P ORTOLAN: a Model-Driven Cartography Framework

Processing large amounts of data to extract useful information is an essential task within companies. To help in this task, visualization techniques have been commonly used due to their capacity to present data in synthesized views, easier to understand and manage. However, achieving the right visualization display for a data set is a complex cartography process that involves several transformation steps to adapt the (domain) data to the (visualization) data format expected by visualization tools. To maximize the benefits of visualization we propose Portolan, a generic model-driven cartography framework that facilitates the discovery of the data to visualize, the specification of view definitions for that data and the transformations to bridge the gap with the visualization tools. Our approach has been implemented on top of the Eclipse EMF modeling framework and validated on three different use cases

The Parthenon, April 8, 2014

The Parthenon, Marshall University’s student newspaper, is published by students Monday through Friday during the regular semester and weekly Thursday during the summer. The editorial staff is responsible for the news and the editorial content

Graph Modelling and Transformation: Theory meets Practice

In this paper, we focus on the role of graphs and graph transformation for four practical application areas from software system development. We present the typical problems in these areas and investigate how the respective systems are modelled by graphs and graph transformation. In particular, we are interested in the usefulness of theoretical graph transformation results and graph transformation tools in order to solve these problems. Finally, we characterize concepts and tool features which are still missing in practice to solve the presented and related problems even better. Keywords: graph modelling, graph transformation, graph transformation tool

Utah State Magazine, Winter 2017

The quarterly magazine for friends and alumni of Utah State University.https://digitalcommons.usu.edu/utahstatemagazine/1104/thumbnail.jp

Montana Kaimin, October 2, 1986

Student newspaper of the University of Montana, Missoula.https://scholarworks.umt.edu/studentnewspaper/8913/thumbnail.jp

Improving the Response Time of M-Learning and Cloud Computing Environments Using a Dominant Firefly Approach

© 2013 IEEE. Mobile learning (m-learning) is a relatively new technology that helps students learn and gain knowledge using the Internet and Cloud computing technologies. Cloud computing is one of the recent advancements in the computing field that makes Internet access easy to end users. Many Cloud services rely on Cloud users for mapping Cloud software using virtualization techniques. Usually, the Cloud users' requests from various terminals will cause heavy traffic or unbalanced loads at the Cloud data centers and associated Cloud servers. Thus, a Cloud load balancer that uses an efficient load balancing technique is needed in all the cloud servers. We propose a new meta-heuristic algorithm, named the dominant firefly algorithm, which optimizes load balancing of tasks among the multiple virtual machines in the Cloud server, thereby improving the response efficiency of Cloud servers that concomitantly enhances the accuracy of m-learning systems. Our methods and findings used to solve load imbalance issues in Cloud servers, which will enhance the experiences of m-learning users. Specifically, our findings such as Cloud-Structured Query Language (SQL), querying mechanism in mobile devices will ensure users receive their m-learning content without delay; additionally, our method will demonstrate that by applying an effective load balancing technique would improve the throughput and the response time in mobile and cloud environments