MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators
DatalogMTL is an extension of Datalog with operators from metric temporal logic which has received significant attention in recent years. It is a highly expressive knowledge representation language that is well-suited for applications in temporal ontology-based query answering and stream processing....
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creator | Wang, Dingmin Hu, Pan Wałęga, Przemysław Andrzej Grau, Bernardo Cuenca |
description | DatalogMTL is an extension of Datalog with operators from metric temporal
logic which has received significant attention in recent years. It is a highly
expressive knowledge representation language that is well-suited for
applications in temporal ontology-based query answering and stream processing.
Reasoning in DatalogMTL is, however, of high computational complexity, making
implementation challenging and hindering its adoption in applications. In this
paper, we present a novel approach for practical reasoning in DatalogMTL which
combines materialisation (a.k.a. forward chaining) with automata-based
techniques. We have implemented this approach in a reasoner called MeTeoR and
evaluated its performance using a temporal extension of the Lehigh University
Benchmark and a benchmark based on real-world meteorological data. Our
experiments show that MeTeoR is a scalable system which enables reasoning over
complex temporal rules and datasets involving tens of millions of temporal
facts. |
doi_str_mv | 10.48550/arxiv.2201.04596 |
format | Article |
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logic which has received significant attention in recent years. It is a highly
expressive knowledge representation language that is well-suited for
applications in temporal ontology-based query answering and stream processing.
Reasoning in DatalogMTL is, however, of high computational complexity, making
implementation challenging and hindering its adoption in applications. In this
paper, we present a novel approach for practical reasoning in DatalogMTL which
combines materialisation (a.k.a. forward chaining) with automata-based
techniques. We have implemented this approach in a reasoner called MeTeoR and
evaluated its performance using a temporal extension of the Lehigh University
Benchmark and a benchmark based on real-world meteorological data. Our
experiments show that MeTeoR is a scalable system which enables reasoning over
complex temporal rules and datasets involving tens of millions of temporal
facts.</description><identifier>DOI: 10.48550/arxiv.2201.04596</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Databases</subject><creationdate>2022-01</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2201.04596$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2201.04596$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Dingmin</creatorcontrib><creatorcontrib>Hu, Pan</creatorcontrib><creatorcontrib>Wałęga, Przemysław Andrzej</creatorcontrib><creatorcontrib>Grau, Bernardo Cuenca</creatorcontrib><title>MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators</title><description>DatalogMTL is an extension of Datalog with operators from metric temporal
logic which has received significant attention in recent years. It is a highly
expressive knowledge representation language that is well-suited for
applications in temporal ontology-based query answering and stream processing.
Reasoning in DatalogMTL is, however, of high computational complexity, making
implementation challenging and hindering its adoption in applications. In this
paper, we present a novel approach for practical reasoning in DatalogMTL which
combines materialisation (a.k.a. forward chaining) with automata-based
techniques. We have implemented this approach in a reasoner called MeTeoR and
evaluated its performance using a temporal extension of the Lehigh University
Benchmark and a benchmark based on real-world meteorological data. Our
experiments show that MeTeoR is a scalable system which enables reasoning over
complex temporal rules and datasets involving tens of millions of temporal
facts.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Databases</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz8luwjAUhWFvWFS0D9BV_QJJPeTGuDtE6SCBQCj76Ma5oZZCHDlWh7dvC6zO5teRPsbupciLBYB4xPjtP3OlhMxFAba8YestVRQOT3wf0SXvsOcHwikMfjhyP_BnTNiHI__y6YNvKUXveEWnMcS_cjdSxBTidMtmHfYT3V13zqqXdbV6yza71_fVcpNhacrMFqZRphMdNSCcUW1HoCwYC-0CNWlSoKW2rZTojCaJ1ijtRENKtMJZ0HP2cLk9O-ox-hPGn_rfU589-hdBO0Ts</recordid><startdate>20220112</startdate><enddate>20220112</enddate><creator>Wang, Dingmin</creator><creator>Hu, Pan</creator><creator>Wałęga, Przemysław Andrzej</creator><creator>Grau, Bernardo Cuenca</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20220112</creationdate><title>MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators</title><author>Wang, Dingmin ; Hu, Pan ; Wałęga, Przemysław Andrzej ; Grau, Bernardo Cuenca</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a676-947b27f0feb50c72dfe5295795d8a3e3e253139d11ac73e1a9723c0be20d0c953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Databases</topic><toplevel>online_resources</toplevel><creatorcontrib>Wang, Dingmin</creatorcontrib><creatorcontrib>Hu, Pan</creatorcontrib><creatorcontrib>Wałęga, Przemysław Andrzej</creatorcontrib><creatorcontrib>Grau, Bernardo Cuenca</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Dingmin</au><au>Hu, Pan</au><au>Wałęga, Przemysław Andrzej</au><au>Grau, Bernardo Cuenca</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators</atitle><date>2022-01-12</date><risdate>2022</risdate><abstract>DatalogMTL is an extension of Datalog with operators from metric temporal
logic which has received significant attention in recent years. It is a highly
expressive knowledge representation language that is well-suited for
applications in temporal ontology-based query answering and stream processing.
Reasoning in DatalogMTL is, however, of high computational complexity, making
implementation challenging and hindering its adoption in applications. In this
paper, we present a novel approach for practical reasoning in DatalogMTL which
combines materialisation (a.k.a. forward chaining) with automata-based
techniques. We have implemented this approach in a reasoner called MeTeoR and
evaluated its performance using a temporal extension of the Lehigh University
Benchmark and a benchmark based on real-world meteorological data. Our
experiments show that MeTeoR is a scalable system which enables reasoning over
complex temporal rules and datasets involving tens of millions of temporal
facts.</abstract><doi>10.48550/arxiv.2201.04596</doi><oa>free_for_read</oa></addata></record> |
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title | MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators |
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