Streaming Knowledge Bases

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dc.contributor.authorWalavalkar, Onkar
dc.contributor.authorJoshi, Anupam
dc.contributor.authorFinin, Tim
dc.contributor.authorYesha, Yelena
dc.date.accessioned2018-11-28T18:35:51Z
dc.date.available2018-11-28T18:35:51Z
dc.date.issued2008-10-26
dc.descriptionProceedings of the Fourth International Workshop on Scalable Semantic Web knowledge Base Systemsen_US
dc.description.abstractWith the advent of pervasive computing, we encounter many scenarios where data is constantly flowing between sensors and applications. The volume of data produced is large, so is the rate of the dataflow. In such scenarios, knowledge extraction boils down to finding useful information i.e. detecting events of interest. Typical use cases where event detection is of paramount importance are surveillance, tracking, telecommunications data management, disease outburst detection and environmental monitoring. There are many streaming database applications built to deal with these dynamic environments. However, they can only deal with raw data – not with streaming facts. We argue that much like a new database approach had to be developed to deal with streaming data, a new approach will be required to deal with streaming facts expressed in the languages of the Semantic Web. Existing reasoners use techniques that load the whole RDF graph in main memory and carry out queries on it. This approach is of little use in real-time reasoning for streaming scenarios and takes considerable amount of time. In this paper, we combine a continuous query processors with Semantic Web techniques to build a reasoner that can deal with streaming facts. We describe our technique, and present empirical validation of its efficacy.en_US
dc.description.sponsorshipPartial support for this work was provided by MURI award FA9550-08-1-0265 from the Air Force O±ce of Scienti¯c Research and National Science Foundation award ITR 0326460.en_US
dc.description.urihttps://ebiquity.umbc.edu/paper/html/id/416/Streaming-Knowledge-Basesen_US
dc.format.extent16 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.genrepreprints
dc.identifierdoi:10.13016/M2FQ9Q902
dc.identifier.urihttp://hdl.handle.net/11603/12114
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectKnowledge Baseen_US
dc.subjectSemantic Weben_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.titleStreaming Knowledge Basesen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-8641-3193
dcterms.creatorhttps://orcid.org/0000-0002-6593-1792

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