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.

Graphs are powerful abstractions for modeling relationships and enabling data science tasks. In causal inference, Directed Acyclic Graphs (DAGs) serve as a key formalism, but they are typically handcrafted by experts and rarely treated as first-class data artifacts in graph data management systems. This paper presents a novel vision to align causal analysis with property graphs—the foundation of modern graph databases—by rethinking graph models to incorporate hypernodes, structural equations, and causality-aware query semantics. By unifying graph databases with causal reasoning, our approach enables the declarative expression of DAG manipulation operations along with interventions and counterfactuals, combining expressiveness with computational efficiency. We validate this vision through a proof-of-concept implementation supporting scalable causal queries over DAGs, ultimately aiming to make graph databases causally aware and support data-driven, personalized decision-making across several scientific domains.

International audience

Over one billion cars interact with each other on the road every day. Each driver has his own driving style, which could impact safety, fuel economy and road congestion. Knowledge about the driving style of the driver could be used to encourage “better” driving behaviour through immediate feedback while driving, or by scaling auto insurance rates based on the aggressiveness of the driving style. In this work we report on our study of driving behaviour profiling based on unsupervised data mining methods. The main goal is to detect the different driving behaviours, and thus to cluster drivers with similar behaviour. This paves the way to new business models related to the driving sector, such as Pay-How-You-Drive insurance policies and car rentals. Driver behavioral characteristics are studied by collecting information from GPS sensors on the cars and by applying three different analysis approaches (DP-means, Hidden Markov Models, and Behavioural Topic Extraction) to the contextual scene detection problems on car trips, in order to detect different behaviour along each trip. Subsequently, drivers are clustered in similar profiles based on that and the results are compared with a human-defined groundtruth on drivers classification. The proposed framework is tested on a real dataset containing sampled car signals. While the different approaches show relevant differences in trip segment classification, the coherence of the final driver clustering results is surprisingly high.

An essential aspect for building effective crowdsourcing com- putations is the ability of "controlling the crowd", i.e. of dynamically adapting the behaviour of the crowdsourcing systems as response to the quantity and quality of completed tasks or to the availability and reliability of performers. Most crowdsourcing systems only provide limited and predefined controls; in contrast, we present an approach to crowdsourcing which provides fine-level, powerful and flexible controls. We model each crowdsourcing application as composition of elementary task types and we progressively transform these high level specifications into the features of a reactive execution environment that supports task planning, assignment and completion as well as performer monitoring and exclusion. Controls are specified as active rules on top of data structures which are derived from the model of the application; rules can be added, dropped or modified, thus guaranteeing maximal flexibility with limited effort.