Dissertation Talk: Scalable Systems for Large-Scale Dynamic Connected Data Processing

Presentation: Dissertation Talk: EE | May 16 | 9-10 a.m. | 405 Soda Hall

 Anand Padmanabha Iyer

 Electrical Engineering and Computer Sciences (EECS)

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 more than an order of magnitude improvement over the state-of-the-art and are currently under evaluation in the industry for real-world deployments.

 api@eecs.berkeley.edu