This paper presents a new interactive scatter plot visualization for multi-dimensional data analysis. We apply Rough Set Theory (RST) to reduce the visual complexity through dimensionality reduction. We use an innovative point-to-region mouse click concept to enable direct interactions with scatter points that are theoretically impossible. To show the decision trend we use a virtual Z dimension to display a set of linear flows showing approximation of the decision trend. We conducted case studies to demonstrate the effectiveness and usefulness of our new technique for analyzing the property of three popular data sets including wine quality, wages and cars. The paper also includes a pilot usability study to evaluate parallel coordinate visualization with scatter plot matrices visualization with RST results.

Computer forensics investigators aim to analyseand present facts through the examination of digital evidences in short times. As the volume of suspicious data is becoming large, the difficulties of catching the digital evidence in a legally acceptable time are high. This paper proposes an effective method for reducing investigation time redundancy to achieve the normalization of data on hard disk drives (HDD) for computer forensics. We use visualization techniques, parallel coordinates, to analyse data instead of using data analysis algorithms only, and also choose a Red-Black tree structure to de-duplicate data. It reduces the time complexity, including the time spent of searching data, adding data as well as deleting data. We show the advantages of our approach; moreover, we demonstrate how this method can enhance the efficiency and quality of computer forensics task.

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