An Intuitionistic Calculus to Complex Abnormal Event Recognition on Data Streams

Data mining in real-time data streams is associated with multiple types of uncertainty, which often leads the respective categorizers to make erroneous predictions related to the presence or absence of complex events. But recognizing complex abnormal events, even those that occur in extremely rare c...

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Autores principales:	Zhao Lijun, Hu Guiqiu, Li Qingsheng, Ding Guanhua
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology (General) T1-995 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/9842835ccc5c4ca699b2ee701ee6c3a2
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id	oai:doaj.org-article:9842835ccc5c4ca699b2ee701ee6c3a2
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spelling	oai:doaj.org-article:9842835ccc5c4ca699b2ee701ee6c3a22021-11-22T01:10:56ZAn Intuitionistic Calculus to Complex Abnormal Event Recognition on Data Streams1939-012210.1155/2021/3573753https://doaj.org/article/9842835ccc5c4ca699b2ee701ee6c3a22021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3573753https://doaj.org/toc/1939-0122Data mining in real-time data streams is associated with multiple types of uncertainty, which often leads the respective categorizers to make erroneous predictions related to the presence or absence of complex events. But recognizing complex abnormal events, even those that occur in extremely rare cases, offers significant support to decision-making systems. Therefore, there is a need for robust recognition mechanisms that will be able to predict or recognize when an abnormal event occurs or will occur on a data stream. Considering this need, this paper presents an Intuitionistic Tumbling Windows event calculus (ITWec) methodology. It is an innovative data analysis system that combines for the first time in the literature a set of multiple systems for Complex Abnormal Event Recognition (CAER). In the proposed system, the probabilities of the existence of a high-level complex abnormal event for each period are initially calculated nonparametrically, based on the probabilities of the low-level events associated with it. Because cumulative results are sought in consecutive, nonoverlapping sections of the data stream, the method uses the clearly defined rules of initialization and termination of the tumbling windows method, where there is an explicit determination of the time interval within which several blocks of a particular stream are investigated window. Finally, the number of maximum probable intervals in which an event is likely to occur based on a certain probability threshold is calculated, based on a parametric representation of intuitively fuzzy sets.Zhao LijunHu GuiqiuLi QingshengDing GuanhuaHindawi-WileyarticleTechnology (General)T1-995Science (General)Q1-390ENSecurity and Communication Networks, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995 Science (General) Q1-390
spellingShingle	Technology (General) T1-995 Science (General) Q1-390 Zhao Lijun Hu Guiqiu Li Qingsheng Ding Guanhua An Intuitionistic Calculus to Complex Abnormal Event Recognition on Data Streams
description	Data mining in real-time data streams is associated with multiple types of uncertainty, which often leads the respective categorizers to make erroneous predictions related to the presence or absence of complex events. But recognizing complex abnormal events, even those that occur in extremely rare cases, offers significant support to decision-making systems. Therefore, there is a need for robust recognition mechanisms that will be able to predict or recognize when an abnormal event occurs or will occur on a data stream. Considering this need, this paper presents an Intuitionistic Tumbling Windows event calculus (ITWec) methodology. It is an innovative data analysis system that combines for the first time in the literature a set of multiple systems for Complex Abnormal Event Recognition (CAER). In the proposed system, the probabilities of the existence of a high-level complex abnormal event for each period are initially calculated nonparametrically, based on the probabilities of the low-level events associated with it. Because cumulative results are sought in consecutive, nonoverlapping sections of the data stream, the method uses the clearly defined rules of initialization and termination of the tumbling windows method, where there is an explicit determination of the time interval within which several blocks of a particular stream are investigated window. Finally, the number of maximum probable intervals in which an event is likely to occur based on a certain probability threshold is calculated, based on a parametric representation of intuitively fuzzy sets.
format	article
author	Zhao Lijun Hu Guiqiu Li Qingsheng Ding Guanhua
author_facet	Zhao Lijun Hu Guiqiu Li Qingsheng Ding Guanhua
author_sort	Zhao Lijun
title	An Intuitionistic Calculus to Complex Abnormal Event Recognition on Data Streams
title_short	An Intuitionistic Calculus to Complex Abnormal Event Recognition on Data Streams
title_full	An Intuitionistic Calculus to Complex Abnormal Event Recognition on Data Streams
title_fullStr	An Intuitionistic Calculus to Complex Abnormal Event Recognition on Data Streams
title_full_unstemmed	An Intuitionistic Calculus to Complex Abnormal Event Recognition on Data Streams
title_sort	intuitionistic calculus to complex abnormal event recognition on data streams
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/9842835ccc5c4ca699b2ee701ee6c3a2
work_keys_str_mv	AT zhaolijun anintuitionisticcalculustocomplexabnormaleventrecognitionondatastreams AT huguiqiu anintuitionisticcalculustocomplexabnormaleventrecognitionondatastreams AT liqingsheng anintuitionisticcalculustocomplexabnormaleventrecognitionondatastreams AT dingguanhua anintuitionisticcalculustocomplexabnormaleventrecognitionondatastreams AT zhaolijun intuitionisticcalculustocomplexabnormaleventrecognitionondatastreams AT huguiqiu intuitionisticcalculustocomplexabnormaleventrecognitionondatastreams AT liqingsheng intuitionisticcalculustocomplexabnormaleventrecognitionondatastreams AT dingguanhua intuitionisticcalculustocomplexabnormaleventrecognitionondatastreams
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An Intuitionistic Calculus to Complex Abnormal Event Recognition on Data Streams

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