Complex event forecasting with prediction suffix trees
Abstract Complex event recognition (CER) systems have become popular in the past two decades due to their ability to “instantly” detect patterns on real-time streams of events. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detect...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Alevizos, Elias [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: The VLDB journal - Springer Berlin Heidelberg, 1992, 31(2021), 1 vom: 15. Sept., Seite 157-180 |
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| Übergeordnetes Werk: |
volume:31 ; year:2021 ; number:1 ; day:15 ; month:09 ; pages:157-180 |
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10.1007/s00778-021-00698-x |
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| 520 | |a Abstract Complex event recognition (CER) systems have become popular in the past two decades due to their ability to “instantly” detect patterns on real-time streams of events. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of complex event forecasting (CEF). Our framework combines two formalisms: (a) symbolic automata which are used to encode complex event patterns and (b) prediction suffix trees which can provide a succinct probabilistic description of an automaton’s behavior. We compare our proposed approach against state-of-the-art methods and show its advantage in terms of accuracy and efficiency. In particular, prediction suffix trees, being variable-order Markov models, have the ability to capture long-term dependencies in a stream by remembering only those past sequences that are informative enough. We also discuss how CEF solutions should be best evaluated on the quality of their forecasts. | ||
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| 700 | 1 | |a Paliouras, Georgios |4 aut | |
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10.1007/s00778-021-00698-x doi (DE-627)OLC2077903929 (DE-He213)s00778-021-00698-x-p DE-627 ger DE-627 rakwb eng 004 VZ Alevizos, Elias verfasserin (orcid)0000-0002-9260-0024 aut Complex event forecasting with prediction suffix trees 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Complex event recognition (CER) systems have become popular in the past two decades due to their ability to “instantly” detect patterns on real-time streams of events. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of complex event forecasting (CEF). Our framework combines two formalisms: (a) symbolic automata which are used to encode complex event patterns and (b) prediction suffix trees which can provide a succinct probabilistic description of an automaton’s behavior. We compare our proposed approach against state-of-the-art methods and show its advantage in terms of accuracy and efficiency. In particular, prediction suffix trees, being variable-order Markov models, have the ability to capture long-term dependencies in a stream by remembering only those past sequences that are informative enough. We also discuss how CEF solutions should be best evaluated on the quality of their forecasts. Finite automata Regular expressions Complex event recognition Complex event processing Symbolic automata Variable-order Markov models Artikis, Alexander aut Paliouras, Georgios aut Enthalten in The VLDB journal Springer Berlin Heidelberg, 1992 31(2021), 1 vom: 15. Sept., Seite 157-180 (DE-627)170933059 (DE-600)1129061-4 (DE-576)032856466 1066-8888 nnns volume:31 year:2021 number:1 day:15 month:09 pages:157-180 https://doi.org/10.1007/s00778-021-00698-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_2018 GBV_ILN_4277 AR 31 2021 1 15 09 157-180 |
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Abstract Complex event recognition (CER) systems have become popular in the past two decades due to their ability to “instantly” detect patterns on real-time streams of events. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of complex event forecasting (CEF). Our framework combines two formalisms: (a) symbolic automata which are used to encode complex event patterns and (b) prediction suffix trees which can provide a succinct probabilistic description of an automaton’s behavior. We compare our proposed approach against state-of-the-art methods and show its advantage in terms of accuracy and efficiency. In particular, prediction suffix trees, being variable-order Markov models, have the ability to capture long-term dependencies in a stream by remembering only those past sequences that are informative enough. We also discuss how CEF solutions should be best evaluated on the quality of their forecasts. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Abstract Complex event recognition (CER) systems have become popular in the past two decades due to their ability to “instantly” detect patterns on real-time streams of events. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of complex event forecasting (CEF). Our framework combines two formalisms: (a) symbolic automata which are used to encode complex event patterns and (b) prediction suffix trees which can provide a succinct probabilistic description of an automaton’s behavior. We compare our proposed approach against state-of-the-art methods and show its advantage in terms of accuracy and efficiency. In particular, prediction suffix trees, being variable-order Markov models, have the ability to capture long-term dependencies in a stream by remembering only those past sequences that are informative enough. We also discuss how CEF solutions should be best evaluated on the quality of their forecasts. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Complex event recognition (CER) systems have become popular in the past two decades due to their ability to “instantly” detect patterns on real-time streams of events. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of complex event forecasting (CEF). Our framework combines two formalisms: (a) symbolic automata which are used to encode complex event patterns and (b) prediction suffix trees which can provide a succinct probabilistic description of an automaton’s behavior. We compare our proposed approach against state-of-the-art methods and show its advantage in terms of accuracy and efficiency. In particular, prediction suffix trees, being variable-order Markov models, have the ability to capture long-term dependencies in a stream by remembering only those past sequences that are informative enough. We also discuss how CEF solutions should be best evaluated on the quality of their forecasts. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Complex event forecasting with prediction suffix trees |
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Artikis, Alexander Paliouras, Georgios |
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