Novel Method for Speeding Up Time Series Processing in Smart City Applications
نویسندگان
چکیده
The huge amount of daily generated data in smart cities has called for more effective storage, processing, and analysis technologies. A significant part this are streaming (i.e., time series data). Time similarity or dissimilarity measuring represents an essential critical task several mining machine learning algorithms. Consequently, a distance measure that can extract the similarities differences among precise way highly increase efficiency processes. This paper proposes novel elastic to how much is dissimilar from another. proposed based on Adaptive Simulated Annealing Representation (ASAR) approach Based Distance Measure (ASAR-Distance). ASAR-Distance adapts ASAR include information about shape by including additional slopes local trends. slope information, together with magnitude used calculate new definition combines Manhattan, Cosine, Dynamic Warping measures. experimental results have shown able overcome limitations handling time-shifting, reading trends precisely, inherited high computational complexity traditional
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ژورنال
عنوان ژورنال: Smart cities
سال: 2022
ISSN: ['2624-6511']
DOI: https://doi.org/10.3390/smartcities5030048