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.

This position paper explores the challenges, existing solutions, and open issues related to resource allocation in federated learning environments. The focus is on how to allocate resources effectively while adhering to service level objectives (SLOs) and fairness requirements, which include factors such as server location, data provenance, energy consumption, sovereignty, carbon footprint, and economic cost. The goal is to optimise resource distribution across different stages of the federated learning process within a given architecture, ensuring that these fairness criteria are integrated into the allocation strategy. This approach aligns with decolonial methodologies that seek to offer more sustainable and equitable alternatives to the resource-intensive artificial intelligence processes prevalent today.

International audience

Digitally-supported participatory methods are often used in policy-making to develop inclusive policies by collecting and integrating citizen's opinions. However, these methods fail to capture the complexity and nuances in citizen's needs, i.e., citizens are generally unaware of other's needs, perspectives, and experiences. Consequently, policies developed with this underlying gap tend to overlook the alignment of multistakeholder perspectives, and design policies based on the optimization of high-level demographic features. In our contribution, we propose a method to enable citizens understand other's perspectives and calibrate their positions. First, we collected requirements and design principles to develop our approach by involving stakeholders and experts in policymaking in a series of workshops. Then, we conducted a crowdsourcing study with 420 participants to compare the effect of different text and images, on people's initial and final motivations and their willingness to change opinions. We observed that both influence participant's opinion change, however, the effect is more pronounced for textual modality. Finally, we discuss overarching implications of designing with empathy to mediate alignment of citizen's perspectives.

Sensor networks are increasingly commonplace in visions of smart cities and future healthcare systems, promising greater efficiency and increased wellbeing. However, the design of these technologies remains focused on specific users and fragmented by context, overlooking the diversity of needs, wants and values present when technologies, people, and lived realities interact within instrumented spaces. In this paper we present a workshop method – Sensing Care – that can help researchers, interdisciplinary design and development teams, and potentially affected users, to explore what it takes to design for living with sensor technologies that intersect and interact across private and public spaces, through speculative scenarios and role play. Drawing from three deployments of the workshop, we discuss how this approach supports the design of future care-oriented sensor networks, and helps designers understand what it means to live with complex technologies as people traverse diverse contexts.

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.

Crowdsourcing has emerged as a novel paradigm where humans are employed to perform computational tasks. In the context of Domain-Specific Modeling Language (DSML) development, where the involvement of end-users is crucial to assure that the resulting language satisfies their needs, crowdsourcing tasks could be defined to assist in the language definition process. By relying on the crowd, it is possible to show an early version of the language to a wider spectrum of users, thus increasing the validation scope and eventually promoting its acceptance and adoption. We propose a systematic method for creating crowdsourcing campaigns aimed at refining the graphical notation of DSMLs. The method defines a set of steps to identify, create and order the questions for the crowd. As a result, developers are provided with a set of notation choices that best fit end-users' needs. We also report on an experiment validating the approach.

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.