Research Article | OPEN ACCESS
Research on Incomplete Transaction Footprints in Networked Software
1Junfeng Man, 2Cheng Peng, 3Qianqian Li and 1Changyun Li
1College of Computer and Communication, Hunan University of Technology, Zhuzhou 412007
2School of Information Science and Engineering, Central South University, Changsha 410083
3Department of Information and Engineer, Hunan Chemical Vocation Technology College, Zhuzhou 412004, China
Research Journal of Applied Sciences, Engineering and Technology 2013 24:5561-5565
Received: September 30, 2012 | Accepted: December 12, 2012 | Published: May 30, 2013
Abstract
In networked software, interactive behaviors of software entities generate lots of behavioral footprints, some of them may lose tokens or tokens are useless. The paper studies the constructing process of State Transition Model (STM) in which the process of incomplete transactions are satisfied with Markov property, it is pointed that the STM are originally extracted from behavior log files generated by the runtime behaviors of networked software. The contributions of this paper are to mark the partly tokenized behavior footprints in the STM through Maximum Flow (MF) algorithm, then find the original source for each behavior footprint. The experiment results indicate that the maximum flow algorithm can accurately turn the partly tokenized behavior into complete footprint sequences.
Keywords:
Behavior footprints, incomplete transaction maximum flow algorithm, networked software, state transition model,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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