{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:45:09Z","timestamp":1742975109336,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319461304"},{"type":"electronic","value":"9783319461311"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-46131-1_20","type":"book-chapter","created":{"date-parts":[[2016,9,2]],"date-time":"2016-09-02T09:59:36Z","timestamp":1472810376000},"page":"129-144","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Finding Dynamic Co-evolving Zones in Spatial-Temporal Time Series Data"],"prefix":"10.1007","author":[{"given":"Yun","family":"Cheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiucheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,9,3]]},"reference":[{"key":"20_CR1","unstructured":"aqi.cn: Beijing air pollution. http:\/\/aqicn.org\/city\/beijing\/"},{"key":"20_CR2","unstructured":"Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop, Seattle, WA, vol. 10, pp. 359\u2013370 (1994)"},{"key":"20_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1007\/978-3-319-12640-1_16","volume-title":"Neural Information Processing","author":"Y Cheng","year":"2014","unstructured":"Cheng, Y., Li, X., Li, Z., Jiang, S., Jiang, X.: Fine-grained air quality monitoring based on gaussian process regression. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds.) ICONIP 2014, Part II. LNCS, vol. 8835, pp. 126\u2013134. Springer, Heidelberg (2014)"},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Li, X., Li, Z., Jiang, S., Li, Y., Jia, J., Jiang, X.: Aircloud: a cloud-based air-quality monitoring system for everyone. In: Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems, pp. 251\u2013265. ACM (2014)","DOI":"10.1145\/2668332.2668346"},{"issue":"1","key":"20_CR5","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s11634-006-0004-6","volume":"1","author":"AD Chouakria","year":"2007","unstructured":"Chouakria, A.D., Nagabhushan, P.N.: Adaptive dissimilarity index for measuring time series proximity. Adv. Data Anal. Classif. 1(1), 5\u201321 (2007)","journal-title":"Adv. Data Anal. Classif."},{"key":"20_CR6","unstructured":"Eiter, T., Mannila, H.: Computing discrete fr\u00e9chet distance. Technical report, Citeseer (1994)"},{"issue":"1","key":"20_CR7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/BF03018603","volume":"22","author":"M. Maurice Fr\u00e9chet","year":"1906","unstructured":"Fr\u00e9chet, M.M.: Sur quelques points du calcul fonctionnel. Rendiconti del Circolo Matematico di Palermo (1884\u20131940) 22(1), 1\u201372 (1906)","journal-title":"Rendiconti del Circolo Matematico di Palermo"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Kawahara, Y., Sugiyama, M.: Change-point detection in time-series data by direct density-ratio estimation. In: SDM, vol. 9, pp. 389\u2013400. SIAM (2009)","DOI":"10.1137\/1.9781611972795.34"},{"key":"20_CR9","unstructured":"Keogh, E., Chu, S., Hart, D., Pazzani, M.: An online algorithm for segmenting time series. In: Proceedings IEEE International Conference on Data Mining, 2001, ICDM 2001, pp. 289\u2013296. IEEE (2001)"},{"key":"20_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/9789812565402_0001","volume":"57","author":"E Keogh","year":"2004","unstructured":"Keogh, E., Chu, S., Hart, D., Pazzani, M.: Segmenting time series: a survey and novel approach. Data Min. Time Ser. Databases 57, 1\u201322 (2004)","journal-title":"Data Min. Time Ser. Databases"},{"issue":"5","key":"20_CR11","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1093\/bioinformatics\/btm563","volume":"24","author":"P Langfelder","year":"2008","unstructured":"Langfelder, P., Zhang, B., Horvath, S.: Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for r. Bioinformatics 24(5), 719\u2013720 (2008)","journal-title":"Bioinformatics"},{"key":"20_CR12","unstructured":"Marti, G., Andler, S., Nielsen, F., Donnat, P.: Clustering financial time series: How long is enough? arXiv preprint arXiv:1603.04017 (2016)"},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Montero, P., Vilar, J.A.: Tsclust: an r package for time series clustering. J. Stat. Softw. 62(1), 1\u201343 (2014)","DOI":"10.18637\/jss.v062.i01"},{"key":"20_CR14","unstructured":"Sankoff, D., Kruskal, J.B.: Time warps, string edits, and macromolecules: the theory and practice of sequence comparison. Addison-Wesley Publication, Reading (1983). Edited by Sankoff, D., Kruskal, J.B"},{"key":"20_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/11535331_8","volume-title":"Advances in Spatial and Temporal Databases","author":"M Sharifzadeh","year":"2005","unstructured":"Sharifzadeh, M., Azmoodeh, F., Shahabi, C.: Change detection in time series data using wavelet footprints. In: Medeiros, C.B., Egenhofer, M., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 127\u2013144. Springer, Heidelberg (2005)"},{"issue":"3","key":"20_CR16","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1016\/j.telpol.2013.12.002","volume":"39","author":"R Trasarti","year":"2015","unstructured":"Trasarti, R., Olteanu-Raimond, A.M., Nanni, M., Couronn\u00e9, T., Furletti, B., Giannotti, F., Smoreda, Z., Ziemlicki, C.: Discovering urban and country dynamics from mobile phone data with spatial correlation patterns. Telecommun. Policy 39(3), 347\u2013362 (2015)","journal-title":"Telecommun. Policy"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Wang, P., Wang, H., Wang, W.: Finding semantics in time series. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, pp. 385\u2013396. ACM (2011)","DOI":"10.1145\/1989323.1989364"},{"issue":"11","key":"20_CR18","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1016\/j.patcog.2005.01.025","volume":"38","author":"T. Warren Liao","year":"2005","unstructured":"Warren Liao, T.: Clustering of time series data-a survey. Pattern Recogn. 38(11), 1857\u20131874 (2005). http:\/\/dx.doi.org\/10.1016\/j.patcog.2005.01.025","journal-title":"Pattern Recognition"},{"issue":"3","key":"20_CR19","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1109\/TNN.2005.845141","volume":"16","author":"R Xu","year":"2005","unstructured":"Xu, R., Wunsch, D., et al.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645\u2013678 (2005)","journal-title":"IEEE Trans. Neural Netw."},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"Yamanishi, K., Takeuchi, J.i.: A unifying framework for detecting outliers and change points from non-stationary time series data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 676\u2013681. ACM (2002)","DOI":"10.1145\/775047.775148"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zheng, Y., Ma, X., Han, J.: Assembler: efficient discovery of spatial co-evolving patterns in massive geo-sensory data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1415\u20131424. ACM (2015)","DOI":"10.1145\/2783258.2783394"},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Yi, X., Li, M., Li, R., Shan, Z., Chang, E., Li, T.: Forecasting fine-grained air quality based on big data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2267\u20132276. ACM (2015)","DOI":"10.1145\/2783258.2788573"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-46131-1_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,5]],"date-time":"2021-09-05T00:04:08Z","timestamp":1630800248000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-46131-1_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319461304","9783319461311"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-46131-1_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]},"assertion":[{"value":"3 September 2016","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Riva del Garda","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2016","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2016","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2016","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2016","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}