{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T20:16:04Z","timestamp":1742933764170,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819612413"},{"type":"electronic","value":"9789819612420"}],"license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-1242-0_11","type":"book-chapter","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T08:07:31Z","timestamp":1733990851000},"page":"143-157","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mining Rare Temporal Pattern in\u00a0Time Series"],"prefix":"10.1007","author":[{"given":"Long Van","family":"Ho","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nguyen","family":"Ho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong Trinh","family":"Le","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anh-Vu","family":"Dinh-Duc","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khang","family":"Quach","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ngoc Tu","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Alipourchavary, E., Erfani, S.M., Leckie, C.: Mining rare recurring events in network traffic using second order contrast patterns. In: International Joint Conference on Neural Networks (IJCNN). IEEE (2021)","key":"11_CR1","DOI":"10.1109\/IJCNN52387.2021.9533918"},{"doi-asserted-by":"crossref","unstructured":"Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26 (1983)","key":"11_CR2","DOI":"10.1145\/182.358434"},{"doi-asserted-by":"crossref","unstructured":"Begum, N., Keogh, E.: Rare time series motif discovery from unbounded streams. Proc. VLDB Endow. 8(2) (2014)","key":"11_CR3","DOI":"10.14778\/2735471.2735476"},{"doi-asserted-by":"crossref","unstructured":"Biswas, S., Mondal, K.C.: Dynamic FP tree based rare pattern mining using multiple item supports constraints. In: Computational Intelligence, Communications, and Business Analytics (CICBA). Springer (2019)","key":"11_CR4","DOI":"10.1007\/978-981-13-8581-0_24"},{"doi-asserted-by":"crossref","unstructured":"Borah, A., Nath, B.: Rare association rule mining from incremental databases. Pattern Anal. Appl. 23 (2020)","key":"11_CR5","DOI":"10.1007\/s10044-018-0759-3"},{"doi-asserted-by":"crossref","unstructured":"Bouasker, S., Inoubli, W., Yahia, S.B., Diallo, G.: Pregnancy associated breast cancer gene expressions: new insights on their regulation based on rare correlated patterns. Trans. Comput. Biol. Bioinform. 18(3) (2020)","key":"11_CR6","DOI":"10.1109\/TCBB.2020.3015236"},{"doi-asserted-by":"crossref","unstructured":"Cai, S., et al.: An efficient anomaly detection method for uncertain data based on minimal rare patterns with the consideration of anti-monotonic constraints. Inf. Sci. 580 (2021)","key":"11_CR7","DOI":"10.1016\/j.ins.2021.08.097"},{"unstructured":"City, N.Y.: NYC opendata (2019). https:\/\/opendata.cityofnewyork.us\/","key":"11_CR8"},{"doi-asserted-by":"crossref","unstructured":"Cui, Y., Gan, W., Lin, H., Zheng, W.: Fri-miner: fuzzy rare itemset mining. Appl. Intell. (2022)","key":"11_CR9","DOI":"10.1007\/s10489-021-02574-1"},{"doi-asserted-by":"crossref","unstructured":"Fournier-Viger, P., Yang, P., Li, Z., Lin, J.C.W., Kiran, R.U.: Discovering rare correlated periodic patterns in multiple sequences. Data Knowl. Eng. 126 (2020)","key":"11_CR10","DOI":"10.1016\/j.datak.2019.101733"},{"doi-asserted-by":"crossref","unstructured":"Gao, Y., Lin, J.: Efficient discovery of time series motifs with large length range in million scale time series. In: IEEE International Conference on Data Mining (ICDM). IEEE (2017)","key":"11_CR11","DOI":"10.1109\/ICDM.2017.8356939"},{"unstructured":"Healy, W., et al.: Net zero energy residential test facility instrumented data (2018). https:\/\/pages.nist.gov\/netzero\/index.html\/","key":"11_CR12"},{"doi-asserted-by":"crossref","unstructured":"Ho, V.L., Ho, N., Le, C.T., Dinh-Duc, A.V., Nguyen, N.T.: Mining rare temporal pattern in time series (2024). https:\/\/arxiv.org\/abs\/2409.05042","key":"11_CR13","DOI":"10.1007\/978-981-96-1242-0_11"},{"doi-asserted-by":"crossref","unstructured":"Ho, V.L., Ho, N., Pedersen, T.B.: Efficient temporal pattern mining in big time series using mutual information, vol.\u00a015. VLDB Endowment (2022)","key":"11_CR14","DOI":"10.14778\/3494124.3494147"},{"doi-asserted-by":"crossref","unstructured":"Ho, V.L., Ho, N., Pedersen, T.B.: Mining seasonal temporal patterns in time series. In: IEEE International Conference on Data Engineering (ICDE). IEEE (2023)","key":"11_CR15","DOI":"10.1109\/ICDE55515.2023.00174"},{"doi-asserted-by":"crossref","unstructured":"Iqbal, M., Wulandari, C.P., Yunanto, W., Sari, G.I.P.: Mining non-zero-rare sequential patterns on activity recognition. Jurnal Matematika MANTIK 5(1) (2019)","key":"11_CR16","DOI":"10.15642\/mantik.2019.5.1.1-9"},{"doi-asserted-by":"crossref","unstructured":"Ji, Y., Ohsawa, Y.: Mining frequent and rare itemsets with weighted supports using additive neural itemset embedding. In: International Joint Conference on Neural Networks (IJCNN). IEEE (2021)","key":"11_CR17","DOI":"10.1109\/IJCNN52387.2021.9534070"},{"doi-asserted-by":"crossref","unstructured":"Kam, P., Fu, A.W.C.: Discovering temporal patterns for interval-based events. In: Data Warehousing and Knowledge Discovery (DaWak) (2000)","key":"11_CR18","DOI":"10.1007\/3-540-44466-1_32"},{"doi-asserted-by":"crossref","unstructured":"Lee, Z., Lindgren, T., Papapetrou, P.: Z-miner: an efficient method for mining frequent arrangements of event intervals. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2020)","key":"11_CR19","DOI":"10.1145\/3394486.3403095"},{"doi-asserted-by":"crossref","unstructured":"Li, Y., Cai, S.: Detecting outliers in data streams based on minimum rare pattern mining and pattern matching. Inf. Technol. Control 51(2) (2022)","key":"11_CR20","DOI":"10.5755\/j01.itc.51.2.30524"},{"doi-asserted-by":"crossref","unstructured":"Moskovitch, R., Shahar, Y.: Fast time intervals mining using the transitivity of temporal relations. Knowl. Inf. Syst. 42 (2015)","key":"11_CR21","DOI":"10.1007\/s10115-013-0707-x"},{"doi-asserted-by":"crossref","unstructured":"Omiecinski, E.R.: Alternative interest measures for mining associations in databases. IEEE Trans. Knowl. Data Eng. (TKDE) 15(1) (2003)","key":"11_CR22","DOI":"10.1109\/TKDE.2003.1161582"},{"doi-asserted-by":"crossref","unstructured":"Ouyang, W.: Mining rare sequential patterns in large transaction databases. In: International Conference on Computer Science and Electronic Technology. Atlantis Press (2016)","key":"11_CR23","DOI":"10.2991\/cset-16.2016.39"},{"doi-asserted-by":"crossref","unstructured":"Piri, S., Delen, D., Liu, T., Paiva, W.: Development of a new metric to identify rare patterns in association analysis: the case of analyzing diabetes complications. Expert Syst. Appl. 94 (2018)","key":"11_CR24","DOI":"10.1016\/j.eswa.2017.09.061"},{"unstructured":"Rahman, A.: Rare sequential pattern mining of critical infrastructure control logs for anomaly detection. Ph.D. thesis, Queensland University of Technology (2019)","key":"11_CR25"},{"doi-asserted-by":"crossref","unstructured":"Rahman, A., Xu, Y., Radke, K., Foo, E.: Finding anomalies in scada logs using rare sequential pattern mining. In: Network and System Security. Springer (2016)","key":"11_CR26","DOI":"10.1007\/978-3-319-46298-1_32"},{"unstructured":"Samet, A., Guyet, T., Negrevergne, B.: Mining rare sequential patterns with ASP. In: International Conference on Inductive Logic Programming (2017)","key":"11_CR27"},{"unstructured":"K city infectious disease\u00a0surveillance system: Kidss (2021). https:\/\/kidss.city.kawasaki.jp\/","key":"11_CR28"},{"unstructured":"Weather, O.: Open weather (2021). https:\/\/openweathermap.org\/","key":"11_CR29"},{"doi-asserted-by":"crossref","unstructured":"Wu, S.Y., Chen, Y.L.: Mining nonambiguous temporal patterns for interval-based events. EEE Trans. Knowl. Data Eng. (TKDE) 19 (2007)","key":"11_CR30","DOI":"10.1109\/TKDE.2007.190613"},{"doi-asserted-by":"crossref","unstructured":"Zhu, J., Wang, K., Wu, Y., Hu, Z., Wang, H.: Mining user-aware rare sequential topic patterns in document streams. IEEE Trans. Knowl. Data Eng. (TKDE) 28(7) (2016)","key":"11_CR31","DOI":"10.1109\/TKDE.2016.2541149"}],"container-title":["Lecture Notes in Computer Science","Databases Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-1242-0_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T20:03:56Z","timestamp":1736193836000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-1242-0_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,13]]},"ISBN":["9789819612413","9789819612420"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-1242-0_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,13]]},"assertion":[{"value":"13 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Database Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gold Coast, QLD","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"35","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adc2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adc-conference.github.io\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}