T12:24:19Z","timestamp":1757593459000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An efficient distance based outlier detection method for batch-processed data streams"],"prefix":"10.1007","volume":"28","author":[{"given":"Shubin","family":"Su","sequence":"first","affiliation":[]},{"given":"Limin","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Shupan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zongliang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaoxi","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Xingwang","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"key":"5420_CR1","unstructured":"Knorr, E.M., Ng, R.T.: A unified approach for mining outliers. In: Proceedings of the 1997 Conference of the Centre for Advanced Studies on Collaborative research, SASCON\u201997, pp 219\u2013222 (1997)"},{"issue":"3","key":"5420_CR2","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s007780050006","volume":"8","author":"EM Knorr","year":"2000","unstructured":"Knorr, E.M., Ng, R.T., Tucakov, V.: Distance-based outliers: algorithms and applications. VLDB J. 8(3), 237\u2013253 (2000)","journal-title":"VLDB J."},{"issue":"06","key":"5420_CR3","first-page":"2535","volume":"18","author":"S Mehnaz","year":"2021","unstructured":"Mehnaz, S., Bertino, E.: A fine-grained approach for anomaly detection in file system accesses with enhanced temporal user profiles. Inst. Electric. Electron. Eng. 18(06), 2535 (2021)","journal-title":"Inst. Electric. Electron. Eng."},{"key":"5420_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3495165","volume":"13","author":"AN Rashid","year":"2022","unstructured":"Rashid, A.N., Ahmed, M., Sikos, L.F., Haskell-Dowland, P.S.: Anomaly detection in cybersecurity datasets via cooperative co-evolution-based feature selection. ACM Trans. Manag. Inform. Syst. (TMIS) 13, 1\u201339 (2022)","journal-title":"ACM Trans. Manag. Inform. Syst. (TMIS)"},{"issue":"1","key":"5420_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3588918","volume":"1","author":"P Jia","year":"2023","unstructured":"Jia, P., Cai, S., Ooi, B.C., Wang, P., Xiong, Y.: Anomaly detection in cybersecurity datasets via cooperative co-evolution-based feature selection. Proc. ACM Manag. Data 1(1), 1\u201326 (2023)","journal-title":"Proc. ACM Manag. Data"},{"key":"5420_CR6","first-page":"1","volume":"50","author":"J Li","year":"2019","unstructured":"Li, J., Zhang, J., Pang, N., Qin, X.: Weighted outlier detection of high-dimensional categorical data using feature grouping. IEEE Trans. Syst., Man, Cybernet. 50, 1\u201314 (2019)","journal-title":"IEEE Trans. Syst., Man, Cybernet."},{"key":"5420_CR7","doi-asserted-by":"crossref","unstructured":"Kemp, J., Barker, C., Good, N., Bain, M.: Developing an anomaly detection framework for medicare claims. In: Proceedings of the 2023 Australasian Computer Science Week, pp 234\u2013237 (2023)","DOI":"10.1145\/3579375.3579410"},{"key":"5420_CR8","doi-asserted-by":"crossref","unstructured":"Avdiienko, V., Kuznetsov, K., Rommelfanger, I., Rau, A., Gorla, A., Zeller, A.: Detecting behavior anomalies in graphical user interfaces. In: 2017 IEEE\/ACM 39th International Conference on Software Engineering Companion, ICSE-C\u201917, pp 201\u2013203 (2017)","DOI":"10.1109\/ICSE-C.2017.130"},{"key":"5420_CR9","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/j.measurement.2019.06.052","volume":"146","author":"Z Chen","year":"2019","unstructured":"Chen, Z., Xu, K., Wei, J., Dong, G.: Voltage fault detection for lithium-ion battery pack using local outlier factor. Measurement 146, 544\u2013556 (2019)","journal-title":"Measurement"},{"key":"5420_CR10","doi-asserted-by":"crossref","unstructured":"Yash, P., Gundawar, S., Kumar, N., Rajasekaraiah, U.B., Ganesan, K.P., Kar, P.: Multiforecast-based early anomaly detection for spacecraft health monitoring. In: Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD) (9):275\u2013283 (2024)","DOI":"10.1145\/3632410.3632458"},{"key":"5420_CR11","doi-asserted-by":"crossref","unstructured":"Sukhija, N., Bautista, E., Butz, D., Whitney, C.: Towards anomaly detection for monitoring power consumption in hpc facilities. In: Proceedings of the 14th International Conference on Management of Digital EcoSystems, Association for Computing Machinery, MEDES \u201922, pp 1\u20138 (2022)","DOI":"10.1145\/3508397.3564826"},{"key":"5420_CR12","doi-asserted-by":"crossref","unstructured":"Ren, H., Xu, B., Wang, Y., Yi, C., Huang, C., Kou, X., Xing, T., Yang, M., Tong, J., Zhang, Q.: Time-series anomaly detection service at microsoft. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD\u201919, pp 3009\u20133017 (2019)","DOI":"10.1145\/3292500.3330680"},{"key":"5420_CR13","doi-asserted-by":"crossref","unstructured":"Nguyen, K.T., Dinh, D.T., Do, M.N., Tran, M.T.: Anomaly detection in traffic surveillance videos with gan-based future frame prediction. In: Proceedings of the 2020 International Conference on Multimedia Retrieval, ICMR\u201920, pp 457\u2013463 (2020)","DOI":"10.1145\/3372278.3390701"},{"key":"5420_CR14","doi-asserted-by":"crossref","unstructured":"Song, Y., Cheng, J.: Self-supervised anomaly detection of medical images based on dual-module discrepancy. In: Proceedings of the 5th ACM International Conference on Multimedia in Asia, pp 1\u20137 (2024)","DOI":"10.1145\/3595916.3626388"},{"key":"5420_CR15","doi-asserted-by":"crossref","unstructured":"M, Y., Srinivas, K.S.: Using federated learning in anomaly detection and analytics on real-time streaming data of healthcare. In: Proceedings of the 2023 7th International Conference on Graphics and Signal Processing, pp 29\u201334 (2023)","DOI":"10.1145\/3606283.3606288"},{"key":"5420_CR16","doi-asserted-by":"crossref","unstructured":"Hamzehei, S., Bai, J., Raimondi, G., Tripp, R., Ostroff, L., Nabavi, S.: 3d biological\/biomedical image registration with enhanced feature extraction and outlier detection. In: Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp 1\u201310 (2023)","DOI":"10.1145\/3584371.3612965"},{"key":"5420_CR17","doi-asserted-by":"crossref","unstructured":"Cao\u00a0L, R.E., Wang, J.: Poster: Multi-query outlier detection over data streams. ACM International Conference on Distributed and Event-Based Systems, pp 362\u2013365 (2016)","DOI":"10.1145\/2933267.2933292"},{"key":"5420_CR18","doi-asserted-by":"crossref","unstructured":"Cao, L., Wang, J., Rundensteiner, E.A.: Sharing-aware outlier analytics over high-volume data streams. In: Proceedings of the 2016 International Conference on Management of Data, pp 527\u2013540 (2016)","DOI":"10.1145\/2882903.2882920"},{"key":"5420_CR19","first-page":"1","volume":"13","author":"M Singh","year":"2021","unstructured":"Singh, M., Pamula, R.: Adinof: adaptive density summarizing incremental natural outlier detection in data stream. Neural Comput. Appl. 13, 1\u201317 (2021)","journal-title":"Neural Comput. Appl."},{"key":"5420_CR20","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1007\/978-3-319-53480-0_37","volume-title":"Intelligent Systems Design and Applications","author":"I Souiden","year":"2017","unstructured":"Souiden, I., Brahmi, Z., Toumi, H.: A survey on outlier detection in the context of stream mining: review of existing approaches and recommadations. In: Madureira, A.M., Abraham, A., Gamboa, D., Novais, P. (eds.) Intelligent Systems Design and Applications, pp. 372\u2013383. Springer, Cham (2017)"},{"issue":"2","key":"5420_CR21","first-page":"321","volume":"80","author":"DM Hawkins","year":"1980","unstructured":"Hawkins, D.M.: Identification of outliers. Monographs Appl. Probab. Stat. 80(2), 321\u2013338 (1980)","journal-title":"Monographs Appl. Probab. Stat."},{"key":"5420_CR22","doi-asserted-by":"crossref","unstructured":"Angiulli, F., Fassetti, F.: Detecting distance-based outliers in streams of data. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, ACM, CIKM\u201907, pp 811\u2013820 (2007)","DOI":"10.1145\/1321440.1321552"},{"key":"5420_CR23","doi-asserted-by":"crossref","unstructured":"Yang, D., Rundensteiner, E.A., Ward, M.O.: Neighbor-based pattern detection for windows over streaming data. In: Proceedings of the 12th International Conference on Extending Database Technology, Advances in Database Technology, EDBT\u201909, pp 529\u2013540 (2009)","DOI":"10.1145\/1516360.1516422"},{"issue":"12","key":"5420_CR24","first-page":"40","volume":"28","author":"T Vaithianathan","year":"2016","unstructured":"Vaithianathan, T., Zhang, X.: Fast memory efficient local outlier detection in data streams. IEEE Trans. Knowl. Data Eng. 28(12), 40 (2016)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"5420_CR25","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1016\/j.ins.2022.06.013","volume":"607","author":"A Degirmenci","year":"2022","unstructured":"Degirmenci, A., Karal, O.: Efficient density and cluster based incremental outlier detection in data streams. Inf. Sci. 607, 901\u2013920 (2022)","journal-title":"Inf. Sci."},{"key":"5420_CR26","doi-asserted-by":"crossref","unstructured":"Na, G.S., Kim, D., Yu, H.: Dilof: Effective and memory efficient local outlier detection in data streams. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1993\u20132002 (2018)","DOI":"10.1145\/3219819.3220022"},{"issue":"6","key":"5420_CR27","doi-asserted-by":"publisher","first-page":"2282","DOI":"10.1109\/TNNLS.2022.3162123","volume":"33","author":"G Pang","year":"2022","unstructured":"Pang, G., Aggarwal, C., Shen, C., Sebe, N.: Editorial deep learning for anomaly detection. IEEE Trans. Neural Netw. Learn. Syst. 33(6), 2282\u20132286 (2022)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"5420_CR28","doi-asserted-by":"crossref","unstructured":"Gallego-Mejia, J., Bustos-Brinez, O., Gonzalez, F.: Inqmad: Incremental quantum measurement anomaly detection. In: IEEE International Conference on Data Mining Workshops (ICDMW), pp 787\u2013796 (2022)","DOI":"10.1109\/ICDMW58026.2022.00107"},{"key":"5420_CR29","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.is.2015.07.006","volume":"55","author":"M Kontaki","year":"2016","unstructured":"Kontaki, M., Gounaris, A., Papadopoulos, A.N., Tsichlas, K., Manolopoulos, Y.: Efficient and flexible algorithms for monitoring distance-based outliers over data streams. Inf. Syst. 55, 37\u201353 (2016)","journal-title":"Inf. Syst."},{"issue":"2","key":"5420_CR30","doi-asserted-by":"publisher","first-page":"141","DOI":"10.14778\/3425879.3425885","volume":"14","author":"L Tran","year":"2020","unstructured":"Tran, L., Mun, M.Y., Shahabi, C.: Real-time distance-based outlier detection in data streams. Proc. VLDB Endow. 14(2), 141\u2013153 (2020)","journal-title":"Proc. VLDB Endow."},{"key":"5420_CR31","doi-asserted-by":"crossref","unstructured":"Angiulli, F., Fassetti, F.: Detecting distance-based outliers in streams of data. In: International Conference on Information and Knowledge Management, CIKM\u201907, pp 811\u2013820 (2007)","DOI":"10.1145\/1321440.1321552"},{"key":"5420_CR32","doi-asserted-by":"crossref","unstructured":"Cao, L., Yang, D., Wang, Q., Yu, Y., Rundensteiner, E.A.: Scalable distance-based outlier detection over high-volume data streams. In: IEEE International Conference on Data Engineering, ICDE\u201914, pp 76\u201387 (2014)","DOI":"10.1109\/ICDE.2014.6816641"},{"key":"5420_CR33","doi-asserted-by":"crossref","unstructured":"Kontaki, M., Gounaris, A., Papadopoulos, A.N., Tsichlas, K., Manolopoulos, Y.: Continuous monitoring of distance-based outliers over data streams. In: IEEE International Conference on Data Engineering, ICDE\u201911, pp 135\u2013146 (2011)","DOI":"10.1109\/ICDE.2011.5767923"},{"key":"5420_CR34","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.is.2015.07.006","volume":"55","author":"M Kontaki","year":"2016","unstructured":"Kontaki, M., Gounaris, A., Papadopoulos, A.N., Tsichlas, K., Manolopoulos, Y.: Efficient and flexible algorithms for monitoring distance-based outliers over data streams. Inform. Syst. 55, 37\u201353 (2016)","journal-title":"Inform. Syst."},{"key":"5420_CR35","doi-asserted-by":"crossref","unstructured":"Tran, L., Fan, L., Shahabi, C.: Fast distance-based outlier detection in data streams based on micro-clusters. In: Proceedings of the Tenth International Symposium on Information and Communication Technology, SoICT\u201919, pp 162\u2013169 (2019)","DOI":"10.1145\/3368926.3369667"},{"key":"5420_CR36","doi-asserted-by":"crossref","unstructured":"Cao, L., Wang, J., Rundensteiner, E.A.: Multi-query outlier detection over data streams: poster. In: Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems, DEBS\u201916, pp 362\u2013365 (2016)","DOI":"10.1145\/2933267.2933292"},{"key":"5420_CR37","doi-asserted-by":"crossref","unstructured":"Cao, L., Wang, J., Rundensteiner, E.A.: Sharing-aware outlier analytics over high-volume data streams. In: Proceedings of the 2016 International Conference on Management of Data, SIGMOD\u201916, pp 527\u2013540 (2016)","DOI":"10.1145\/2882903.2882920"},{"issue":"11","key":"5420_CR38","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.14778\/3342263.3342269","volume":"12","author":"S Yoon","year":"2019","unstructured":"Yoon, S., Lee, J.G., Lee, B.S.: Nets: extremely fast outlier detection from a data stream via set-based processing. Proc. VLDB Endow. 12(11), 1303\u20131315 (2019)","journal-title":"Proc. VLDB Endow."},{"issue":"1","key":"5420_CR39","first-page":"1","volume":"13","author":"A Kumar","year":"2021","unstructured":"Kumar, A., Kumar, A., Bashir, A.K., Rashid, M., Kumar, V.A., Kharel, R.: Rupak: Distance based pattern driven mining for outlier detection in high dimensional big dataset. ACM Trans. Manag. Inform. Syst. (TMIS) 13(1), 1\u201317 (2021)","journal-title":"ACM Trans. Manag. Inform. Syst. (TMIS)"},{"key":"5420_CR40","volume-title":"Data Ming Concepts and Techniques","author":"JW Han","year":"2001","unstructured":"Han, J.W., Kambr, M.: Data Ming Concepts and Techniques. Higher Education Press, Beijing (2001)"},{"issue":"12","key":"5420_CR41","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.14778\/2994509.2994526","volume":"9","author":"L Tran","year":"2016","unstructured":"Tran, L., Fan, L., Shahabi, C.: Distance-based outlier detection in data streams. Proc. Vldb Endow. 9(12), 1089\u20131100 (2016)","journal-title":"Proc. Vldb Endow."},{"key":"5420_CR42","doi-asserted-by":"crossref","unstructured":"Kontaki, M., Gounaris, A., Papadopoulos, A.N., Tsichlas, K., Manolopoulos, Y.: Continuous monitoring of distance-based outliers over data streams. In: Proceedings of the 2011 IEEE 27th International Conference on Data Engineering, IEEE Computer Society, ICDE\u201911, pp 135\u2013146 (2011)","DOI":"10.1109\/ICDE.2011.5767923"},{"issue":"12","key":"5420_CR43","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.14778\/2994509.2994526","volume":"9","author":"L Tran","year":"2016","unstructured":"Tran, L., Fan, L., Shahabi, C.: Distance-based outlier detection in data streams. Proc. VLDB Endow. 9(12), 1089\u20131100 (2016)","journal-title":"Proc. VLDB Endow."},{"key":"5420_CR44","doi-asserted-by":"crossref","unstructured":"Cao, L., Yang, D., Wang, Q., Yu, Y., Wang, J., Rundensteiner, E.A.: Scalable distance-based outlier detection over high-volume data streams. In: 2014 IEEE 30th International Conference on Data Engineering, ICDE\u201914, pp 76\u201387 (2014)","DOI":"10.1109\/ICDE.2014.6816641"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05420-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05420-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05420-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T17:31:05Z","timestamp":1759944665000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05420-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,11]]},"references-count":44,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["5420"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05420-y","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"type":"print","value":"1386-7857"},{"type":"electronic","value":"1573-7543"}],"subject":[],"published":{"date-parts":[[2025,9,11]]},"assertion":[{"value":"22 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 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 Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The work is a novel work and has not been published elsewhere nor is it currently under review for publication elsewhere.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}}],"article-number":"696"}}