Journal
SOFT COMPUTING
Volume 24, Issue 14, Pages 10535-10560Publisher
SPRINGER
DOI: 10.1007/s00500-019-04562-7
Keywords
Internet of things; Time series; Piecewise linear representation; Multi-resolution representation
Categories
Funding
- National Natural Science Foundation of China [61772310, 61702300, 61702302, 61802231]
- Science and Technology Development Funds of Shandong Province [2014GGX101028]
- Project of Qingdao Postdoctoral Applied Research
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The evolution of IoT has increased the popularity of all types of sensing devices in a variety of industrial fields and has resulted in enormous growth in the volume of sensor data. Considering the high volume and dimensionality of sensor data, the ability to perform in-depth data analysis and data mining tasks directly on the raw time series sensor data is limited. To solve this problem, we propose a novel dimensional reduction and multi-resolution representation approach for time series sensor data. This approach utilizes an appropriate number of important data points (IDPs) within a certain time series sensor data to produce a corresponding multi-resolution piecewise linear representation (MPLR), called MPLR-IDP. The results of the theoretical analyses and experiments show that MPLR-IDP can reduce the dimensionality while maintaining the important characteristics of time series data. MPLR-IDP can represent the data in a more flexible way to meet diverse needs of different users.
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