About: Handling Impossible Derivations During Stream Reasoning

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Handling Impossible Derivations During Stream Reasoning
Creator	Bal, Henri Bazoobandi, Hamid Urbani, Jacopo Van Harmelen, Frank
Source	PMC
abstract	With the rapid expansion of the Web and the advent of the Internet of Things, there is a growing need to design tools for intelligent analytics and decision making on streams of data. Logic-based frameworks like LARS allow the execution of complex reasoning on such streams, but it is paramount that the computation is completed in a timely manner before the stream expires. To reduce the runtime, we can extend the validity of inferred conclusions to the future to avoid repeated derivations, but this is not enough to avoid all sources of redundant computation. To further alleviate this problem, this paper introduces a new technique that infers the impossibility of certain derivations in the future and blocks the reasoner from performing computation that is doomed to fail anyway. An experimental analysis on microbenchmarks shows that our technique leads to a significant reduction of the reasoning runtime.
has issue date	2020-05-07(xsd:dateTime)
bibo:doi	10.1007/978-3-030-49461-2_1
has license	no-cc
sha1sum (hex)	00dd17fcbf34e0cb84272565ac52768cf14d42da
schema:url	https://doi.org/10.1007/978-3-030-49461-2_1
resource representing a document's title	Handling Impossible Derivations During Stream Reasoning
has PubMed Central identifier	PMC7250624
schema:publication	The Semantic Web
resource representing a document's body	covid:00dd17fcbf34e0cb84272565ac52768cf14d42da#body_text
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