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

• Published in: PloS one Vol. 10; no. 11; p. e0141980
• Main Authors: Chang, Hsien-Tsung, Mishra, Nilamadhab, Lin, Chung-Chih
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.11.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Architectural engineering; Arrays; Artificial intelligence; Artificial Intelligence - trends; Artificial neural networks; Big Data; Cluster Analysis; Clustering; Cognition & reasoning; Computer science; Data analysis; Data management; Data processing; Decision Making; Engineering; Explicit knowledge; Fuzzy logic; Granular materials; Humans; Information management; Information Storage and Retrieval - trends; Information Systems; Intelligence; Intelligence (information); International conferences; Internet; Internet of Things; Knowledge; Knowledge discovery; Machine Learning; Mathematical analysis; Natural language processing; Neural networks; Performance management; Semantics; Structural analysis; Taiwan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0141980

The current rapid growth of Internet of Things (IoT) in various commercial and non-commercial sectors has led to the deposition of large-scale IoT data, of which the time-critical analytic and clustering of knowledge granules represent highly thought-provoking application possibilities. The objective of the present work is to inspect the structural analysis and clustering of complex knowledge granules in an IoT big-data environment. In this work, we propose a knowledge granule analytic and clustering (KGAC) framework that explores and assembles knowledge granules from IoT big-data arrays for a business intelligence (BI) application. Our work implements neuro-fuzzy analytic architecture rather than a standard fuzzified approach to discover the complex knowledge granules. Furthermore, we implement an enhanced knowledge granule clustering (e-KGC) mechanism that is more elastic than previous techniques when assembling the tactical and explicit complex knowledge granules from IoT big-data arrays. The analysis and discussion presented here show that the proposed framework and mechanism can be implemented to extract knowledge granules from an IoT big-data array in such a way as to present knowledge of strategic value to executives and enable knowledge users to perform further BI actions.