LINKS team

Linking Dynamic Data

Leader: Joachim Niehren



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.

Nicolas Crosetti

Aggregation Algorithms for Machine Learning and Data Mining

Dimitri Gallois

Reasoning on Recursive queries

Paul Gallot

Safety of Data Transformations (SaDaT)

José Martin Lozano Aparicio

Intégration sécurisée de données Web

Momar Sakho

Streaming path queries for linked data

Adrien Boiret

Normalization and Learning of Transducers on Trees and Words 2016-11-07

Tom Sebastian

Evaluation of XPath Queries on XML streams with Networks of Early Nested Word Automata 2016-06-17

Radu Ciucanu

Requêtes et schémas hétérogènes : Complexité et apprentissage 2015-07-01

Aurélien Lemay

Machine Learning Techniques for semistructured Data : infer queries and transformation with grammatical inference tools 2018-11-16

Slawomir Staworko

Symbolic Inference Methods for Databases 2015-12-14

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