| Matrix profile ii: Exploiting a novel algorithm and gpus to break the one hundred million barrier for time series motifs and joins Y Zhu, Z Zimmerman, NS Senobari, CCM Yeh, G Funning, A Mueen, ... 2016 IEEE 16th international conference on data mining (ICDM), 739-748, 2016 | 298 | 2016 |
| Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile CCM Yeh, Y Zhu, L Ulanova, N Begum, Y Ding, HA Dau, Z Zimmerman, ... Data Mining and Knowledge Discovery 32, 83-123, 2018 | 167 | 2018 |
| Matrix profile XI: SCRIMP++: time series motif discovery at interactive speeds Y Zhu, CCM Yeh, Z Zimmerman, K Kamgar, E Keogh 2018 IEEE international conference on data mining (ICDM), 837-846, 2018 | 152 | 2018 |
| Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond Z Zimmerman, K Kamgar, NS Senobari, B Crites, G Funning, P Brisk, ... Proceedings of the ACM Symposium on Cloud Computing, 74-86, 2019 | 82 | 2019 |
| An open-source compiler and PCB synthesis tool for digital microfluidic biochips D Grissom, C Curtis, S Windh, C Phung, N Kumar, Z Zimmerman, ... INTEGRATION, the VLSI journal 51, 169-193, 2015 | 75 | 2015 |
| Matrix profile xx: Finding and visualizing time series motifs of all lengths using the matrix profile F Madrid, S Imani, R Mercer, Z Zimmerman, N Shakibay, E Keogh 2019 IEEE International conference on big knowledge (ICBK), 175-182, 2019 | 63 | 2019 |
| Super‐efficient cross‐correlation (SEC‐C): A fast matched filtering code suitable for desktop computers NS Senobari, GJ Funning, E Keogh, Y Zhu, CCM Yeh, Z Zimmerman, ... Seismological Research Letters 90 (1), 322-334, 2019 | 47 | 2019 |
| Exploiting a novel algorithm and GPUs to break the ten quadrillion pairwise comparisons barrier for time series motifs and joins Y Zhu, Z Zimmerman, N Shakibay Senobari, CCM Yeh, G Funning, ... Knowledge and Information Systems 54, 203-236, 2018 | 45 | 2018 |
| The Swiss army knife of time series data mining: ten useful things you can do with the matrix profile and ten lines of code Y Zhu, S Gharghabi, DF Silva, HA Dau, CCM Yeh, N Shakibay Senobari, ... Data Mining and Knowledge Discovery 34, 949-979, 2020 | 42 | 2020 |
| Matrix Profile XVIII: Time Series Mining in the Face of Fast Moving Streams using a Learned Approximate Matrix Profile Z Zimmerman, NS Senobari, G Funning, E Papalexakis, S Oymak, P Brisk, ... IEEE International Conference on Data Mining, 2019 | 22 | 2019 |
| PCB escape routing and layer minimization for digital microfluidic biochips J McDaniel, Z Zimmerman, D Grissom, P Brisk IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2016 | 16 | 2016 |
| FA-LAMP: fpga-accelerated learned approximate matrix profile for time series similarity prediction A Kalantar, Z Zimmerman, P Brisk 2021 IEEE 29th Annual International Symposium on Field-Programmable Custom …, 2021 | 12 | 2021 |
| Matrix profile xvii: Indexing the matrix profile to allow arbitrary range queries Y Zhu, CCM Yeh, Z Zimmerman, E Keogh 2020 IEEE 36th International Conference on Data Engineering (ICDE), 1846-1849, 2020 | 12 | 2020 |
| Using the similarity Matrix Profile to investigate foreshock behavior of the 2004 Parkfield earthquake N Shakibay Senobari, G Funning, Z Zimmerman, Y Zhu, E Keogh AGU Fall Meeting Abstracts 2018, S51B-03, 2018 | 9 | 2018 |
| Fpga-based acceleration of time series similarity prediction: From cloud to edge A Kalantar, Z Zimmerman, P Brisk ACM Transactions on Reconfigurable Technology and Systems 16 (1), 1-27, 2022 | 4 | 2022 |
| Matrix profile XXVI: mplots: scaling time series similarity matrices to massive data M Shahcheraghi, R Mercer, JMDA Rodrigues, A Der, HFS Gamboa, ... 2022 IEEE International Conference on Data Mining (ICDM), 1179-1184, 2022 | 4 | 2022 |
| Matrix Profile Index Approximation for Streaming Time Series M Shahcheraghi, T Cappon, S Oymak, E Papalexakis, E Keogh, ... 2021 IEEE International Conference on Big Data (Big Data), 2775-2784, 2021 | 1 | 2021 |
| Matrix Profile Index Prediction for Streaming Time Series M Shahcheraghi, T Cappon, S Oymak, E Papalexakis, E Keogh, ... Workshop on ML for Systems at NeurIPS 2020, 2020 | 1 | 2020 |
| Breaking computational barriers to perform time series pattern mining at scale and at the edge ZP Zimmerman University of California, Riverside, 2019 | 1 | 2019 |
| Introducing Mplots: scaling time series recurrence plots to massive datasets M Shahcheraghi, R Mercer, JMA Rodrigues, A Der, HFS Gamboa, ... Journal of Big Data 11 (1), 96, 2024 | | 2024 |
Eamonn KeoghDistinguished Professor of Computer Science, University of California - RiversideAdresse e-mail validée de cs.ucr.edu
