Data Systems Seminar Series (2021-2022)

Cross-Domain Text-to-SQL Semantic Parsing

Adaptive Join Order Optimization using Search Space Linearization

Join ordering is one of the core problems of query optimization, as differences in join order can affect the execution time of queries by orders of magnitudes. Unfortunately, the problem is NP hard in general, and real-world queries can join hundreds of relations, which makes exact solutions prohibitive expensive. In this talk we show how to tackle the join ordering problem by using a search space linearization technique. This adaptive optimization mechanism allows for a smooth transition from guaranteed optimality to a greedier approach, depending on the size of problem. In practice, a surprisingly large number of queries can be solved optimally or near optimally, with very low optimization times even for hundreds of relations.

Thomas Neumann is a full professor in the Department of Computer Science at the Technical University of Munich. His research interests are in the areas of database systems, query processing, and query optimization. In 2020, he received the Gottfried Wilhelm Leibniz Prize, which is considered the most important research award in Germany.

Basil: Scaling BFT with ACID transactions

The Evolving Face of Misinformation in Text, Image, and Video Content

The computer vision community has created a technology that, unfortunately, is getting more bad press than good. In 2014, the first GANS paper automatically generated very low resolutions of faces of people that never existed from a random latent distribution. Although the technology was impressive because it was automated, it was nowhere near as good as what could be done with the simple photo editor. In the same year, DARPA started the media forensics program to combat the proliferation of edited images and videos generated by our adversaries. Although DARPA envisioned the development of automated technologies, no one thought they would evolve so fast. Five years later, the technology has progressed to the point where even a novice can modify full videos, i.e., DeepFakes, and generate new content of people and scenes that never existed overnight using commodity hardware. Recently the US government has become increasingly concerned about the real dangers of using “DeepFakes” technologies from both national security and misinformation points of view. To this end, academia, industry, and the government needs to come together to apply technologies,  develop policies that put pressure on service providers, and educate the public before we get to the point where “seeing is believing” is a thing of the past.

In this talk, I will cover some of the primary efforts in applying counter manipulation detection technology.  I will discuss how we are extending existing technology to deal with the problems of detecting GAN generated content and highlighting inconsistencies between the text, audio, image, and video content in heterogeneous media “assets.”