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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Synthetic Data Generation For The Internet Of Things, Jason Anderson, K. E. Kennedy, Linh B. Ngo, Andre Luckow, Amy Apon
Synthetic Data Generation For The Internet Of Things, Jason Anderson, K. E. Kennedy, Linh B. Ngo, Andre Luckow, Amy Apon
Publications
The concept of Internet of Things (IoT) is rapidly moving from a vision to being pervasive in our everyday lives. This can be observed in the integration of connected sensors from a multitude of devices such as mobile phones, healthcare equipment, and vehicles. There is a need for the development of infrastructure support and analytical tools to handle IoT data, which are naturally big and complex. But, research on IoT data can be constrained by concerns about the release of privately owned data. In this paper, we present the design and implementation results of a synthetic IoT data generation framework. …
Teaching Hdfs/Mapreduce Systems Concepts To Undergraduates, Linh B. Ngo, Amy W. Apon, Edward B. Duffy
Teaching Hdfs/Mapreduce Systems Concepts To Undergraduates, Linh B. Ngo, Amy W. Apon, Edward B. Duffy
Publications
This paper presents the development of a Hadoop MapReduce module that has been taught in a course in distributed computing to upper undergraduate computer science students at Clemson University. The paper describes our teaching experiences and the feedback from the students over several semesters that have helped to shape the course. We provide suggested best practices for lecture materials, the computing platform, and the teaching methods. In addition, the computing platform and teaching methods can be extended to accommodate emerging technologies and modules for related courses.
Assessing The Effect Of High Performance Computing Capabilities On Academic Research Output, Amy Apon, Linh B. Ngo, Michael E. Payne, Paul W. Wilson
Assessing The Effect Of High Performance Computing Capabilities On Academic Research Output, Amy Apon, Linh B. Ngo, Michael E. Payne, Paul W. Wilson
Publications
This paper uses nonparametric methods and some new results on hypothesis testing with nonparametric efficiency estimators and applies these to analyze the effect of locally-available high performance computing (HPC) resources on universities efficiency in producing research and other outputs. We find that locally-available HPC resources enhance the technical efficiency of research output in Chemistry, Civil Engineering, Physics, and History, but not in Computer Science, Economics, nor English; we find mixed results for Biology. Out research results provide a critical first step in a quantitative economic model for investments in HPC.