Computation Frameworks in Modern Graph Database Engines
Abstract: After being introduced decades ago in academic research, graph database systems have recently made a strong comeback fueled by industrial demand. Logically, they return to classic database foundations like the ER model, whose entities now correspond to graph vertices, and whose binary relationships are modeled as edges. Computationally, however, the landscape is new and exciting, as the advent of massively parallel and cloud computing is facilitating unprecedented scale-out and commercial adoption. This talk will discuss developments in the computation models underlying existing graph database engines, as well as their connection to the surface syntax of high-level declarative query languages (such as the upcoming ISO/ANSI GQL graph query language standard).
Bio: Alin Deutsch is a full professor of Computer Science and Engineering at UC San Diego, where he also serves as faculty director of the Masters of Advanced Study program in Data Science and Engineering. His research interests include query language design and optimization for various data models ranging from text to the relational and post-relational models (with particular emphasis on graph data). He has also worked on cross-model data integration and on automatic verification of business processes. He earned his PhD in Computer Science from the University of Pennsylvania, an MSc degree from the Technical University of Darmstadt (Germany) and a BSc degree from the Polytechnic University Bucharest (Romania). He is the recipient of the 2019 ICDT Test of Time Award, the 2018 Alberto Mendelzon ACM PODS Test of Time Award, the ACM SIGMOD 2006 Top-3 Best Paper Award, a Jean D’Alembert Excellence Fellowship from the University Paris-Saclay, the Alfred P. Sloan Fellowship, and an NSF CAREER award.