TY - GEN
T1 - Expressive stream reasoning with laser
AU - Bazoobandi, Hamid R.
AU - Beck, Harald
AU - Urbani, Jacopo
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85032186605&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032186605&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-68288-4_6
DO - 10.1007/978-3-319-68288-4_6
M3 - Conference contribution
AN - SCOPUS:85032186605
SN - 9783319682877
VL - 1
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 87
EP - 103
BT - The Semantic Web – ISWC 2017
A2 - d'Amato, Claudia
A2 - Fernandez, Miriam
A2 - Tamma, Valentina
A2 - Lecue, Freddy
A2 - Cudré-Mauroux, Philippe
A2 - Sequeda, Juan
A2 - Lange, Christoph
A2 - Heflin, Jeff
PB - Springer/Verlag
T2 - 16th International Semantic Web Conference, ISWC 2017
Y2 - 21 October 2017 through 25 October 2017
ER -