Wearable Big Data Pertinence Learning with Deep Spatiotemporal co-Mining
Wearable Big Data Pertinence Learning with Deep Spatiotemporal co-Mining
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DOI：
10.1109/i2mtc48687.2022.9806704
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
J. Wong;Qingxue Zhang
中科院分区：
文献类型：
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作者：
J. Wong;Qingxue Zhang
可穿戴式计算机利用其无处不在的大数据捕获和流处理能力，极大地推进了大数据实践。然而，一个关键的挑战是要传输的数据量，这消耗了电池有限的可穿戴设备的太多能量。针对这一障碍，我们提出了一种新的大数据针对性学习方法，该方法可以学习和提取可穿戴大数据中的相关模式，以减少冗余。具体而言，提出了一种基于卷积自编码器和长短期记忆的混合深度学习方法，该方法可以同时挖掘数据中的空间和时间模式，以提取关键模式。在一个真实世界的运动动力学大数据应用中，所获得的时空共挖掘能力显示了相关性提取和冗余最小化的诱人潜力。这项研究有望极大地推动可穿戴大数据实践。
Wearable Computers are greatly advancing big data practices, by levering their capabilities of ubiquitous big data capturing and streaming. However, one critical challenge is the amount of data to be transmitted, which consumes too much energy of the battery-constrained wearable devices. Targeting this obstacle, we propose a novel big data pertinence learning approach, which can learn and extract pertinent patterns in wearable big data for redundancy reduction. More specifically, a hybrid deep learning approach based on both Convolutional Autoencoder and Long Short-term Memory is proposed, which can mine both spatial and temporal patterns in the data for key pattern extraction. The achieved spatiotemporal co-mining ability when evaluated on a real- world motion dynamics big data application, demonstrates the attractive potential of pertinence extraction and redundancy minimization. This study is expected to greatly advance wearable big data practices.