Modern applications often need to manage and analyze widely diverse datasets that span
multiple data models [1],[2],[3],[4],[5]. Warehousing the data through Extract-Transform-Load …

Data federation addresses the problem of uniformly accessing multiple, possibly
heterogeneous data sources, by mapping them into a unified schema, such as an RDF …

Fact checking has captured the attention of the media and the public alike; it has also
recently received strong attention from the computer science community, in particular from …

Digital data is a gold mine for modern journalism. However, datasets which interest
journalists are extremely heterogeneous, ranging from highly structured (relational …

We consider the problem of integrating heterogeneous data (relational, JSON, key-values,
graphs etc.) and querying it efficiently. Traditional data integration systems fall into two …

Due to the increasing variety of the current database landscape, polyglot data management
has become a hot research topic in recent years. The underlying idea is to combine the …

One of the most challenging issues in the era of Big Data is the Variety of the data. In
general, there are two solutions to directly manage multi-model data currently: a single …

The proliferation of heterogeneous data sources in many application contexts brings an
urgent need for expressive and efficient data integration mechanisms. There are strong …

Big data applications routinely involve diverse datasets: relations flat or nested, complex-
structure graphs, documents, poorly structured logs, or even text data. To handle the data …

The variety of data is one of the most challenging issues for the research and practice in
data management. The so-called multi-model data are naturally organized in different, but …