caa a22 4500 445871431 CHVBK 20180317145513.0 cr unu---uuuuu 170323e20110101xx s 000 0 eng 10.1007/s10618-010-0176-8 doi (NATIONALLICENCE)springer-10.1007/s10618-010-0176-8 A disk-aware algorithm for time series motif discovery [Elektronische Daten] [Abdullah Mueen, Eamonn Keogh, Qiang Zhu, Sydney Cash, M. Westover, Nima Bigdely-Shamlo] Time series motifs are sets of very similar subsequences of a long time series. They are of interest in their own right, and are also used as inputs in several higher-level data mining algorithms including classification, clustering, rule-discovery and summarization. In spite of extensive research in recent years, finding time series motifs exactly in massive databases is an open problem. Previous efforts either found approximate motifs or considered relatively small datasets residing in main memory. In this work, we leverage off previous work on pivot-based indexing to introduce a disk-aware algorithm to find time series motifs exactly in multi-gigabyte databases which contain on the order of tens of millions of time series. We have evaluated our algorithm on datasets from diverse areas including medicine, anthropology, computer networking and image processing and show that we can find interesting and meaningful motifs in datasets that are many orders of magnitude larger than anything considered before. The Author(s), 2010 Time series motifs nationallicence Bottom-up search nationallicence Random references nationallicence Pruning nationallicence Mueen Abdullah Department of Computer Science & Engineering, University of California, Riverside, CA, USA aut Keogh Eamonn Department of Computer Science & Engineering, University of California, Riverside, CA, USA aut Zhu Qiang Department of Computer Science & Engineering, University of California, Riverside, CA, USA aut Cash Sydney Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA aut Westover M. Massachusetts General Hospital, Brigham and Women's Hospital, Boston, MA, USA aut Bigdely-Shamlo Nima Swartz Center for Computational Neuroscience, University of California, San Diego, CA, USA aut Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 22/1-2(2011-01-01), 73-105 1384-5810 22:1-2<73 2011 22 10618 https://doi.org/10.1007/s10618-010-0176-8 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10618-010-0176-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Mueen Abdullah Department of Computer Science & Engineering, University of California, Riverside, CA, USA aut NATIONALLICENCE 700 1- Keogh Eamonn Department of Computer Science & Engineering, University of California, Riverside, CA, USA aut NATIONALLICENCE 700 1- Zhu Qiang Department of Computer Science & Engineering, University of California, Riverside, CA, USA aut NATIONALLICENCE 700 1- Cash Sydney Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA aut NATIONALLICENCE 700 1- Westover M. Massachusetts General Hospital, Brigham and Women's Hospital, Boston, MA, USA aut NATIONALLICENCE 700 1- Bigdely-Shamlo Nima Swartz Center for Computational Neuroscience, University of California, San Diego, CA, USA aut NATIONALLICENCE 773 0- Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 22/1-2(2011-01-01), 73-105 1384-5810 22:1-2<73 2011 22 10618 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer