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

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increasing data production scales with business requirements that ultimately transform a working knowledge base into a superseded state. Such a superseded knowledge base lacks adequate knowledge context scenarios, and the semantics, rules, frames, and ontology contents may not meet the latest requirements of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 Thus, reengineering a superseded knowledge base into a renovated knowledge base system can yield greater business value and is more cost effective and feasible than standardising a new system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.

Collaborative Intelligent Environment Perception and Mission Control of Scientific Researchers in Semantic Knowledge Framework Based on Complex Theory

“…Second, the semantic knowledge framework can be a form of computer knowledge storage and representation and concept. Finally, the semantic knowledge framework can also be used for knowledge processing, such as identification, reasoning, query, knowledge consistency maintenance, scenario calculus, and planning so as to achieve knowledge fusion, knowledge extraction, knowledge discovery, natural language generation, and other functions [15,16]. Semantic knowledge framework can be used to describe any complex relationship between anything, but this description is based on a series of basic semantic relations.…”

Design Methodology of Microservices to Support Predictive Analytics for IoT Applications

“…However, the authors did not elaborate on the detailed mechanisms and they did not consider approaches to deal with heterogeneous data. Further works include some examples from industry such as in [21] authors have proposed a knowledge-based analytics and clustering framework for BI applications and authors in [22] have proposed a knowledge reengineering framework on IoT analytics for BI services.…”

Internet of Everything Advancement Study in Data Science and Knowledge Analytic Streams

“…In 1989-1996, more tasks/terms are included in data science, i.e. data classification, data mining, and knowledge discovery [6]. In 1997-2001, statistical computing is included as a part of data science.…”