PODS 2023: Tutorials
Tutorial 1: Sampling Big Ideas in Query Optimization
Speaker: Edith Cohen (Google)
Abstract
The use of random sampling can greatly enhance the scalability of complex data analysis tasks. Samples serve as versatile summaries that can be applied directly or integrated as a component in the data analysis process. In this tutorial, I will discuss the design and applications of weighted sampling schemes, focusing on some of my favorite big ideas. The tutorial will particularly emphasize algorithmic simplicity and practicality and the context of streamed or distributed data.
Tutorial 2: A Query Language Perspective on Graph Learning
Speaker: Floris Geerts (University of Antwerp)
Abstract
A key component of graph and relational learning methods is the computation of vector representations of the input graphs or relations. The starting point of this tutorial is that we model this computation as queries, mapping discrete relational objects into the realm of real vectors. We then survey recent works in the machine learning community on the expressive power of graph learning methods from this unifying query language perspective. Here, we consider the expressive power related to separability of inputs and to the approximation power of functions. Finally, we argue that the bridge between graph learning and query languages opens many avenues for further research.
