An Integrated Model for Evaluation of Big Data Interactive Systems Under Uncertainty

Abstract The study aimed to propose a judgment-based evaluation model for usability evaluating of big data interactive system s. Human judgment is associated with uncertainty and gray information. We used the fuzzy technique for integration, summarization, and distance calculation of quality value judgment. The proposed model is an integrated fuzzy Multi Factors Evaluation (MFE) model based on experts’ judgments in HCI, ISPD, and AMLMs. We provided a Fuzzy Inference System (FIS) for scoring usability evaluation metrics in different big data interactive system s. A multi-model big data interactive system is implemented for experimental testing of the model. The achieved results from the proposed model and experimental tests are compared using statistical correlation tests. The results show the ability of the proposed model for usability evaluation of big data interactive systems without the need for conducting empirical tests. It is concluded that applying a dataset in a neuro-FIS and training system cause to produce more than a hundred effective rules. The findings indicate that the proposed model can be applied for big data interactive system evaluation, informative evaluation, and complex empirical tests. Future studies may improve the FIS with the integration of artificial neural networks.