The appearance of linked data on the web calls for novel database management technologies for linked data collections. The classical challenges from database research need to be now raised for linked data: how to define exact logical queries, how to manage dynamic updates, and how to automatize the search for appropriate queries. In contrast to mainstream linked open data, the LINKS project will focus on linked data collections in various formats, under the assumption that the data is correct in most dimensions. The challenges remain difficult to over come due to incomplete data, uninformative or heterogeneous schema and the remaining data errors and ambiguities. We will develop algorithms for evaluating and optimizing logical queries on linked data collections, incremental algorithms that can monitor streams of linked data and manage dynamical updates of linked data collections, and symbolic learning algorithms that can infer appropriate queries for linked data collections from examples.
Evaluation du programme hyperstreaming pour les données dynamiques
Langages réguliers et circuits de taille linéaire
Aggregation Algorithms for Machine Learning and Data Mining
Compilation et exécution efficace des expressions régulières en Streaming