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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
portfolio
publications
Towards User-Centric Graph Repairs
IEEE 40th International Conference on Data Engineering Workshops (ICDEW), 2024
An early vision and problem formulation for user-centric, interactive repair of inconsistent graph data, combining automatic inference with human expertise.
Recommended citation: A. Pachera, A. Bonifati and A. Mauri, "Towards User-Centric Graph Repairs," in 2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW), Utrecht, Netherlands, 2024, pp. 375-376, doi: 10.1109/ICDEW61823.2024.00057.
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Understanding Students’ Errors in Graph Query Formulation
ACM Transactions on Computing Education (ToCE), 2025
An empirical study of the common errors students make when formulating graph queries, providing insights for the design of graph database education tools.
Recommended citation: Understanding Students' Errors in Graph Query Formulation. ACM Transactions on Computing Education (ToCE), 2025. DOI: 10.1145/3743687
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Grafixer: Enabling User-Centric Repairs for Property Graphs
PACMMOD — Demonstration, SIGMOD 2025
An interactive tool for human-in-the-loop repair of property graphs. Users upload datasets, define constraints in Cypher, and collaboratively correct inconsistencies; administrators monitor real-time statistics and oversee the repair process through an interactive dashboard.
Recommended citation: A. Pachera, A. Bonifati, A. Mauri. Grafixer: Enabling User-Centric Repairs for Property Graphs. PACMMOD Demonstration, SIGMOD 2025. DOI: 10.1145/3722212.3725105
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User-Centric Property Graph Repairs
Proceedings of the ACM on Management of Data (PACMMOD), SIGMOD 2025
An interactive, user-centric approach to repairing property graphs under denial constraints. It introduces a query-based inconsistency detection mechanism, a dependency graph for tracking violations, and an assignment algorithm enabling multi-user repairs via independent sets — outperforming interactive and non-interactive baselines by 30% on average, validated with a user study.
Recommended citation: A. Pachera, A. Bonifati, A. Mauri. User-Centric Property Graph Repairs. Proceedings of the ACM on Management of Data (PACMMOD), 2025. DOI: 10.1145/3709735
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WhatIf: Causal Analysis with Graph Databases
Proceedings of the VLDB Endowment (PVLDB), 2025
We propose graph-based techniques for interactive causal analysis over property graph databases, enabling efficient exploration of causal relationships in complex, interconnected data.
Recommended citation: A. Pachera, A. Bonifati, A. Mauri. WhatIf: Causal Analysis with Graph Databases. Proceedings of the VLDB Endowment (PVLDB), 2025. DOI: 10.14778/3749646.3749671
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Budgeted Interactive Property Graph Repair with Graph Neural Networks
ACM SIGMOD 2027 (To Appear)
A Graph Neural Network–based framework for interactive property graph repair that combines representation learning, optimization, and human expertise under budget constraints. The model learns to estimate repair difficulty and decides, within a fixed budget, which repairs to automate and which to route to human experts.
Recommended citation: A. Pachera, A. Bonifati, A. Mauri. Budgeted Interactive Property Graph Repair with Graph Neural Networks. ACM SIGMOD 2027 (to appear).
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talks
Improving Data Quality and Reasoning in Graph Databases: Human-in-the-Loop, Machine Learning, and Causal Approaches
Published:
Research seminar given during my visiting period at the University of British Columbia, hosted by Prof. Laks V. S. Lakshmanan. The talk surveyed my work on improving data quality and reasoning in graph databases, spanning human-in-the-loop repair, graph neural networks, and causal analysis over property graphs.
teaching
Bases de données avancées [FR]
Workshop in Undergraduate Program · Université Claude Bernard Lyon 1, Math Department · 2024
Laboratory of Data Processing and Analytics
Workshop in Master Program · Université Claude Bernard Lyon 1, Math Department · 2024
The objective of this course is to introduce students to the principles and methods of advanced data management and processing. The course covered the techniques of storing, pre-processing and processing different types of data (structured, semi-structured and unstructured).
Laboratory of Soft Skill 1
Workshop in Master Program · Université Claude Bernard Lyon 1, Math Department · 2024
Available projects :
Foundations of Prompt Engineering
Lecturer · Bocconi University · 2025
Invited lecturer for a module on the Foundations of Prompt Engineering, introducing students to large language models and effective prompting strategies for real-world tasks.