A common conceptual view of text analysis is that of a two-step process, where we first extract relations from text documents and then apply a relational query over the result. Hence, text analysis shares technical challenges with, and can draw ideas from, relational databases. A framework that formally instantiates this connection is that of the document spanners. In this article, we review recent advances in various research efforts that adapt fundamental database concepts to text analysis through the lens of document spanners. Among others, we discuss aspects of query evaluation, aggregate queries, provenance, and distributed query planning.
Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey