University of Minnesota
Software Engineering Center

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CodeFreeze 2014 Breakout Sessions


1. MongoDB, by Jeffrey Lemmerman and Matt Chimento of Medtronic
2. Machine Learning with GraphLab at Thomson Reuters by Mike Edwards of Thomson Reuters
3. Riak and Cassandra, by Joel Crabb and Kannan Swaminathan of BestBuy

1. MongoDB
Massive amounts of data are generated during the manufacturing and testing of components at Medtronic. Real-time monitoring and analysis of these datasets is the key to ensuring R&D efficiency and product reliability. An overview of MongoDB and its use for capturing, curating, and analyzing these datasets will be explored.

Jeff Lemmerman is a Sr. Software Engineer and aspiring Data Scientist at the Medtronic Energy and Component Center. Jeff pioneered the use of MongoDB to store battery test data and he is currently working to establish the infrastructure and tools needed to create analysis-ready datasets. Jeff earned a B.S. in Astrophysics and a B.S. in Physics from the University of Minnesota in 2005. While working at Medtronic, Jeff earned his Master of Science in Software Engineering degree from the University of Minnesota in 2012.

Matt Chimento is a Principal Test Engineer and Project Manager at the Medtronic Energy and Component Center. Matt developed the foundational software used to control equipment and collect measurement data for many manufacturing operations and is currently working to improve the infrastructure required to efficiently capture and analyze measurement data. Matt earned a B.S. in Computer Engineering from Kettering University in 2005 and will complete an MBA at the University Of Minnesota Carlson School Of Management in 2014. Back to top.

2. Machine Learning with GraphLab at Thomson Reuters
Big data is the new bacon and graph data wraps it in a layer of dark chocolate. During this session we will discuss graph data, analysis and machine learning at big data scales. How do graph analytical techniques differ from other types of data analysis? What cluster computing platforms are available to scale these algorithms? How well do the associated programming languages and paradigms map to more traditional application development skillsets? In addition to providing an introduction to these topics, I will be sharing the experiences of Thomson Reuters as we’ve applied these ideas to our content and applications.

Mike Edwards is a Principal Architect within the Data Center Architecture team at Thomson Reuters. Mike has been with Thomson Reuters for 23 years, where he has held various engineering and management positions. In his current role, Mike is responsible for Big Data and Analytics initiatives across the company, where he works with emerging technologies and standards that cover the entire stack, from data infrastructure, to data mining algorithms, to data visualization. As part of this work, Mike is leading an R&D project whose mission is to investigate and apply graph-based knowledge representation, machine learning and intelligence amplification to Thomson Reuters content and applications. Mike holds degrees in Psychology and Piano Performance. Back to top.

3. Riak and Cassandra
Corporate use of NoSQL systems has increased dramatically over the last few years. Best Buy has implemented multiple systems using various NoSQL engines. In this presentation, we will take an in depth review of how uses both Cassandra and Riak to solve business problems while meeting the technical demands of one of North America's largest online retailers. We will review two solutions, one using Cassandra and one using Riak. These solutions are highly distributed and highly responsive while serving specific business needs. We will provide an overview of the main lessons learned along with best practices we have developed in our journey.

Joel Crabb is the Chief Architect of, one of the largest eCommerce sites in North America. Joel has been guiding through its eCommerce platform transformation moving to a distributed high scale component based platform. He has worked as an architect and developer of high scale web systems for the last 14 years and worked in the medical device and power systems industries prior to that. In 2009, Joel began introducing Big Data systems to corporate consulting clients; standing up a Hadoop cluster to demonstrate the potential capabilities. Since then he has had an ongoing interest in Big Data/NoSQL systems and is helping implement a number of different NoSQL platforms. Joel has a B.S. in Electrical Engineering from Washington University in St. Louis, an M.S. in Nuclear Engineering from the University of Wisconsin – Madison, and an MBA from the University of Minnesota – Carlson School.

Kannan Swaminathan is a proven leader in eCommerce with a history of co-creating strategy, translating business strategy into solutions that result in dramatically improved revenue, market competitiveness, and return-on investment. He has over 15 years of experience devising plans in support of long-term business goals and implementing solutions primarily in retail and consumer packaged goods industry segments. Back to top.

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