Graphs are data model abstractions that are becoming pervasive in several real-life applications and use cases. In these settings, users primarily focus on entities and their relationships, further enhanced with multiple labels and properties to form the so-called property graphs. Modern big graph processing systems need to keep pace with the increasing fundamental requirements of these applications and to tackle unforeseen challenges. Motivated by our community-wide vision on future graph processing systems, in this talk I will present the system challenges that are lying behind big graph processing and analytics research areas. Many current graph query engines only support subsets of graph queries that they can efficiently evaluate, thus disregarding more expressive query fragments on top of property graphs. It becomes crucial to address efficient query evaluation for complex graph queries, as well the extensibility of the underlying graph query and constraint languages and the support of property graph schemas. Moreover, the dynamic aspects of query evaluation on streaming graphs are equally important components of big graph ecosystems and require design and benchmarking efforts. During the talk, I intend to touch upon our work on these topics and to pinpoint the research directions and open problems for big graph processing systems.
Speaker
Angela Bonifati is a Professor of Computer Science at Lyon 1 University and at the CNRS Liris research lab, where she leads the Database Group. In 2019 and 2020, she was on leave at INRIA. Prior to that, she was working as a Professor at Lille 1 University (2011-2015) and as a researcher at CNR, Italy until 2011. She received her Ph.D. from Politecnico di Milano in 2002. Her current research interests are on the interplay between relational and graph-oriented data paradigms, particularly query processing, data integration and learning for both structured and unstructured data models. She is involved in several grants at Lyon 1 University, including French, EU H2020 and industrial grants. She has also co-authored more than 150 publications in top venues of the data management field along with two books (edited by Springer in 2011 and Morgan Claypool in 2018) and an invited paper in ACM Sigmod Record 2018. She is the Program Chair of ACM Sigmod 2022 and an Associate Editor for both Proceedings of VLDB and IEEE ICDE. She is an Associate Editor for the VLDB Journal, ACM TODS, Distributed and Parallel Databases and Frontiers in Big Data. She is currently the President of the EDBT Executive Board and a member of the ICDT council. She holds many visiting scholar positions in foreign universities in both Europe and North America. Since 2020, she is also Adjunct Professor at the University of Waterloo in Canada.
Chao Zhang is a postdoc with the CNRS Liris research lab at Lyon 1 University. He received his Ph.D. in computer science from the University of Clermont Auvergne, France in 2019. His research interests include graph query processing, stream processing, and query rewriting. He is a program committee member of VLDB 2023, and SIGMOD 2022.