Scalable Systems for Large-Scale Dynamic Connected Data Processing

Abstract: As the proliferation of sensors rapidly make the Internet-of-Things (IoT) a reality, the devices and sensors in this ecosystem—such as smartphones, video cameras, home automation systems and autonomous vehicles—constantly map out the real-world producing unprecedented amounts of connected data that captures complex and diverse relations. Unfortunately, existing big data processing and machine learning frameworks are ill-suited for analyzing such dynamic connected data and face several challenges when employed for this purpose.
 
In this talk, I will present my research that focuses on building scalable systems for dynamic connected data processing. I will discuss simple abstractions that make it easy to operate on such data, efficient data structures for state management, and computation models that reduce redundant work. I will also describe how bridging theory and practice with algorithms and techniques that leverage approximation and streaming theory can significantly speed up computations. The systems I have built achieve over an order of magnitude improvement over the state-of-the-art and are currently under evaluation in the industry for real-world deployments.

Biography: Anand Iyer is a Ph.D. candidate at the University of California, Berkeley advised by Prof. Ion Stoica. His research interest is in systems with a current focus on enabling efficient analysis and machine learning on large-scale dynamic, connected data. He is a recipient of the Best Paper Award at SIGMOD GRADES-NDA 2018 for his work on approximate graph analytics. Before coming to Berkeley, he was a member of the Mobility, Networking and Systems group at Microsoft Research India. He completed his M.S at the University of Texas at Austin.

 

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Media Contact: Anand Sivasubramaniam

 
 

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