2D Approach Measuring Multidimensional Data Pattern in Big Data Visualization

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of 2016 IEEE International Conference on Big Data Analysis, 2016, pp. 194 - 199 (5)
Issue Date:
2016-03-12
Full metadata record
Files in This Item:
Filename Description Size
A2C3AD7A-17D4-474D-88B3-1CD3A2F1A05D am.pdfAccepted Manuscript Version926.52 kB
Adobe PDF
Big Data, structured and unstructured data, contains millions attributes in multiple dimensions. This has arisen three issues: 1) how to measure the structured and unstructured multidimensional data patterns for Big Data analysis; 2) how to display multidimensional data patterns in normal size of screen; 3) how to optimize the data attributes in Big Data visualization. In this work, we have visual analyzed Big Data variety based on the complexity of multidimensional data. Firstly, we introduce 2D dimension which divided the multidimensional dataset into 2D data pattern subsets, and then establish 2D-Ratio algorithm to measure 2D dimension in multiple data patterns. Second, we create two additional parallel axes by using 2D-Ratio to compare 2D dimensional patterns for visualization. Third, the dimension clustering and shrunk attribute have been introduced in 2D-Ratio parallel coordinates to reduce the data over-crowed. The experiment shows that our model can be efficiently and accurately used for Big Data analysis and visualization.
Please use this identifier to cite or link to this item: