Amedeo Pachera

Property graphs are powerful data models, yet they often suffer from inconsistencies caused by violations of integrity constraints. Existing repair strategies are either fully automated—struggling with ambiguous cases that require domain expertise—or human-driven, which lack scalability and overlook budget and user heterogeneity. In this work, we propose a novel approach to property graph repair that integrates automatic inference with user expertise. We introduce a Graph Neural Network (GNN)-based difficulty estimator that jointly captures topological and semantic aspects of violations, enabling the system to distinguish between cases suitable for automatic repair and those requiring human input. Building on this, we design a budgeted, difficulty- and user-aware assignment algorithm that allocates violations to users based on their expertise and capacity while maximizing expected repair quality under resource constraints. We prove the NP-hardness of the problem and provide a Lagrangian relaxation. Extensive experiments demonstrate that our approach significantly improves repair accuracy compared to baselines and substantially reduces the number of involved users compared to both other interactive approaches.

Query languages are the foundations of database teaching and education practices. The broad adoption of graph databases contrasts with the limited research into how they are taught. Contrary to relational databases, graph databases allow navigational queries with higher expressivity and lack an a priori schema. In this article, we design a multi-step exploratory user study investigating these peculiarities and how they impact the student’s learning process. Focusing on the widely used Cypher graph query language, we studied a new taxonomy for classifying the errors around graph queries and conducted an in-depth analysis. Our investigation highlights several learning barriers, including misunderstanding the semantics of language constructs, the confusion between data and schema, and the pattern-matching mechanism. We expect the lessons learned and the derived best teaching practices to have an impact on database education and to influence future generations of students, researchers, and practitioners working on graph database technologies.

Property graphs serve as unifying abstractions for encoding, inspecting, and updating interconnected data with greater expressive power. They are increasingly popular across various application domains involving real users. However, graph data often contains inconsistencies that need proper transformations to address underlying constraint violations and often require specific domain knowledge. In this paper, we propose an interactive and user-centric approach to repair property graphs under denial constraints. Our approach includes a novel theoretical framework comprising a query-based inconsistency detection mechanism, a dependency graph for tracking violations, and an assignment algorithm facilitating multi-user property graph repairs by leveraging independent sets. We evaluate our approach through several experiments on real-world and synthetic datasets, considering different levels of user expertise and comparing against various baselines. Even with multiple non-oracle users, our approach outperforms existing interactive and non-interactive baselines by 30% on average in terms of repair quality. Additionally, we conduct a user study to assess real user performance in property graph repairs.