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1. Matrix Profile XVIII: Time Series Mining in the Face of Fast Moving Streams using a Learned Approximate Matrix Profile

   https://doi.org/10.1109/ICDM.2019.00104  

   Zimmerman, Zachary ; Shakibay Senobari, Nader ; Funning, Gareth ; Papalexakis, Evangelos ; Oymak, Samet ; Brisk, Philip ; Keogh, Eamonn ( November 2019 , International Conference on Data Mining)
2. Matrix profile xxiii: Contrast profile: A novel time series primitive that allows real world classification

   https://doi.org/10.1109/ICDM51629.2021.00151  

   Mercer, R ; Alaee, S ; Abdoli, A ; Singh, S ; Murillo, A ; Keogh, E. ( December 2021 , IEEE International Conference on Data Mining workshops)
3. FA-LAMP: FPGA-Accelerated Learned Approximate Matrix Profile for Time Series Similarity Prediction

   https://doi.org/10.1109/FCCM51124.2021.00013  

   Kalantar, Amin ; Zimmerman, Zachary ; Brisk, Philip ( May 2021 , International Symposium on Field-Programmable Custom Computing Machines)

   null (Ed.)

   With the proliferation of low-cost sensors and the Internet-of-Things (IoT), the rate of producing data far exceeds the compute and storage capabilities of today’s infrastructure. Much of this data takes the form of time series, and in response, there has been increasing interest in the creation of time series archives in the last decade, along with the development and deployment of novel analysis methods to process the data. The general strategy has been to apply a plurality of similarity search mechanisms to various subsets and subsequences of time series data in order to identify repeated patterns and anomalies; however, the computational demands of these approaches renders them incompatible with today’s power-constrained embedded CPUs. To address this challenge, we present FA-LAMP, an FPGA-accelerated implementation of the Learned Approximate Matrix Profile (LAMP) algorithm, which predicts the correlation between streaming data sampled in real-time and a representative time series dataset used for training. FA-LAMP lends itself as a real-time solution for time series analysis problems such as classification and anomaly detection, among others. FA-LAMP provides a mechanism to integrate accelerated computation as close as possible to IoT sensors, thereby eliminating the need to transmit and store data in the cloud for posterior analysis. At its core, LAMP and FA-LAMP employ Convolution Neural Networks (CNNs) to perform prediction. This work investigates the challenges and limitations of deploying CNNs on FPGAs when using state-of-the-art commercially-supported frameworks built for this purpose, namely, the Xilinx Deep Learning Processor Unit (DPU) overlay and the Vitis AI development environment. This work exposes several technical limitations of the DPU, while providing a mechanism to overcome these limits by attaching our own hand-optimized IP block accelerators to the DPU overlay. We evaluate FA-LAMP using a low-cost Xilinx Ultra96-V2 FPGA, demonstrating performance and energy improvements of more than an order of magnitude compared to a prototypical LAMP deployment running on a Raspberry Pi 3. Our implementation is publicly available at https://github.com/fccm2021sub/fccm-lamp. 

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4. Matrix Profile XIV: Scaling Time Series Motif Discovery with GPUs to Break a Quintillion Pairwise Comparisons a Day and Beyond

   https://doi.org/10.1145/3357223.3362721  

   Zimmerman, Zachary ; Kamgar, Kaveh ; Senobari, Nader Shakibay ; Crites, Brian ; Funning, Gareth ; Brisk, Philip ; Keogh, Eamonn ( November 2019 , ACM Symposium on Cloud Computing)
5. Matrix Profile XVI: Efficient and Effective Labeling of Massive Time Series Archives

   https://doi.org/10.1109/DSAA.2019.00061  

   Madrid, Frank ; Singh, Shailendra ; Chesnais, Quentin ; Mauck, Kerry ; Keogh, Eamonn ( October 2019 , 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA))