  Social media, e-commerce, streaming video, e-mail, cloud documents, web
pages, traffic flows, and network packets fill vast digital lakes, rivers, and
oceans that we each navigate daily. This digital hyperspace is an amorphous
flow of data supported by continuous streams that stretch standard concepts of
type and dimension. The unstructured data of digital hyperspace can be
elegantly represented, traversed, and transformed via the mathematics of
hypergraphs, hypersparse matrices, and associative array algebra. This paper
explores a novel mathematical concept, the semilink, that combines pairs of
semirings to provide the essential operations for graph analytics, database
operations, and machine learning. The GraphBLAS standard currently supports
hypergraphs, hypersparse matrices, the mathematics required for semilinks, and
seamlessly performs graph, network, and matrix operations. With the addition of
key based indices (such as pointers to strings) and semilinks, GraphBLAS can
become a richer associative array algebra and be a plug-in replacement for
spreadsheets, database tables, and data centric operating systems, enhancing
the navigation of unstructured data found in digital hyperspace.
