Open Access
ARTICLE
Expanding Hot Code Path for Data Cleaning on Software Graph
Guang Sun1, 2, *, Xiaoping Fan1, Wangdong Jiang1, Hangjun Zhou1, Fenghua Li1, Rong Yang1
1 Institute of Big Data, Hunan University of Finance and Economics, Changsha, 410205, China.
2 School of Engineering, University of Alabama, Tuscaloosa, 35487, USA.
* Corresponding Author: Guang Sun. Email: .
Computers, Materials & Continua 2020, 63(2), 743-753. https://doi.org/10.32604/cmc.2020.05564
Received 01 January 2019; Accepted 12 April 2019; Issue published 01 May 2020
Abstract
Graph analysis can be done at scale by using Spark GraphX which loading data
into memory and running graph analysis in parallel. In this way, we should take data out of
graph databases and put it into memory. Considering the limitation of memory size, the
premise of accelerating graph analytical process reduces the graph data to a suitable size
without too much loss of similarity to the original graph. This paper presents our method of
data cleaning on the software graph. We use SEQUITUR data compression algorithm to
find out hot code path and store it as a whole paths directed acyclic graph. Hot code path is
inherent regularity of a program. About 10 to 200 hot code path account for 40%-99% of a
program’s execution cost. These hot paths are acyclic contribute more than 0.1%-1.0% of
some execution metric. We expand hot code path to a suitable size which is good for
runtime and keeps similarity to the original graph.
Keywords
Cite This Article
G. Sun, X. Fan, W. Jiang, H. Zhou, F. Li
et al., "Expanding hot code path for data cleaning on software graph,"
Computers, Materials & Continua, vol. 63, no.2, pp. 743–753, 2020. https://doi.org/10.32604/cmc.2020.05564