Research on Methods and Techniques for IoT Big Data Cluster Analysis

Ni, Bin

doi:10.1109/iciscae.2018.8666889

Cited by 10 publications

(2 citation statements)

References 10 publications

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“…Big data modeling Hadoop techniques are used for processing in the experiment. The study is inspired that there is a certain degree of coherence in this form of clustering analysis [42].…”

Section: Literature Reviewmentioning

confidence: 99%

Internet of things and Big Data Analytic: A State of the Art Review

Hasan

2022

JASTT

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Th Connectivity plays a vital role in today's world when it comes to a device or things connection. The Internet of Things (IoT) plays a fundamental role in understanding real-world objects virtually and generating massive amounts of data from and for them. The exponential rise in data consumption and the exploding number of IoT-connected devices are only two indicators of how the expansion of Big-Data (BD) exactly parallels that of the IoT. Concerns of data collecting efficiency, data processing, analytics, and security are not trivial when it comes to managing big data in a constantly growing network. Researchers have looked at the difficulties of implementing the Internet of Things to find solutions to these issues. Despite the abundance of research on big data, analytics, and the Internet of Things, the intersection of these fields opens several prospects for developing big data and analytics in IoT settings. This paper focuses on feasible and ideal solutions for the effective integration of IoT with BD. The challenges of combining IoT and BD were also illustrated by presenting an analysis of this issue to a community of readers.

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“…Big data modeling Hadoop techniques are used for processing in the experiment. The study is inspired that there is a certain degree of coherence in this form of clustering analysis [42].…”

Section: Literature Reviewmentioning

confidence: 99%

Internet of things and Big Data Analytic: A State of the Art Review

Hasan

2022

JASTT

View full text Add to dashboard Cite

show abstract

“…Subsequently, the objects are grouped into different classes. By doing so, different inherent relationships become apparent, which provide valuable information that assists managers in decision making [13]. Alternatively, association mining involves discovering relationships between two items or concepts by first identifying the frequent itemset and then generating rules for them [14].…”

Section: Related Workmentioning

confidence: 99%

Knowledge Extraction and Data Visualization: A Proposed Framework for Secure Decision Making using Data Mining

Alshareef¹,

Majrashi²,

Helal³

et al. 2021

IJACSA

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The decision-making process, promptly on time, is a crucial success factor in large organizations. Generally, the data warehouses of these organizations grow rapidly with the data generated from various business activities. This huge volume of data needs to be analyzed and decisions must be made quickly to meet the market challenges. Accurate knowledge extraction and its visualization from big data can guide decisionmakers to conduct key analysis and make correct predictions. This paper proposes a decision-making framework that not only takes into account knowledge extraction and visualization but also considers the security of the data. The proposed framework uses data mining techniques to extract useful patterns, then, visualizes those patterns for further analysis and decision making. The significance of the proposed framework lies in the mechanism through which it protects the data from intruders. The data is first processed and then stored in an encrypted format on the cloud. When the data is needed for analysis and decision making, a temporary copy of the data is first decrypted, and then important patterns are visualized. The proposed framework will assist managers and other decision-makers to analyze and visualize the data in real-time with an enhanced security mechanism.

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Big Data Performance Analysis on a Hadoop Distributed File System Based on Geometric Data Perturbation Technique

Marichamy

Natarajan

2019

Procedia Computer Science

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Research on Methods and Techniques for IoT Big Data Cluster Analysis

Cited by 10 publications

References 10 publications

Internet of things and Big Data Analytic: A State of the Art Review

Internet of things and Big Data Analytic: A State of the Art Review

Knowledge Extraction and Data Visualization: A Proposed Framework for Secure Decision Making using Data Mining

Big Data Performance Analysis on a Hadoop Distributed File System Based on Geometric Data Perturbation Technique

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