[Back]

H. Bazoobandi, H. Beck, J. Urbani:
"Expressive Stream Reasoning with Laser";
in: "The Semantic Web - ISWC 2017. 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part I", Springer, LNCS, 2017, ISBN: 978-3-319-68287-7, 87 - 103.

An increasing number of use cases require a timely
extraction of non-trivial knowledge from semantically annotated data
streams, especially on the Web and for the Internet of Things
(IoT). Often, this extraction requires expressive reasoning, which is
challenging to compute on large streams. We propose Laser, a new
reasoner that supports a pragmatic, non-trivial fragment of the logic
LARS which extends Answer Set Programming (ASP) for streams. At its
core, Laser implements a novel evaluation procedure which annotates
formulae to avoid the re-computation of duplicates at multiple time
points. This procedure, combined with a judicious implementation of
the LARS operators, is responsible for significantly better runtimes
than the ones of other state-of-the-art systems like C-SPARQL and
CQELS, or an implementation of LARS which runs on the ASP solver
Clingo. This enables the application of expressive logic-based
reasoning to large streams and opens the door to a wider range of
stream reasoning use cases.

Stream Reasoning, Answer Set Programming, Nonmonotonic Reasoning

http://dx.doi.org/10.1007/978-3-319-68288-4_6

http://publik.tuwien.ac.at/files/publik_265441.pdf