{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:50:24Z","timestamp":1740124224820,"version":"3.37.3"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T00:00:00Z","timestamp":1600992000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T00:00:00Z","timestamp":1600992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP15K21069","JP16H01722"],"award-info":[{"award-number":["JP15K21069","JP16H01722"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Distrib Parallel Databases"],"published-print":{"date-parts":[[2021,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This article addresses a new pattern mining problem in time series sensor data, which we call <jats:bold><jats:italic>correlated attribute pattern mining<\/jats:italic><\/jats:bold>. The correlated attribute patterns (CAPs for short) are the sets of attributes (e.g., temperature and traffic volume) on sensors that are spatially close to each other and temporally correlated in their measurements. Although the CAPs are useful to accurately analyze and understand spatio-temporal correlation between attributes, the existing mining methods are inefficient to discover CAPs because they extract unnecessary patterns. Therefore, we propose a mining method Miscela to efficiently discover CAPs. M<jats:sc>iscela<\/jats:sc> can discover not only simultaneous correlated patterns but also time delayed correlated patterns. Furthermore, we extend M<jats:sc>iscela<\/jats:sc> to automatically search for correlated patterns with any time delays. Through our experiments using three real sensor datasets, we show that the response time of M<jats:sc>iscela<\/jats:sc> is up to 20.84 times faster compared with the state-of-the-art method. We show that M<jats:sc>iscela<\/jats:sc> discovers meaningful patterns for urban managements and environmental studies.<\/jats:p>","DOI":"10.1007\/s10619-020-07312-z","type":"journal-article","created":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T16:03:00Z","timestamp":1601049780000},"page":"637-664","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MISCELA: discovering simultaneous and time-delayed correlated attribute patterns"],"prefix":"10.1007","volume":"39","author":[{"given":"Kei","family":"Harada","sequence":"first","affiliation":[]},{"given":"Yuya","family":"Sasaki","sequence":"additional","affiliation":[]},{"given":"Makoto","family":"Onizuka","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,25]]},"reference":[{"key":"7312_CR1","unstructured":"Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB, pp. 487\u2013499 (1994)"},{"key":"7312_CR2","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Li, X., Li, Y.: Finding dynamic co-evolving zones in spatial-temporal time series data. In: Proceedings of the ECML PKDD, pp. 129\u2013144 (2016)","DOI":"10.1007\/978-3-319-46131-1_20"},{"key":"7312_CR3","doi-asserted-by":"crossref","unstructured":"Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: Proceedings of the ACM SIGKDD, pp. 493\u2013498 (2003)","DOI":"10.1145\/956750.956808"},{"key":"7312_CR4","unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the ACM SIGKDD, pp. 226\u2013231 (1996)"},{"key":"7312_CR5","doi-asserted-by":"crossref","unstructured":"Harada, K., Sasaki, Y., Onizuka, M.: Miscela: Discovering correlated attribute patterns in time series sensor data. In: Proceedings of the IEEE MDM, pp. 72\u201380 (2019)","DOI":"10.1109\/MDM.2019.00-72"},{"key":"7312_CR6","unstructured":"Hassani, M., M\u00fcller, E., Spaus, P., Faqolli, A., Palpanas, T., Seidl, T.: Self-organizing energy aware clustering of nodes in sensor networks using relevant attributes (2010)"},{"key":"7312_CR7","unstructured":"Keogh, E., Chu, S., Hart, D., Pazzani, M.: An online algorithm for segmenting time series. In: Proceedings of the IEEE ICDM, pp. 289\u2013296 (2001)"},{"key":"7312_CR8","unstructured":"Lonardi, J., Patel, P.: Finding motifs in time series. In: Proceedings of the ACM SIGKDD, pp. 53\u201368 (2002)"},{"key":"7312_CR9","doi-asserted-by":"crossref","unstructured":"Matsubara, Y., Sakurai, Y., Van Panhuis, W.G., Faloutsos, C.: FUNNEL: automatic mining of spatially coevolving epidemics. In: Proceedings of the ACM SIGKDD, pp. 105\u2013114 (2014)","DOI":"10.1145\/2623330.2623624"},{"key":"7312_CR10","doi-asserted-by":"crossref","unstructured":"Minnen, D., Isbell, C., Essa, I., Starner, T.: Detecting subdimensional motifs: an efficient algorithm for generalized multivariate pattern discovery. In: Proceedings of the IEEE ICDM, pp. 601\u2013606 (2007)","DOI":"10.1109\/ICDM.2007.52"},{"key":"7312_CR11","doi-asserted-by":"crossref","unstructured":"Mueen, A., Keogh, E., Zhu, Q., Cash, S., Westover, B.: Exact discovery of time series motifs. In: Proceedings of the SDM, pp. 473\u2013484 (2009)","DOI":"10.1137\/1.9781611972795.41"},{"key":"7312_CR12","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.bjp.2013.12.020","volume":"61","author":"L Sanchez","year":"2014","unstructured":"Sanchez, L., Mu\u00f1oz, L., Galache, J.A., Sotres, P., Santana, J.R., Gutierrez, V., Ramdhany, R., Gluhak, A., Krco, S., Theodoridis, E.: Smartsantander: IoT experimentation over a smart city testbed. Comput. Netw. 61, 217\u2013238 (2014)","journal-title":"Comput. Netw."},{"key":"7312_CR13","unstructured":"Sasaki, Y., Ishikawa, Y., Fujiwara, Y., Onizuka, M.: Sequenced route query with semantic hierarchy. In: EDBT, pp. 37\u201348 (2018)"},{"issue":"2","key":"7312_CR14","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/s10994-005-5829-2","volume":"58","author":"Y Tanaka","year":"2005","unstructured":"Tanaka, Y., Iwamoto, K., Uehara, K.: Discovery of time-series motif from multi-dimensional data based on MDL principle. Mach. Learn. 58(2), 269\u2013300 (2005)","journal-title":"Mach. Learn."},{"key":"7312_CR15","doi-asserted-by":"crossref","unstructured":"Tanaka, Y., Uehara, K.: Discover motifs in multi-dimensional time-series using the principal component analysis and the MDL principle. In: Proceedings of the Springer MLDM, pp. 252\u2013265 (2003)","DOI":"10.1007\/3-540-45065-3_22"},{"issue":"3\u20134","key":"7312_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\u20134), 347\u2013362 (2015)","journal-title":"Telecommun. Policy"},{"key":"7312_CR17","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 ACM SIGKDD, pp. 1415\u20131424 (2015)","DOI":"10.1145\/2783258.2783394"}],"container-title":["Distributed and Parallel Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10619-020-07312-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10619-020-07312-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10619-020-07312-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T01:34:19Z","timestamp":1632533659000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10619-020-07312-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,25]]},"references-count":17,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["7312"],"URL":"https:\/\/doi.org\/10.1007\/s10619-020-07312-z","relation":{},"ISSN":["0926-8782","1573-7578"],"issn-type":[{"type":"print","value":"0926-8782"},{"type":"electronic","value":"1573-7578"}],"subject":[],"published":{"date-parts":[[2020,9,25]]},"assertion":[{"value":"16 September 2020","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2020","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}