Robust Fuzzy Neuro system for Big Data Analytics

The evaluation of visual analytics (VA) is a challenging field enabling analysts to get insight into diverse data types and formats. It aims at understanding events described by data and supporting the knowledge discovery process by integrating different data analysis methods. Recently, the evolution of intelligent decision support systems has enabled the inductive and predictive approaches of data analysis to make important decisions faster with a higher level of confidence and lower uncertainty. This paper introduces a new and intelligent evaluation method of VA that understands the users’ work as well as the features of their environments including vagueness, uncertainty and ambiguity due to workload. To this end, we apply an adaptive neuro-fuzzy inference system (ANFIS) to get quantitative and qualitative measures and determine the lowest evaluation score with better approximation. By combining fuzzy logic, used to deal with the inaccuracies and uncertainty problems during the evaluation process, and neural network, used to solve the problem of continuous changes in assessment environments with the delivery of adaptive learning content. By using the ANFIS approach that allows accurate prediction of evaluation scores, the proposed method seems more efficient compared to the recent evaluation methodology.

show abstract

An Automatic Generation of Domain Ontologies Based on an MDA Approach to Support Big Data Analytics

Laaz

Wakil

Gotti

et al. 2021

Advances in Systems Analysis, Software Engineering, and High Performance Computing

View full text Add to dashboard Cite

This chapter proposes a new methodology for the automatic generation of domain ontologies to support big data analytics. This method ensures the recommendations of the MDA approach by transforming UML class diagrams to domain ontologies in PSM level through ODM, which is an OMG standard for ontology modeling. In this work, the authors have focused on the model-driven architecture approach as the best solution for representing and generating ontology artifacts in an intuitive way using the UML graphical syntax. The creation of domain ontologies will form the basis for application developers to target business professional context; however, the future of big data will depend on the use of technologies to model ontologies. With that said, this work supports the combination of ontologies and big data approaches as the most efficient way to store, extract, and analyze data. It is shown using the theoretical approach and concrete results obtained after applying the proposed process to an e-learning domain ontology.

show abstract

Robust Fuzzy Neuro system for Big Data Analytics

Cited by 4 publications

References 9 publications

Big Data Analytics Concepts, Technologies Challenges, and Opportunities

Big Data Analytics Concepts, Technologies Challenges, and Opportunities

A Predictive Visual Analytics Evaluation Approach Based on Adaptive Neuro-Fuzzy Inference System

An Automatic Generation of Domain Ontologies Based on an MDA Approach to Support Big Data Analytics

Contact Info

Product

Resources

About