An IoT Knowledge Reengineering Framework for Semantic Knowledge Analytics for BI-Services

Nilamadhab Mishra, Hsien Tsung Chang, Chung Chih Lin*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

12 Scopus citations

Abstract

In a progressive business intelligence (BI) environment, IoT knowledge analytics are becoming an increasingly challenging problem because of rapid changes of knowledge context scenarios along with increasing data production scales with business requirements that ultimately transform a working knowledge base into a superseded state. Such a superseded knowledge base lacks adequate knowledge context scenarios, and the semantics, rules, frames, and ontology contents may not meet the latest requirements of contemporary BI-services. Thus, reengineering a superseded knowledge base into a renovated knowledge base system can yield greater business value and is more cost effective and feasible than standardising a new system for the same purpose. Thus, in this work, we propose an IoT knowledge reengineering framework (IKR framework) for implementation in a neurofuzzy system to build, organise, and reuse knowledge to provide BI-services to the things (man, machines, places, and processes) involved in business through the network of IoT objects. The analysis and discussion show that the IKR framework can be well suited to creating improved anticipation in IoT-driven BI-applications.

Original languageEnglish
Article number759428
JournalMathematical Problems in Engineering
Volume2015
DOIs
StatePublished - 2015

Bibliographical note

Publisher Copyright:
� 2015 Nilamadhab Mishra et al.

Fingerprint

Dive into the research topics of 'An IoT Knowledge Reengineering Framework for Semantic Knowledge Analytics for BI-Services'. Together they form a unique fingerprint.

Cite this