TY - JOUR
TI - Full-text support for publish/subscribe ontology systems
AU - Zervakis, L.
AU - Tryfonopoulos, C.
AU - Skiadopoulos, S.
AU - Koubarakis, M.
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PY - 2016
VL - 9678
TODO - null
SP - 233-249
PB - Springer-Verlag
SN - null
TODO - 10.1007/978-3-319-34129-3_15
TODO - Indexing (of information), And filters;  Continuous queries;  Main memory;  Ontology system;  Orders of magnitude;  Publish/subscribe;  Query indexing;  State of the art, Semantic Web
TODO - In this work, we envision a publish/subscribe ontology system that is able to index large numbers of expressive continuous queries and filter them against RDF data that arrive in a streaming fashion. To this end, we propose a SPARQL extension that supports the creation of full-text continuous queries and propose a family of main-memory query indexing algorithms which perform matching at low complexity and minimal filtering time. We experimentally compare our approach against a state-of-the-art competitor (extended to handle indexing of full-text queries) both on structural and full-text tasks using real-world data. Our approach proves two orders of magnitude faster than the competitor in all types of filtering tasks. © Springer International Publishing Switzerland 2016.
ER -