Name
Abstract | ||
|---|---|---|
In this paper, we present a novel visualization technique for assisting the observation and analysis of algorithmic complexity. In comparison with conventional line graphs, this new technique is not sensitive to the units of measurement, allowing multivariate data series of different physical qualities (e.g., time, space and energy) to be juxtaposed together conveniently and consistently. It supports multivariate visualization as well as uncertainty visualization. It enables users to focus on algorithm categorization by complexity classes, while reducing visual impact caused by constants and algorithmic components that are insignificant to complexity analysis. It provides an effective means for observing the algorithmic complexity of programs with a mixture of algorithms and black-box software through visualization. Through two case studies, we demonstrate the effectiveness of complexity plots in complexity analysis in research, education and application. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1111/cgf.12098 | Comput. Graph. Forum |
Keywords | DocType | Volume |
complexity analysis,uncertainty visualization,complexity plot,algorithmic component,complexity class,multivariate data series,algorithmic complexity,novel visualization technique,multivariate visualization,new technique | Journal | 32 |
Issue | Citations | PageRank |
3 | 2 | 0.38 |
References | Authors | |
14 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeyarajan Thiyagalingam | 1 | 126 | 16.68 |
| Simon Walton | 2 | 19 | 2.30 |
| Brian Duffy | 3 | 52 | 3.99 |
| Anne Trefethen | 4 | 407 | 20.46 |
| Min Chen | 5 | 1293 | 82.69 |