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The Seventh Annual Tom Tom Founders Festival is a week-long celebration of innovators, visionaries, and artists who are shaping small cities. The Festival occurs at dozens of venues throughout downtown Charlottesville. 

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Thursday, April 12 • 4:30pm - 4:35pm
Comparing Baseline Statistical Control to Machine Learning Models in Evaluating Performance in IoT Systems Now Open

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Internet-enabled devices and the Internet of Things (IoT) will continue to become an important component of networked computing systems. These systems leverage “big data” processes that collect, clean, analyze and model large data streams. This project demonstrates techniques and strategies in maintaining performance for IoT systems. Performance is vital to creating reliable data analytics from the edge to the back office. As IoT become more real-time, having reliable performance in the architecture will require near real-time analysis of dependencies for utilization, throughput, resources and access. Establishing baseline performance will become a part of service level agreements (SLAs) of “Big Data” systems. Statistical process control (SPC) and quality improvement in engineering systems that use computing infrastructure have been an industry standard for decades. It has not been, until recently, that such processes were integrated with data processing systems and IP networks. Machine learning (ML) can be used to find anomalies and patterns in the performance of IoT systems by using large datasets to learn from and predict events. The purpose of this project is to compare these two strategies qualitatively and quantitatively while providing guidance for IoT system optimization and monitoring. Business applications for this project include hardware sizing, system health checks, and baseline performance monitoring. The goal is to perform analysis on a series of metrics across multiple layers in an IoT architecture. The Open Systems Interconnection model (OSI model) of IoT will serve as the dichotomy of a baseline performance model. Quantitative and qualitative results will allow businesses to determine scale, performance, accessibility and availability of these systems.

avatar for Derek Moore

Derek Moore

Senior Technical Consultant, Independent IT Researcher
Derek Moore is an IT professional with experience in software and services in utilities, telecommunication, manufacturing and energy. A subject matter expert in system analysis and systems engineering, who has successfully implemented enterprise solutions for multinational and national... Read More →

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Capital One

Capital One is a diversified bank that offers a broad array of financial products and service to consumers, small businesses and commercial clients. 
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S&P Global

S&P Global Inc. (prior to April 2016 McGraw Hill Financial, Inc., and prior to 2013 McGraw Hill Companies) is an American publicly traded corporation headquartered in New York City. Its primary areas of business are financial information and analytics. It is the parent company of... Read More →

Thursday April 12, 2018 4:30pm - 4:35pm EDT
Violet Crown - Theater 5
  Machine Learning, Summit
  • Festival (FREE), Included in Summit Badge, or Individually Ticketed Summit