PERFORMANCE ANALYSIS OF HYBRID CLASSIFICATION SYSTEM MODEL FOR BIG DATA STREAM USING INTERNET OF THINGS

2021 ◽  
Vol 11 (3/4) ◽  
pp. 1
Author(s):  
Gayathri Devi N ◽  
Manikandan K
Keyword(s):  
Big Data ◽  
Performance Analysis ◽  
Internet Of Things ◽  
Classification System ◽  
Data Stream ◽  
System Model ◽  
Hybrid Classification

scholarly journals Self-Adaptive Pre-Processing Methodology for Big Data Stream Mining in Internet of Things Environmental Sensor Monitoring

Symmetry ◽  
2017 ◽  
Vol 9 (10) ◽  
pp. 244 ◽  
Author(s):  
Kun Lan ◽  
Simon Fong ◽  
Wei Song ◽  
Athanasios Vasilakos ◽  
Richard Millham
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Stream ◽  
Data Stream Mining ◽  
Stream Mining ◽  
Environmental Sensor ◽  
Sensor Monitoring ◽  
Self Adaptive

scholarly journals 5G-Oriented IoT Big Data Analysis Method System

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lei Hu ◽  
Xianling Xia
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Collection ◽  
Internet Of Things ◽  
Big Data Analysis ◽  
System Model ◽  
Analysis Method ◽  
Data Environment ◽  
Internet Of Things Technology ◽  
And Storage

The application degree and application scope of 5G Internet of Things technology and big data analysis technology are becoming wider and wider, bringing opportunities for the development of traditional enterprises and providing technological innovation support for the development of new enterprises. Based on 5G Internet of Things technology and big data technology, this paper designs and studies an intelligent agricultural monitoring platform. We collect crop growth data and monitor crop growth status through this platform to study the 5G-oriented IoT big data analysis method system. This paper studies the data collection and storage issues involved in the huge agricultural IoT data environment. This article analyzes the specific sources of agricultural big data, the specific methods of data collection, and the methods of various database storage technologies. Combining wireless sensor network technology, large-source data processing technology, and distributed data storage technology, a method is proposed to solve the problem of rural Internet data collection and storage in the big data environment. This paper proposes a spatiotemporal block processing TSBPS to store the first detection data. The method uses spatiotemporal preblocking, data compression, and caching to significantly improve the recording speed of near real-time storage and microdetection data. In the experimental part of this article, experiments are carried out on the key parts of the IOT-HSQM system model that may limit storage or query performance. Experimental results show that this article compares TSBPS and direct writing methods. The maximum write speed increased by 79%, and the average write speed increased by 42%. The IOT-HSQM system model can meet the requirements of compiling and query performance and statistical analysis.

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