A series of invited talks by younger researchers.
Accelerating Video Database Systems using Emerging Hardware Technologies
Joy Arulraj (Georgia Institute of Technology)
Over the last decade, advances in deep learning have led to a resurgence of interest in automated analysis of videos at scale. This approach poses many challenges, ranging from the high computational overhead associated with deep learning models to the types of queries that the user may ask. In this talk, I will present EVA, an end-to-end video database system that we are developing at Georgia Tech, for tackling these challenges using novel query optimization and machine learning techniques. I will then discuss about opportunities for the community to help accelerate video database systems using their expertise in leveraging emerging hardware technologies.
Joy Arulraj is an Assistant Professor of Computer Science at the Georgia Institute of Technology. His research focuses on developing systems for efficiently and effortlessly querying video datasets by synthesizing techniques from data systems and machine learning. His research has been recognized with the IEEE TCDE Early Career Award (2022) and the ACM SIGMOD Jim Gray Doctoral Dissertation Award (2019). His group is supported by funding from the NSF, Cisco Research, Alibaba, Adobe, and Intel.
What the Primacy of Economics Means for Hardware And Software
Viktor Leis (Friedrich-Alexander-Universität Erlangen-Nürnberg)
Using several historical examples, I will argue that both hardware and software is downstream from economics. Economics has been called "the dismal science" and harsh economic realities can prevent technological breakthroughs. At the same time, however, economic thinking can also help overcome seemingly inescapable tradeoffs that we face when building software systems. It may also be our only hope for managing the proliferation of complex heterogeneous hardware, in particular in the cloud.
Viktor Leis is a Professor for Data Management at Friedrich-Alexander University Erlangen-Nürnberg, Germany. His research revolves around designing high-performance data management systems and includes core database topics such as query processing, query optimization, index structures, and storage. Viktor received his doctoral degree in 2016 from the Technical University of Munich, where he worked on the main-memory database system HyPer. He is the recipient of the ACM SIGMOD dissertation award (2018), the IEEE TCDE Rising Star Award, and four best paper awards at ICDE and SIGMOD.