{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T19:08:30Z","timestamp":1770923310011,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819567850","type":"print"},{"value":"9789819567867","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-6786-7_7","type":"book-chapter","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T18:07:03Z","timestamp":1770919623000},"page":"94-110","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DriftSense: Adaptive Drift Detection with\u00a0Incremental Hoeffding Trees for\u00a0Real-Time Spatial Crowdsourcing"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1175-3644","authenticated-orcid":false,"given":"Md Mujibur","family":"Rahman","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2196-7651","authenticated-orcid":false,"given":"Quazi","family":"Mamun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5848-7451","authenticated-orcid":false,"given":"Michael","family":"Bewong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4868-4945","authenticated-orcid":false,"given":"Md Zahidul","family":"Islam","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,13]]},"reference":[{"key":"7_CR1","doi-asserted-by":"publisher","unstructured":"AbuElHassan, S., Abo\u00a0Alian, A., AbdelKader, T., Badr, N.: A review on privacy-preserving techniques for spatiotemporal data. Int. J. Data Sci. Anal. 1\u201314 (2025). https:\/\/doi.org\/10.1007\/s41060-025-00807-x","DOI":"10.1007\/s41060-025-00807-x"},{"key":"7_CR2","doi-asserted-by":"publisher","first-page":"111535","DOI":"10.1016\/j.knosys.2024.111535","volume":"289","author":"GJ Aguiar","year":"2024","unstructured":"Aguiar, G.J., Cano, A.: A comprehensive analysis of concept drift locality in data streams. Knowl.-Based Syst. 289, 111535 (2024). https:\/\/doi.org\/10.1016\/j.knosys.2024.111535","journal-title":"Knowl.-Based Syst."},{"key":"7_CR3","doi-asserted-by":"publisher","unstructured":"Amador Coelho, R., Bambirra Torres, L.C., Leite de Castro, C.: Concept drift detection with quadtree-based spatial mapping of streaming data. Inf. Sci. 625, 578\u2013592 (2023). https:\/\/doi.org\/10.1016\/j.ins.2022.12.085, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020025522015808","DOI":"10.1016\/j.ins.2022.12.085"},{"key":"7_CR4","doi-asserted-by":"publisher","unstructured":"Assis, D.N., Souza, V.M.: ADWIN-U: adaptive windowing for unsupervised drift detection on data streams: DN Assis, VMA Souza. Knowl. Inf. Syst. 1\u201330 (2025). https:\/\/doi.org\/10.1007\/s10115-025-02523-1","DOI":"10.1007\/s10115-025-02523-1"},{"key":"7_CR5","doi-asserted-by":"publisher","first-page":"121755","DOI":"10.1016\/j.eswa.2023.121755","volume":"238","author":"H Bai","year":"2024","unstructured":"Bai, H., Hui, S.C.: A crowdsourcing-based incremental learning framework for automated essays scoring. Expert Syst. Appl. 238, 121755 (2024)","journal-title":"Expert Syst. Appl."},{"key":"7_CR6","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.eswa.2017.08.023","volume":"90","author":"RS Barros","year":"2017","unstructured":"Barros, R.S., Cabral, D.R., Gon\u00e7alves, P.M., Santos, S.G.: RDDM: reactive drift detection method. Expert Syst. Appl. 90, 344\u2013355 (2017)","journal-title":"Expert Syst. Appl."},{"key":"7_CR7","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1016\/j.eswa.2017.10.003","volume":"92","author":"J Dem\u0161ar","year":"2018","unstructured":"Dem\u0161ar, J., Bosni\u0107, Z.: Detecting concept drift in data streams using model explanation. Expert Syst. Appl. 92, 546\u2013559 (2018). https:\/\/doi.org\/10.1016\/j.eswa.2017.10.003","journal-title":"Expert Syst. Appl."},{"key":"7_CR8","doi-asserted-by":"publisher","unstructured":"Ferjani, I., Alsaif, S.A.: Dynamic road anomaly detection: harnessing smartphone accelerometer data with incremental concept drift detection and classification. Sensors 24(24) (2024). https:\/\/doi.org\/10.3390\/s24248112, https:\/\/www.mdpi.com\/1424-8220\/24\/24\/8112","DOI":"10.3390\/s24248112"},{"issue":"8","key":"7_CR9","doi-asserted-by":"publisher","first-page":"3584","DOI":"10.1109\/TCYB.2025.3573292","volume":"55","author":"YF Ge","year":"2025","unstructured":"Ge, Y.F., Wang, H., Bertino, E., Cao, J., Zhang, Y.: Multiobjective privacy-preserving task assignment in spatial crowdsourcing. IEEE Trans. Cybern. 55(8), 3584\u20133597 (2025). https:\/\/doi.org\/10.1109\/TCYB.2025.3573292","journal-title":"IEEE Trans. Cybern."},{"issue":"5","key":"7_CR10","doi-asserted-by":"publisher","first-page":"3725","DOI":"10.1007\/s10462-020-09939-x","volume":"54","author":"\u00d6 G\u00f6z\u00fca\u00e7\u0131k","year":"2020","unstructured":"G\u00f6z\u00fca\u00e7\u0131k, \u00d6., Can, F.: Concept learning using one-class classifiers for implicit drift detection in evolving data streams. Artif. Intell. Rev. 54(5), 3725\u20133747 (2020). https:\/\/doi.org\/10.1007\/s10462-020-09939-x","journal-title":"Artif. Intell. Rev."},{"issue":"4","key":"7_CR11","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1111\/coin.12520","volume":"38","author":"M Han","year":"2022","unstructured":"Han, M., Chen, Z., Li, M., Wu, H., Zhang, X.: A survey of active and passive concept drift handling methods. Comput. Intell. 38(4), 1492\u20131535 (2022). https:\/\/doi.org\/10.1111\/coin.12520","journal-title":"Comput. Intell."},{"key":"7_CR12","doi-asserted-by":"publisher","unstructured":"Jiao, Y., Lin, Z., Yu, L., Wu, X.: A fine-grain batching-based task allocation algorithm for spatial crowdsourcing. ISPRS Int. J. Geo-Inf. 11(3) (2022). https:\/\/doi.org\/10.3390\/ijgi11030203, https:\/\/www.mdpi.com\/2220-9964\/11\/3\/203","DOI":"10.3390\/ijgi11030203"},{"key":"7_CR13","doi-asserted-by":"publisher","unstructured":"Li, Z., Xiong, Y., Huang, W.: Drift-detection based incremental ensemble for reacting to different kinds of concept drift. In: 2019 5th International Conference on Big Data Computing and Communications (BIGCOM), pp. 107\u2013114. IEEE (2019). https:\/\/doi.org\/10.1109\/BIGCOM.2019.00025","DOI":"10.1109\/BIGCOM.2019.00025"},{"key":"7_CR14","doi-asserted-by":"publisher","unstructured":"Liu, W., et al.: An adaptive hoeffding tree model based on differential entropy and relative entropy for concept drift detection. In: 2024 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20138. IEEE (2024). https:\/\/doi.org\/10.1109\/IJCNN60899.2024.10650818","DOI":"10.1109\/IJCNN60899.2024.10650818"},{"key":"7_CR15","doi-asserted-by":"publisher","first-page":"26362","DOI":"10.1109\/ACCESS.2021.3058211","volume":"9","author":"G Qiu","year":"2021","unstructured":"Qiu, G., Shen, Y.: Mobility-aware differentially private trajectory for privacy-preserving continual crowdsourcing. IEEE Access 9, 26362\u201326376 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3058211","journal-title":"IEEE Access"},{"key":"7_CR16","doi-asserted-by":"publisher","first-page":"111596","DOI":"10.1016\/j.knosys.2024.111596","volume":"290","author":"Y Sun","year":"2024","unstructured":"Sun, Y., Mi, J., Jin, C.: Entropy-based concept drift detection in information systems. Knowl.-Based Syst. 290, 111596 (2024). https:\/\/doi.org\/10.1016\/j.knosys.2024.111596","journal-title":"Knowl.-Based Syst."},{"key":"7_CR17","doi-asserted-by":"publisher","unstructured":"Suryawanshi, S., Goswami, A., Patil, P.: Enhancing drift detection and model uncertainty handling in imbalanced streaming data using autoencoder-based approach. In: 2023 Second International Conference on Smart Technologies for Smart Nation (SmartTechCon), pp. 1265\u20131270 (2023). https:\/\/doi.org\/10.1109\/SmartTechCon57526.2023.10391432","DOI":"10.1109\/SmartTechCon57526.2023.10391432"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Su\u00e1rez-Cetrulo, A.L., Quintana, D., Cervantes, A.: A survey on machine learning for recurring concept drifting data streams. Expert Syst. Appl. 213, 118934 (2023)","DOI":"10.1016\/j.eswa.2022.118934"},{"key":"7_CR19","doi-asserted-by":"publisher","unstructured":"Wen, L., Xuebin, M., Xu, W.: DDLP: dynamic location data publishing with differential privacy in mobile crowdsensing. China Commun. 22(5), 238\u2013255 (2025). https:\/\/doi.org\/10.23919\/JCC.ja.2022-0734","DOI":"10.23919\/JCC.ja.2022-0734"},{"key":"7_CR20","doi-asserted-by":"publisher","unstructured":"Xiang, Q., Zi, L., Cong, X., Wang, Y.: Concept drift adaptation methods under the deep learning framework: a literature review. Appl. Sci. 13(11) (2023). https:\/\/doi.org\/10.3390\/app13116515, https:\/\/www.mdpi.com\/2076-3417\/13\/11\/6515","DOI":"10.3390\/app13116515"},{"issue":"11","key":"7_CR21","doi-asserted-by":"publisher","first-page":"18063","DOI":"10.1109\/TITS.2024.3434561","volume":"25","author":"Y Yang","year":"2024","unstructured":"Yang, Y., Zhu, M., Li, J., Wang, C., Fan, G.: Crowdsourcing regional coverage balancing method based on transfer learning in taxi service. IEEE Trans. Intell. Transp. Syst. 25(11), 18063\u201318077 (2024). https:\/\/doi.org\/10.1109\/TITS.2024.3434561","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"7_CR22","doi-asserted-by":"publisher","unstructured":"Zhang, X., et al.: Continual learning with strategic selection and forgetting for network intrusion detection. In: IEEE INFOCOM 2025 - IEEE Conference on Computer Communications, pp. 1\u201310 (2025). https:\/\/doi.org\/10.1109\/INFOCOM55648.2025.11044615","DOI":"10.1109\/INFOCOM55648.2025.11044615"}],"container-title":["Communications in Computer and Information Science","Data Science and Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-6786-7_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T18:07:05Z","timestamp":1770919625000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-6786-7_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819567850","9789819567867"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-6786-7_7","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"13 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AusDM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Conference on Data Science and Machine Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brisbane","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ausdm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ausdm25.ausdm.org\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}