PatternTrack: A Visual Pattern Detection Technique for Multidimensional Data

Quan, Li; Chen, Li; Liao, Hongsen; Yong, Jun-Hai

doi:10.1109/csss.2012.343

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…The K-means algorithm has been deployed in their approach. Quan Li et al [13] proposed PatternTrack to detect visual pattern for multidimensional data, they mapped all dimension axes in concentric circles which integrate three level concentric groups: data values, patterns and gradient circles.…”

Section: Related Workmentioning

confidence: 99%

2D approach measuring multidimensional data pattern in big data visualization

Zhang

Huang

2016

2016 IEEE International Conference on Big Data Analysis (ICBDA)

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Abstract-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.

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Section: Related Workmentioning

confidence: 99%

2D approach measuring multidimensional data pattern in big data visualization

Zhang

Huang

2016

2016 IEEE International Conference on Big Data Analysis (ICBDA)

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“…TBVA created tiled heat maps and tiled density strips for Big Data visualization. Quan Li et al [24] proposed PatternTrack to detect visual patterns for multidimensional data, and mapped all dimension axes in concentric circles to integrate three level concentric groups: data values, patterns and gradient circles.…”

Section: Related Workmentioning

confidence: 99%

A New Analytics Model for Large Scale Multidimensional Data Visualization

Zhang

Huang

2015

Cloud Computing and Big Data

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Abstract. With the rise of Big Data, the challenge for modern multidimensional data analysis and visualization is how it grows very quickly in size and complexity. In this paper, we first present a classification method called the 5Ws Dimensions which classifies multidimensional data into the 5Ws definitions. The 5Ws Dimensions can be applied to multiple datasets such as text datasets, audio datasets and video datasets. Second, we establish a Pair-Density model to analyze the data patterns to compare the multidimensional data on the 5Ws patterns. Third, we created two additional parallel axes by using pair-density for visualization. The attributes has been shrunk to reduce data over-crowding in pair-density parallel coordinates. This has achieved more than 80% clutter reduction without the loss of information. The experiment shows that our model can be efficiently used for Big Data analysis and visualization.

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PatternTrack: A Visual Pattern Detection Technique for Multidimensional Data

Cited by 2 publications

References 12 publications

2D approach measuring multidimensional data pattern in big data visualization

2D approach measuring multidimensional data pattern in big data visualization

A New Analytics Model for Large Scale Multidimensional Data Visualization

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