Data Approximation
Data Approximation is using contextualisation to reduce data in terms of volume, variety, and velocity to deal with computational resource constraints while satisfying the application requirements (e.g., time-bound, accuracy, etc.).[1]
Contextualisation is defined as “the process of filtering, aggregating, and inferring IoT data by using relevant information to the applications using the data”.[2]
Usage
The main advantage of using data approximation is to deal with resource constraints in time-sensitive IoT applications. [1]
References
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- ↑ 1.0 1.1 Yavari, Ali; Korala, Harindu; Georgakopoulos, Dimitrios; Kua, Jonathan; Bagha, Hamid (January 2023). "Sazgar IoT: A Device-Centric IoT Framework and Approximation Technique for Efficient and Scalable IoT Data Processing". Sensors. 23 (11): 5211. Bibcode:2023Senso..23.5211Y. doi:10.3390/s23115211. ISSN 1424-8220. PMC 10255853 Check
|pmc=value (help). PMID 37299938 Check|pmid=value (help). - ↑ "Internet of Things data contextualisation for scalable information processing, security, and privacy". researchrepository.rmit.edu.au. Retrieved 2023-08-02.
