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IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

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IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis.

Hsien-Tsung Chang¹, Nilamadhab Mishra¹, Chung-Chih Lin¹

¹Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan, ROC.

|November 25, 2015

Summary

This summary is machine-generated.

This study introduces a new framework for analyzing and clustering complex knowledge from Internet of Things (IoT) big data. The proposed methods enhance business intelligence by extracting valuable insights from large IoT datasets.

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Area of Science:

Computer Science
Data Science
Artificial Intelligence

Background:

The proliferation of Internet of Things (IoT) devices generates massive datasets.
Analyzing and clustering complex knowledge granules within this big data is crucial for time-critical applications.
Existing methods for knowledge granule analysis in IoT environments require enhancement for complex data structures.

Purpose of the Study:

To inspect the structural analysis and clustering of complex knowledge granules in an IoT big-data environment.
To propose a novel framework for knowledge granule analytic and clustering (KGAC) tailored for IoT big data.
To enable effective business intelligence (BI) applications through advanced data analysis.

Main Methods:

Development of a knowledge granule analytic and clustering (KGAC) framework.
Implementation of a neuro-fuzzy analytic architecture for discovering complex knowledge granules.
Utilization of an enhanced knowledge granule clustering (e-KGC) mechanism for elastic assembly of granules from IoT big data.

Main Results:

The proposed KGAC framework effectively explores and assembles knowledge granules from IoT big data arrays.
The neuro-fuzzy architecture successfully discovers complex knowledge granules.
The e-KGC mechanism demonstrates superior elasticity in assembling tactical and explicit complex knowledge granules.

Conclusions:

The developed framework and mechanism can extract valuable knowledge granules from IoT big data.
Extracted knowledge is of strategic value to executives for informed decision-making.
The approach facilitates further business intelligence actions by knowledge users.