Stream reasoning with DatalogMTL
We study stream reasoning in DatalogMTL—an extension of Datalog with metric temporal operators. We propose a sound and complete stream reasoning algorithm that is applicable to forwardpropagating DatalogMTL programs, in which propagation of derived information towards past time points is precluded....
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2023
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| _version_ | 1826309867321688064 |
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| author | Walega, P Kaminski, M Wang, D Cuenca Grau, B |
| author_facet | Walega, P Kaminski, M Wang, D Cuenca Grau, B |
| author_sort | Walega, P |
| collection | OXFORD |
| description | We study stream reasoning in DatalogMTL—an extension of Datalog with metric temporal operators. We propose a sound and complete stream reasoning algorithm that is applicable to forwardpropagating DatalogMTL programs, in which propagation of derived information towards past time
points is precluded. Memory consumption in our generic algorithm depends both on the properties of
the rule set and the input data stream; in particular, it depends on the distances between timestamps
occurring in data. This may be undesirable in certain practical scenarios since these distances can
be very small, in which case the algorithm may require large amounts of memory. To address this
issue, we propose a second algorithm, where the size of the required memory becomes independent
on the timestamps in the data at the expense of disallowing punctual intervals in the rule set. We
have implemented our approach as an extension of the DatalogMTL reasoner MeTeoR and tested it
experimentally. The obtained results support the feasibility of our approach in practice. |
| first_indexed | 2024-03-07T07:42:07Z |
| format | Journal article |
| id | oxford-uuid:350b3976-399f-4faa-8c92-2f289edd71d5 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:42:07Z |
| publishDate | 2023 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:350b3976-399f-4faa-8c92-2f289edd71d52023-05-05T12:11:17ZStream reasoning with DatalogMTLJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:350b3976-399f-4faa-8c92-2f289edd71d5EnglishSymplectic ElementsElsevier2023Walega, PKaminski, MWang, DCuenca Grau, BWe study stream reasoning in DatalogMTL—an extension of Datalog with metric temporal operators. We propose a sound and complete stream reasoning algorithm that is applicable to forwardpropagating DatalogMTL programs, in which propagation of derived information towards past time points is precluded. Memory consumption in our generic algorithm depends both on the properties of the rule set and the input data stream; in particular, it depends on the distances between timestamps occurring in data. This may be undesirable in certain practical scenarios since these distances can be very small, in which case the algorithm may require large amounts of memory. To address this issue, we propose a second algorithm, where the size of the required memory becomes independent on the timestamps in the data at the expense of disallowing punctual intervals in the rule set. We have implemented our approach as an extension of the DatalogMTL reasoner MeTeoR and tested it experimentally. The obtained results support the feasibility of our approach in practice. |
| spellingShingle | Walega, P Kaminski, M Wang, D Cuenca Grau, B Stream reasoning with DatalogMTL |
| title | Stream reasoning with DatalogMTL |
| title_full | Stream reasoning with DatalogMTL |
| title_fullStr | Stream reasoning with DatalogMTL |
| title_full_unstemmed | Stream reasoning with DatalogMTL |
| title_short | Stream reasoning with DatalogMTL |
| title_sort | stream reasoning with datalogmtl |
| work_keys_str_mv | AT walegap streamreasoningwithdatalogmtl AT kaminskim streamreasoningwithdatalogmtl AT wangd streamreasoningwithdatalogmtl AT cuencagraub streamreasoningwithdatalogmtl |