Graph algorithms analyze social networks, traffic routes, and web page links. While single machine algorithms traverse graphs more efficiently, the graph data may exceed a single machine’s memory or storage. This talk will present some common distributed graph algorithms using both MapReduce (Hadoop) and Pregel (Bagel) frameworks. We’ll also discuss each framework’s graph processing strengths and weaknesses.
Speaker: John Hudzina is a Principal Software Engineer with over 15 years of development experience. He currently works for Dynamics Research Corporation (DRC), where he leads a data integration team for a Hadoop-based analytics platform. John is also a PhD Candidate at Nova Southeastern University, where his research focuses on map-reduce workload performance prediction and spot market cloud resources.
Food: We will have pizza and pop, starting at 6:30, first-come, first-serve.
Sponsor: Our food for this meeting is provided by Talent Software http://www.talktotalent.com/