Ensuring Query Compatibility with Evolving XML Schemas

During the life cycle of an XML application, both schemas and queries may change from one version to another. Schema evolutions may affect query results and potentially the validity of produced data. Nowadays, a challenge is to assess and accommodate the impact of theses changes in rapidly evolving XML applications. This article proposes a logical framework and tool for verifying forward/backward compatibility issues involving schemas and queries. First, it allows analyzing relations between schemas. Second, it allows XML designers to identify queries that must be reformulated in order to produce the expected results across successive schema versions. Third, it allows examining more precisely the impact of schema changes over queries, therefore facilitating their reformulation

Smart Trip Alternatives for the Curious

International audienceWhen searching for flights, current systems often suggest routesinvolving waiting times at stopovers. There might exist alternative routes which aremore attractive from a touristic perspective because their duration isnot necessarily much longer while offering enough time in anappropriate place. Choosing among suchalternatives requires additional planning efforts to make sure thate.g. points of interest can conveniently be reached in theallowed time frame. We present a system that automatically computes smart tripalternatives between any two cities. To do so, it searchespoints of interest in large semantic datasets considering theset of accessible areas around each possible layover. It then elects feasible alternatives and displays theirdifferences with respect to the default trip

Constrained Differentially Private Federated Learning for Low-bandwidth Devices

Federated learning becomes a prominent approach when different entities want to learn collaboratively a common model without sharing their training data. However, Federated learning has two main drawbacks. First, it is quite bandwidth inefficient as it involves a lot of message exchanges between the aggregating server and the participating entities. This bandwidth and corresponding processing costs could be prohibitive if the participating entities are, for example, mobile devices. Furthermore, although federated learning improves privacy by not sharing data, recent attacks have shown that it still leaks information about the training data. This paper presents a novel privacy-preserving federated learning scheme. The proposed scheme provides theoretical privacy guarantees, as it is based on Differential Privacy. Furthermore, it optimizes the model accuracy by constraining the model learning phase on few selected weights. Finally, as shown experimentally, it reduces the upstream and downstream bandwidth by up to 99.9% compared to standard federated learning, making it practical for mobile systems.Comment: arXiv admin note: text overlap with arXiv:2011.0557

Knowledge Enhanced Graph Neural Networks

Graph data is omnipresent and has a large variety of applications such as natural science, social networks or semantic web. Though rich in information, graphs are often noisy and incomplete. Therefore, graph completion tasks such as node classification or link prediction have gained attention. On the one hand, neural methods such as graph neural networks have proven to be robust tools for learning rich representations of noisy graphs. On the other hand, symbolic methods enable exact reasoning on graphs. We propose KeGNN, a neuro-symbolic framework for learning on graph data that combines both paradigms and allows for the integration of prior knowledge into a graph neural network model. In essence, KeGNN consists of a graph neural network as a base on which knowledge enhancement layers are stacked with the objective of refining predictions with respect to prior knowledge. We instantiate KeGNN in conjunction with two standard graph neural networks: Graph Convolutional Networks and Graph Attention Networks, and evaluate KeGNN on multiple benchmark datasets for node classification

SPARUB: SPARQL UPDATE Benchmark

One aim of the RDF data model, as standardized by the W3C, is to facilitate the evolution of data over time without requiring all the data consumers to be changed. To this end, one of the latest addition to the SPARQL standard query language is an update language for RDF graphs. The research on efficient and scalable SPARQL evaluation methods increasingly relies on standardized methodologies for benchmarking and comparing systems. However, current RDF benchmarks do not support graphs updates. We propose and share SPARUB: a benchmark for the SPARQL UPDATE language on RDF graphs. The aim of SPARUB is not to be yet another RDF benchmark. Instead it provides the mean to automatically extend and improve existing RDF benchmarks along a new dimension of data updates, while preserving their structure and query scenarios

A Method to Quantitatively Evaluate Geo Augmented Reality Applications

International audienceWe propose a method for quantitatively assessing the quality of Geo AR browsers. Our method aims at measuring the impact of attitude and position estimations on the rendering precision of virtual features. We report on lessons learned by applying our method on various AR use cases with real data. Our measurement technique allows to shedding light on the limits of what can be achieved in Geo AR with current technologies. This also helps in identifying interesting perspectives for the further development of high-quality Geo AR applications

Distributed Evaluation of Graph Queries using Recursive Relational Algebra

We present a system called Dist-µ-RA for the distributed evaluation of recursive graph queries. Dist-µ-RA builds on the recursive relational algebra and extends it with evaluation plans suited for the distributed setting. The goal is to offer expressivity for high-level queries while providing efficiency at scale and reducing communication costs. Experimental results on both real and synthetic graphs show the effectiveness of the proposed approach compared to existing systems

Optimising SPARQL Query Evaluation in the Presence of ShEx Constraints

International audienceShEx (Shape Expressions) is a language for expressing constraints on RDF graphs. In this work, we optimise the evaluation of conjunctive SPARQL queries, on RDF graphs, by taking advantage of ShEx constraints. Our optimisation is based on computing and assigning ranks to query triple patterns, dictating their order of execution. We first define a set of well-formed ShEx schemas, that possess interesting characteristics for SPARQL query optimisation. We then define our optimisation method by exploiting information extracted from a ShEx schema. We finally report on evaluation results performed showing the advantages of applying our optimisation on the top of an existing state-of-the-art query evaluation system