Compared to the traditional data storing, processing, analyzing and visualization which have been performed, Big data requires evolutionary technologies of massive data processing on distributed and parallel systems, such as Hadoop system. Big data analytic systems, thus, have been popular to derive important decision making in various areas. However, visualization on analytic system faces various limitation due to the huge amount of data. This brings the necessity of interactive visualization techniques beyond the traditional static visualization. R has been used and improved for a big data analysis and mining tool. Also, R is supported with various and abundant packages for different targets with visualization. However interactive visualization packages are not easily found in the market. This paper compares and analyzes interactive web packages with visualization packages for R. This paper also proposes interactive web visualized analysis environment for big data with a combination of interactive web packages and visualization packages. In particular, Big data analysis techniques with sensed data are presented as the result by reflecting the decision view on sensing field.
In this digital age, privacy preservation has attracted much attention, as a huge amount of data are generated from multiple sources and transmitted across the Internet. Several perturbation algorithms have emerged to keep sensitive data hidden behind additive noises. In this paper, a novel un-realization algorithm is developed based on a classification and regression tree (CART). First, the sample dataset was distorted, and the duplicate elements were removed, creating a perturbed dataset and an un-realized dataset. Then, a decision tree was set up by the modified CART algorithm and another by the traditional CART based on the un-realized dataset. Finally, the Gini values of the two trees were compared. If the results are the same, then the privacy of the data is preserved. The proposed algorithm was compared with several traditional un-realization algorithms through experiments. The results show that our algorithm achieved excellent results in Gini value, time complexity and output accuracy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.