{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:50:44Z","timestamp":1740099044376,"version":"3.37.3"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319780238"},{"type":"electronic","value":"9783319780245"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-78024-5_32","type":"book-chapter","created":{"date-parts":[[2018,3,22]],"date-time":"2018-03-22T08:28:25Z","timestamp":1521707305000},"page":"359-369","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Approach for Detecting Abnormal Parallel Applications Based on Time Series Analysis Methods"],"prefix":"10.1007","author":[{"given":"Denis","family":"Shaykhislamov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vadim","family":"Voevodin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,3,23]]},"reference":[{"key":"32_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/978-3-319-49956-7_24","volume-title":"Algorithms and Architectures for Parallel Processing","author":"D Nikitenko","year":"2016","unstructured":"Nikitenko, D., Stefanov, K., Zhumatiy, S., Voevodin, V., Teplov, A., Shvets, P.: System monitoring-based holistic resource utilization analysis for every user of a large HPC center. In: Carretero, J., Garcia-Blas, J., Gergel, V., Voevodin, V., Meyerov, I., Rico-Gallego, J.A., D\u00edaz-Mart\u00edn, J.C., Alonso, P., Durillo, J., Garcia S\u00e1nchez, J.D., Lastovetsky, A.L., Marozzo, F., Liu, Q., Bhuiyan, Z.A., F\u00fcrlinger, K., Weidendorfer, J., Gracia, J. (eds.) ICA3PP 2016. LNCS, vol. 10049, pp. 305\u2013318. Springer, Cham (2016). \nhttps:\/\/doi.org\/10.1007\/978-3-319-49956-7_24"},{"key":"32_CR2","unstructured":"Nikitenko, D.A., Voevodin Vad, V., Zhumatiy, S.A., Stefanov, K.S., Teplov, A.M., Shvets, P.A.: Supercomputer application integral characteristics analysis for the whole queued job collection of large-scale HPC systems. In: 10th Annual International Scientific Conference on Parallel Computing Technologies, Arkhangelsk, Russian Federation, CEUR Workshop Proceedings, vol. 1576, pp. 20\u201330 (2016)"},{"key":"32_CR3","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/978-3-319-55669-7_27","volume-title":"Supercomputing","author":"D Shaykhislamov","year":"2016","unstructured":"Shaykhislamov, D.: Using machine learning methods to detect applications with abnormal efficiency. In: Voevodin, V., Sobolev, S. (eds.) RuSCDays 2016. CCIS, vol. 687, pp. 345\u2013355. Springer, Cham (2016). \nhttps:\/\/doi.org\/10.1007\/978-3-319-55669-7_27"},{"key":"32_CR4","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. JMLR 12, 2825\u20132830 (2011)","journal-title":"JMLR"},{"key":"32_CR5","doi-asserted-by":"crossref","unstructured":"Pena, E.H.M., de Assis, M.V.O., Proena, M.L.: Anomaly detection using forecasting methods ARIMA and HWDS. In: 32nd International Conference of the Chilean Computer Science Society (SCCC), Temuco, pp. 63\u201366 (2013)","DOI":"10.1109\/SCCC.2013.18"},{"key":"32_CR6","unstructured":"Cheboli, D.: Anomaly detection of time series. Dissertation, University of Minnesota (2010)"},{"key":"32_CR7","unstructured":"Malhotra, P., Vig, L., Shroff, G., Agarwal, P.: Long short term memory networks for anomaly detection in time series. In: European Symposium on Artificial Neural Networks, vol. 23 (2015)"},{"issue":"505","key":"32_CR8","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1080\/01621459.2013.849605","volume":"109","author":"DS Matteson","year":"2014","unstructured":"Matteson, D.S., James, N.A.: A nonparametric approach for multiple change point analysis of multivariate data. J. Am. Stat. Assoc. 109(505), 334\u2013345 (2014)","journal-title":"J. Am. Stat. Assoc."},{"key":"32_CR9","first-page":"5559","volume":"24","author":"L Vostrikova","year":"1981","unstructured":"Vostrikova, L.: Detection disorder in multidimensional random processes. Sov. Math. Dokl. 24, 5559 (1981)","journal-title":"Sov. Math. Dokl."},{"issue":"2","key":"32_CR10","doi-asserted-by":"crossref","first-page":"10341055","DOI":"10.1214\/09-AOAS245","volume":"4","author":"ML Rizzo","year":"2010","unstructured":"Rizzo, M.L., Szkely, G.J.: Disco analysis: a nonparametric extension of analysis of variance. Ann. Appl. Stat. 4(2), 10341055 (2010)","journal-title":"Ann. Appl. Stat."}],"container-title":["Lecture Notes in Computer Science","Parallel Processing and Applied Mathematics"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-78024-5_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,3,22]],"date-time":"2018-03-22T08:40:42Z","timestamp":1521708042000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-78024-5_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319780238","9783319780245"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-78024-5_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]}}}