Angela Bonifati

Title: Property Graph Transformations in Action: From Data Integration to Causal Analysis

Abstract: Property graphs are key components of modern graph databases and graph analytics systems. They support highly expressive data models consisting of multi-labeled nodes and edges, together with properties represented as key-value pairs. Property graphs serve as versatile data integration paradigms, enabling data in virtually any format to be seamlessly transformed into this model. Moreover, they are at the core of an active standardization effort led by ISO/IEC, which aims to establish standardized declarative graph query languages such as GQL and SQL/PGQ. In addition to these data manipulation language standards, complementary languages for property graph schemas and constraints are emerging as part of future data definition languages. In this talk, I will present novel declarative paradigms for expressing property graph transformations that support both graph-based data integration and data cleaning tasks. Beyond being declarative, these transformations are designed to achieve efficiency and scalability. Furthermore, they are sufficiently flexible to be applied in other contexts, such as causal inference and causal analysis, where declarative graph languages enable complex, path-based causal operations.