YASA: yet another time series segmentation algorithm for anomaly detection in big data problems

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dc.contributor.author Martí Orosa, Luis
dc.contributor.author Sánchez Pi, Nayat
dc.contributor.author Molina López, José Manuel
dc.contributor.author Bicharra García, Ana Cristina
dc.date.accessioned 2018-02-09T17:10:53Z
dc.date.available 2018-02-09T17:10:53Z
dc.date.issued 2014-06-11
dc.identifier.bibliographicCitation Proceedings of: Polycarpou, Marios, et al. (eds.), (2014). Hybrid Artificial Intelligence Systems: 9th International Conference, HAIS 2014, Salamanca, Spain, June 11-13, 2014: proceedings. Springer, pp. 697-708
dc.identifier.isbn 978-3-319-07616-4
dc.identifier.uri http://hdl.handle.net/10016/26213
dc.description.abstract Time series patterns analysis had recently attracted the attention of the research community for real-world applications. Petroleum industry is one of the application contexts where these problems are present, for instance for anomaly detection. Offshore petroleum platforms rely on heavy turbomachines for its extraction, pumping and generation operations. Frequently, these machines are intensively monitored by hundreds of sensors each, which send measurements with a high frequency to a concentration hub. Handling these data calls for a holistic approach, as sensor data is frequently noisy, unreliable, inconsistent with a priori problem axioms, and of a massive amount. For the anomalies detection problems in turbomachinery, it is essential to segment the dataset available in order to automatically discover the operational regime of the machine in the recent past. In this paper we propose a novel time series segmentation algorithm adaptable to big data problems and that is capable of handling the high volume of data involved in problem contexts. As part of the paper we describe our proposal, analyzing its computational complexity. We also perform empirical studies comparing our algorithm with similar approaches when applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.
dc.format.extent 11
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartofseries Lecture Notes in Computer Science
dc.relation.ispartofseries 8480
dc.rights © Springer International Publishing Switzerland 2014
dc.subject.other Time series segmentation
dc.subject.other Anomaly detection
dc.subject.other Big data
dc.subject.other Oil industry applications
dc.title YASA: yet another time series segmentation algorithm for anomaly detection in big data problems
dc.type bookPart
dc.type conferenceObject
dc.subject.eciencia Informática
dc.subject.eciencia Telecomunicaciones
dc.rights.accessRights openAccess
dc.type.version acceptedVersion
dc.relation.eventdate June 11-13, 2014
dc.relation.eventplace Salamanca, Spain
dc.relation.eventtitle International Conference on Hybrid Artificial Intelligence Systems (HAIS 2014)
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 697
dc.identifier.publicationlastpage 708
dc.identifier.publicationtitle Hybrid Artificial Intelligence Systems: 9th International Conference, HAIS 2014, Salamanca, Spain, June 11-13, 2014: proceedings
dc.identifier.uxxi CC/0000027450
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