Rule-based stream reasoning
In recent years, there has been an increasing interest in extending stream processing engines with rule-based temporal reasoning capabilities. To ensure correctness, such systems must be able to output results over the partial data received so far as if the entire (infinite) stream had been availabl...
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| Format: | Thesis |
| Language: | English |
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2019
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| _version_ | 1797065919918243840 |
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| author | Ronca, A |
| author2 | Kaminski, M |
| author_facet | Kaminski, M Ronca, A |
| author_sort | Ronca, A |
| collection | OXFORD |
| description | In recent years, there has been an increasing interest in extending stream processing engines with rule-based temporal reasoning capabilities. To ensure correctness, such systems must be able to output results over the partial data received so far as if the entire (infinite) stream had been available; furthermore, these results must be streamed out as soon as the relevant data is received, thus incurring the minimum possible latency; finally, due to memory limitations, systems can only keep a limited history of previous facts in memory to perform further computations. These requirements pose significant theoretical and practical challenges since temporal rules can derive new information and propagate it both towards past and future time points; as a result, streamed answers can depend on data that has not yet been received, as well as on data that arrived far in the past. Towards developing a solid foundation for practical rule-based stream reasoning, we propose and study in this thesis a suite of decision problems that can be exploited by stream reasoning algorithms to tackle the aforementioned challenges, and provide tight complexity bounds for a core temporal extension of Datalog. All of the problems we consider can be solved at design time (under reasonable assumptions), prior to the processing of any data. Solving these problems enables the use of reasoning algorithms that process the input streams incrementally using a sliding window, while at the same time supporting an expressive rule-based knowledge representation language and minimising both latency and memory consumption. |
| first_indexed | 2024-03-06T21:35:12Z |
| format | Thesis |
| id | oxford-uuid:46031584-aafd-410a-9ec7-092afcb0e805 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T21:35:12Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | oxford-uuid:46031584-aafd-410a-9ec7-092afcb0e8052022-03-26T15:11:12ZRule-based stream reasoningThesishttp://purl.org/coar/resource_type/c_db06uuid:46031584-aafd-410a-9ec7-092afcb0e805knowledge representation and reasoningcomputer scienceartificial intelligenceEnglishHyrax Deposit2019Ronca, AKaminski, MCuenca Grau, BKostylev, EKontchakov, RIn recent years, there has been an increasing interest in extending stream processing engines with rule-based temporal reasoning capabilities. To ensure correctness, such systems must be able to output results over the partial data received so far as if the entire (infinite) stream had been available; furthermore, these results must be streamed out as soon as the relevant data is received, thus incurring the minimum possible latency; finally, due to memory limitations, systems can only keep a limited history of previous facts in memory to perform further computations. These requirements pose significant theoretical and practical challenges since temporal rules can derive new information and propagate it both towards past and future time points; as a result, streamed answers can depend on data that has not yet been received, as well as on data that arrived far in the past. Towards developing a solid foundation for practical rule-based stream reasoning, we propose and study in this thesis a suite of decision problems that can be exploited by stream reasoning algorithms to tackle the aforementioned challenges, and provide tight complexity bounds for a core temporal extension of Datalog. All of the problems we consider can be solved at design time (under reasonable assumptions), prior to the processing of any data. Solving these problems enables the use of reasoning algorithms that process the input streams incrementally using a sliding window, while at the same time supporting an expressive rule-based knowledge representation language and minimising both latency and memory consumption. |
| spellingShingle | knowledge representation and reasoning computer science artificial intelligence Ronca, A Rule-based stream reasoning |
| title | Rule-based stream reasoning |
| title_full | Rule-based stream reasoning |
| title_fullStr | Rule-based stream reasoning |
| title_full_unstemmed | Rule-based stream reasoning |
| title_short | Rule-based stream reasoning |
| title_sort | rule based stream reasoning |
| topic | knowledge representation and reasoning computer science artificial intelligence |
| work_keys_str_mv | AT roncaa rulebasedstreamreasoning |