Section: Overall Objectives

Presentation

We will develop algorithms for answering logical querying on heterogeneous linked data collections in hybrid formats, distributed programming languages for managing dynamic linked data collections and workflows based on queries and mappings, and symbolic machine learning algorithms that can link datasets by infering appropriate queries and mappings.

The following three paragraphs summarize our main research objectives.

Querying Heterogeneous Linked Data

We will develop new kinds of schema mappings for semi-structured datasets in hybrid formats including graph databases, RDF collections, and relational databases. These induce recursive queries on linked data collections for which we will investigate evaluation algorithms, containment problems, and concrete applications.

Managing Dynamic Linked Data

In order to manage dynamic linked data collections and workflows, we will develop distributed data-centric programming languages with streams and parallelism, based on novel algorithms for incremental query answering, study the propagation of updates of dynamic data through schema mappings, and investigate static analysis methods for linked data workflows.

Linking Data Graphs

Finally, we will develop symbolic machine learning algorithms, for inferring queries and mappings between linked data collections in various graphs formats from annotated examples.