Phd Position F/m Decentralized Data Sharing with Hybrid Graph-relational Views

Le descriptif de l’offre ci-dessous est en Anglais_

**Type de contrat**: CDD

**Niveau de diplôme exigé**: Bac + 5 ou équivalent

**Fonction**: Doctorant

**Contexte et atouts du poste**:
This Phd position will be in the context of IPCEI-CIS (Important Project of Common European Interest - Next Generation Cloud Infrastructure and Services) DXP (Data Exchange Platform) project involving Amadeus and three Inria research teams (Loreley, CEDAR and MAGELLAN). This project aims to design and develop an open-source management solution for a federated and distributed data exchange platform (DXP), operating in an open, scalable, and massively distributed environment (cloud-edge continuum).

The PhD will be located at The Inria Center of the University of Lorraine in the Loreley team. It will be supervised by Claudia-Lavinia Ignat, Research Director at Inria in Nancy and Stefania Dumbrava, Assistant Professor at ENSIIE/Inria Paris/Télécom Sud-Paris.

The Inria Center of the University of Lorraine is one of Inria's nine centers and has twenty project teams, located in Nancy, Strasbourg and Saarbrücken. Its activities occupy over 400 people, scientists and research and innovation support staff, including 45 different nationalities. The Inria Center is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institutes, etc.

**Mission confiée**:
**Context and Motivation**:
Modern data management is increasingly shaped by distribution, heterogeneity, and collaboration. Contemporary ecosystems, from scientific knowledge graphs and industrial digital twins to cross-organizational analytics platforms, must manage continuously evolving, jointly produced data shared across autonomous participants. This data often spans clouds, edges, and federated infrastructures and evolves without centralized coordination.

At the same time, data models are converging. The recent SQL/PGQ standard unifies relational querying with graph pattern matching, enabling property graphs ([Angles18]) to be defined as views over relational data. Systems such as Oracle PGQL, Google Cloud Spanner, and DuckPGQ ([Wolde23]) embody this shift, integrating tabular and graph-structured data within a single query framework.

Replication techniques such as CRDTs ([Balegas18], [Preguiça19], [Yu20], [Rault22], [Ignat24]) demonstrate how distributed systems can converge under concurrent updates while preserving constraints. However, they focus on raw state synchronization, whereas in many real-world scenarios, data sharing is guided by views, i.e., query-defined abstractions that determine which parts of the underlying data are exposed. Views can already be incrementally maintained in centralized relational systems, with mature approaches such as DBSP ([Budiu22]) and OpenIVM ([Battiston24]) efficiently propagating changes from base data to derived query results. Recent work has begun to extend incremental view maintenance to decentralized and eventually consistent settings ([Thomassen23]), where replicas evolve independently and must reconcile divergent states. In the context of graph databases, prototype systems have shown how property graph views can be defined, materialized, and incrementally updated over underlying storage layers ([Han24]). However, existing techniques stop short of addressing the more complex challenge of constraint-aware, view-based replication in hybrid graph-relational environments, where data is not only distributed but also governed by specific constraints and access control policies ([Angles21], [Angles23], [Clark22]).

This PhD project addresses this gap by shifting the focus from replicating raw datasets to replicating constraint-aware, incrementally evolving hybrid views representations that combine relational structure with graph connectivity, encode domain semantics, and remain composable and correct across distributed environments. This would enable _semantics-preserving, collaborative data platforms for decentralized graph-relational ecosystems_.

**Objectives of the PhD**

The overarching goal of this PhD is to establish the theoretical and system foundations for a replication framework centered on constraint-aware, incrementally evolving hybrid views, elevating views from static query results to first-class, shareable, and composable entities in distributed data systems. The project will pursue three main objectives:

- **Formalizing replicated hybrid views.** Define a declarative and semantic foundation for views that unify relational and graph data, support selective exposure and structural transformation, and incorporate explicit constraints. This model will enable reasoning about the correctness of views under replication.
- **Designing incremental and constraint-preserving replication mechanisms