Drug traceability is currently a very challenging area given the complexity of several issues, including drug quality and counterfeit medications. The counterfeited drugs have a major impact on human life, treatment outcomes and economic burden. To deal with these issues, we propose a semantic blockchain-based system for drug traceability that aims at detecting counterfeit drugs in order to improve the patients’ safety and quality of life as well as eliminating manufacturers’ potential loss and increasing their revenue. Our proposal is based on blockchain and semantic web technologies to enhance the representation capability of data in the pharmaceutical supply chain.

International audience

Recent years have seen the rise of Web data, in particular Linked Data, with, up to now, more than 1000 datasets in the Linked Open Data Cloud (LOD). These datasets are mostly of entity-centric nature and are highly heterogeneous in terms of domains, language, schema, etc. Hence, the vision of uniformly querying such resources in the LOD has a long way to go. While equivalent entity instances across datasets are often linked by sameAs links, relations from different datasets and schemas are usually not aligned. In this paper, we propose an on-line instance-based relation alignment approach. The alignment may be performed during query execution and requires partial information from the datasets. We align relations to a target dataset using association rule mining approaches. We sample for equivalent entity instances with two main sampling strategies. Preliminary experiments, show that we are able to align relations with high accuracy, even if accessing the entire datasets is impossible or impractical.

International audience

Many data providers make their data available through Web service APIs. In order to unleash the potential of these sources for intelligent applications, the data has to be combined across different APIs. However, due to the heterogeneity of schemas, the integration of different APIs remains a mainly manual task to date. In this paper, we model an API method as a view with binding patterns over a global RDF schema. We present an algorithm that can automatically infer the view definition of a method in the global schema. We also show how to compute transformation functions that can transform API call results into this schema. The key idea of our approach is to exploit the intersection of API call results with a knowledge base and with other call results. Our experiments on more than 50 real Web services show that we can automatically infer the schema with a precision of 81%-100%.

We propose a new method for approximate k-NN search in large scale image databases, based on top-k multi-criteria search techniques. The method defines a simple index structure based on sorted lists, which provides a good compromise between fast retrieval, storage requirements and update cost. The search algorithm delivers approximate results with guarantees about false negatives, with fast emergence of good approximations, monotonically improved and leading if necessary to an exact result. Experiments with the on-disk implementation show that our method produces very good approximate results several times faster than the Baseline method.

We present RoSeS, a running system for large-scale content-based RSS feed filtering and aggregation. The implementation of RoSeS is based on standard database concepts like declarative query languages, views and multi-query optimization. Users create personalized feeds by defining and composing content-based filtering and aggregation queries on collections of RSS feeds. These queries are translated into continuous multi-query execution plans which are optimized using a new cost-based multi-query optimization strategy.

With the current explosion of data, retrieving and integrating information from various sources is a critical problem. The designer has to specify a mediated schema providing a homogeneous view of the sources. We report on an initial work toward automatically generating mappings between elements in the sources and the mediated schema. Information sources we are interested in are XML documents with respect to a document type definition (DTD). We describe the Xyleme project and present our approach implemented in the SAMAG system to automatically find mappings on the basis of semantic and structural criteria. Finally, we report the first results of an experiment where SAMAG has been applied to XML documents in the cultural domain.