List of Knowledge and Data Engineering IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Knowledge and Data Engineering for the year 2015

1. Knowledge and Data Engineering IEEE 2015 Projects
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List Link : http://kasanpro.com/projects-list/knowledge-and-data-engineering-ieee-2015-projects
Title :Malware Propagation in Large-Scale Networks
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/malware-propagation-large-scale-networks
Abstract : Malware is pervasive in networks, and poses a critical threat to network security. However, we have very
limited understanding of malware behavior in networks to date. In this paper, we investigate how malware propagate
in networks from a global perspective. We formulate the problem, and establish a rigorous two layer epidemic model
for malware propagation from network to network. Based on the proposed model, our analysis indicates that the
distribution of a given malware follows exponential distribution, power law distribution with a short exponential tail, and
power law distribution at its early, late and final stages, respectively. Extensive experiments have been performed
through two real-world global scale malware data sets, and the results confirm our theoretical findings.
Title :Discovery of Ranking Fraud for Mobile Apps
Language : C#
Project Link : http://kasanpro.com/p/c-sharp/ranking-fraud-discovery-mobile-apps
Abstract : Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of
bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App developers to use
shady means, such as inflating their Apps' sales or posting phony App ratings, to commit ranking fraud. While the
importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in
this area. To this end, in this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detection
system for mobile Apps. Specifically, we first propose to accurately locate the ranking fraud by mining the active
periods, namely leading sessions, of mobile Apps. Such leading sessions can be leveraged for detecting the local
anomaly instead of global anomaly of App rankings. Furthermore, we investigate three types of evidences, i.e.,
ranking based evidences, rating based evidences and review based evidences, by modeling Apps' ranking, rating and
review behaviors through statistical hypotheses tests. In addition, we propose an optimization based aggregation
method to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-world
App data collected from the iOS App Store for a long time period. In the experiments, we validate the effectiveness of
the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud
activities.
Title :Towards Effective Bug Triage with Software Data Reduction Techniques
Language : Java
Project Link : http://kasanpro.com/p/java/bug-triage-software-data-reduction-techniques
Abstract : Software companies spend over 45 percent of cost in dealing with software bugs. An inevitable step of
fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost in
manual work, text classification techniques are applied to conduct automatic bug triage. In this paper, we address the
problemof data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data.We combine
instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word
dimension. To determine the order of applying instance selection and feature selection, we extract attributes from
historical bug data sets and build a predictive model for a new bug data set. We empirically investigate the
performance of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse and
Mozilla. The results show that our data reduction can effectively reduce the data scale and improve the accuracy of
bug triage. Our work provides an approach to leveraging techniques on data processing to form reduced and
high-quality bug data in software development and maintenance.
Title :Towards Effective Bug Triage with Software Data Reduction Techniques
Language : C#

2. Project Link : http://kasanpro.com/p/c-sharp/effective-bug-triage-software-data-reduction-techniques
Abstract : Software companies spend over 45 percent of cost in dealing with software bugs. An inevitable step of
fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost in
manual work, text classification techniques are applied to conduct automatic bug triage. In this paper, we address the
problemof data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data.We combine
instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word
dimension. To determine the order of applying instance selection and feature selection, we extract attributes from
historical bug data sets and build a predictive model for a new bug data set. We empirically investigate the
performance of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse and
Mozilla. The results show that our data reduction can effectively reduce the data scale and improve the accuracy of
bug triage. Our work provides an approach to leveraging techniques on data processing to form reduced and
high-quality bug data in software development and maintenance.