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complex event recognition Within a Discrete event System Framework

IEEE Transactions on Automatic Control 2025年

作者： Liu, Yu Cao, Lin Shu, Shaolong Lin, Feng Tongji University College of Electronics and Information Engineering Shanghai201804 China Shanghai Maritime University Institute of Logistics Science and Engineering Shanghai200135 China Wayne State University Department of Electrical and Computer Engineering DetroitMI48202 United States

Recognizing complex events revealed by raw data is an increasingly crucial task that serves as one of the foundations for system monitoring and decision-making. Our goal is to accurately recognize the occurred complex events, that is, uniquely determine the occurred complex event sequence from the raw data. We abstract the outputs of data sources as a set of atomic events and then use an automaton to describe all atomic event sequences that can be generated by the given system. We represent a complex event as a set of atomic event sequences. For a given atomic event sequence and a complex event to be recognized, we introduce the notion of "partition"to stand for a possible single complex event sequence. By constructing an augmented automaton that includes all possible partitions, we derive a necessary and sufficient condition for the complex event recognition problem to be solvable. We then find an algorithm to check the condition. When the complex event recognition problem is solvable, any occurred complex event can be determined accurately and promptly online with existing methods like Aho-Corasick algorithm. © 1963-2012 IEEE.

关键词： Automata complex event recognition discrete event systems pattern recognition

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complex event recognition using constrained low-rank representation

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IMAGE AND VISION COMPUTING 2015年 42卷 13-21页

作者： Dehghan, Afshin Oreifej, Omar Shah, Mubarak Univ Cent Florida Orlando FL 32816 USA Univ Calif Berkeley Berkeley CA 94720 USA

complex event recognition is the problem of recognizing events in long and unconstrained videos. In this extremely challenging task, concepts have recently shown a promising direction where core low-level events (referred to as concepts) are annotated and modeled using a portion of the training data, then each complex event is described using concept scores, which are features representing the occurrence confidence for the concepts in the event. However, because of the complex nature of the videos, both the concept models and the corresponding concept scores are significantly noisy. In order to address this problem, we propose a novel low-rank formulation, which combines the precisely annotated videos used to train the concepts, with the rich concept scores. Our approach finds a new representation for each event, which is not only low-rank, but also constrained to adhere to the concept annotation, thus suppressing the noise, and maintaining a consistent occurrence of the concepts in each event. Extensive experiments on large scale real world dataset TRECVID Multimedia event Detection 2011 and 2012 demonstrate that our approach consistently improves the discriminativity of the concept scores by a significant margin. (C) 2015 Published by Elsevier B.V.

关键词： complex event recognition Low-rank optimization Activity recognition Action concepts

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complex event recognition and anomaly detection with event behavior model

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PATTERN ANALYSIS AND APPLICATIONS 2024年第2期27卷 1-15页

作者： Liu, Min-Chang Hsu, Fang-Rong Huang, Chua-Huang Feng Chia Univ Dept Informat Engn & Comp Sci 100 Wenhua Rd Taichung 407 Taiwan

The concept of complex event processing refers to the process of tracking and analyzing a set of related events and drawing conclusions from them. For such systems, complex event recognition is essential. The object of complex event recognition is to recognize meaningful events or patterns and construct processing rules to respond to them. Researchers have conducted numerous studies on the recognition of complex event patterns by using recognition languages or models. However, the completeness of the process in complex event recognition has rarely been discussed. Although the reality of the event is uncertain, the structure for modeling and explaining complex event interactions of contingent information remains unclear. In this study, we focused on developing a general framework for addressing these problems and demonstrating the applicability of model-based approaches to represent spatio-temporal dimensions and causality in complex event recognition. In this paper, we propose an event behavior model for complex event recognition from a process perspective. The developed model could detect and explain anomalies associated with complex events. An experiment was conducted to evaluate the model performance. The results revealed that temporal operations within overlapping events were crucial to event pattern recognition.

关键词： Anomaly detection complex event recognition event pattern event uncertainty Model-based diagnosis

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complex event recognition in the Big Data era: a survey

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VLDB JOURNAL 2020年第1期29卷 313-352页

作者： Giatrakos, Nikos Alevizos, Elias Artikis, Alexander Deligiannakis, Antonios Garofalakis, Minos Athena Res & Innovat Ctr Maroussi Greece Tech Univ Crete Khania Greece Natl & Kapodistrian Univ Athens Athens Greece Natl Ctr Sci Res Demokritos Athens Greece Univ Piraeus Piraeus Greece

The concept of event processing is established as a generic computational paradigm in various application fields. events report on state changes of a system and its environment. complex event recognition (CER) refers to the identification of composite events of interest, which are collections of simple, derived events that satisfy some pattern, thereby providing the opportunity for reactive and proactive measures. Examples include the recognition of anomalies in maritime surveillance, electronic fraud, cardiac arrhythmias and epidemic spread. This survey elaborates on the whole pipeline from the time CER queries are expressed in the most prominent languages, to algorithmic toolkits for scaling-out CER to clustered and geo-distributed architectural settings. We also highlight future research directions.

关键词： Big Data complex event recognition complex event recognition languages Parallelism Elasticity Distributed processing

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complex event recognition WITH UNCERTAINTY REASONING 12

COMPLEX EVENT RECOGNITION WITH UNCERTAINTY REASONING

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Liu, Xueqin Clawson, Kathy Wang, Hui Scotney, Bryan Liu, Jun Univ Ulster Sch Comp & Math Newtownabbey BT37 0QB Antrim North Ireland Univ Ulster Sch Comp & Informat Engn Coleraine BT52 1SA Londonderry North Ireland

ISBN: (纸本)9781479902606

The goal of complex event recognition considered in this paper is the automatic detection of complex high-level events in videos. This is a difficult task, especially when videos are captured under unconstrained conditions, with poor lighting, heavy background clutter and occlusion. In this paper, we propose a hierarchical knowledge-based framework for complex event recognition. The video event knowledge represents an arbitrary complex spatio-temporal event as a hierarchical composition of simpler events in a natural way. Uncertainty reasoning procedures are applied to interpret low level event descriptions according to the video knowledge base in order to recognize high level scenarios.

关键词： Video pattern recognition complex event recognition Hierarchical approach Knowledge base Uncertainty reasoning

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complex event recognition From Discrete Sensor Data With a Discrete event System Framework 22

Complex Event Recognition From Discrete Sensor Data With a D...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Liu, Yu Shu, Shaolong Lin, Feng Tongji Univ Sch Elect & Informat Engn Shanghai Peoples R China Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA

ISBN: (纸本)9781713872344

Recognizing complex events revealed by sensor readings is an increasingly crucial task that serves as one of the foundations for system monitoring and decision-making. In this paper, we investigate the recognition problem for a class of complex events that can be represented by a sequence of discrete sensor outputs. We view the outputs of discrete sensors as sensor events. We use an automaton to describe all possible sequences of sensor events that can be generated in a given system. The complex events to be recognized is a set of sequences of sensor events and can be represented by the marked language of another automaton. For a given sensor event sequence, we introduce the notion of "partition" to stand for a possible complex event sequence. By constructing an augmented automaton that includes all possible partitions, we find a necessary and sufficient condition for the recognition problem to be solvable. We then find an algorithm to check the condition. Finally, an online complex event recognition procedure is proposed to determine the occurred complex events when the complex event recognition problem is solvable. Copyright (c) 2023 The Authors.

关键词： Discrete event systems complex event recognition Discrete sensor data Automaton Pattern recognition

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complex event recognition Notification Methodology for Uncertain IoT Systems Based on Micro-Service Architecture 6

Complex Event Recognition Notification Methodology for Uncer...

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6th IEEE International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Vrbaski, M. Bolic, M. Majumdar, S. Univ Ottawa Sch Elect Engn & Comp Sci 800 King Edward Ave Ottawa ON K1N 6N5 Canada Carlton Univ Syst & Comp Engn 1125 Colonel By Dr Ottawa ON K1S 5B6 Canada

ISBN: (纸本)9781538675038

One of the main purposes of the Internet of Things (loT) systems is to provide information of the observed physical system and to notify when a certain observed complex event occurs in the system. The biggest challenge of the loT systems is that the systems collect a massive amount of uncertain data from diverse loT devices, where the devices are connected through the internet. In addition to that, some events are inferred from other events and uncertainty is propagated from parent events to the inferred events, which additionally contributes to overall system uncertainty. The observed complex events are a complex relationship of primitive events that are produced by loT devices and collected in loT system. There were a couple of attempts to quantify the system uncertainty by estimating the likelihood of the occurrence of events of interest while taking into account the uncertain data and uncertain parameters of the model of the physical system with rule-based complex event processing and/or Bayesian network. These methods were implemented as stand-alone software solutions that are unable to scale under heavy loads of incoming events. This paper proposes a micro-service based notification methodology that uses complex event recognition to handle the loT system uncertainty.

关键词： the internet of things complex event processing complex event recognition probability reasoning probabilistic programming notification micro-services

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A Formal Framework for complex event recognition

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ACM TRANSACTIONS ON DATABASE SYSTEMS 2021年第4期46卷 1–49页

作者： Grez, Alejandro Riveros, Cristian Ugarte, Martin Vansummeren, Stijn Pontificia Univ Catolica Chile Dept Comp Sci Vicuna Mackenna 4860Edificio San Agustin Santiago 7820436 Chile Millennium Inst Fdn Res Data Santiago Chile UHasselt Hasselt Univ Data Sci Inst B-3540 Diepenbeek Belgium

complex event recognition (CER) has emerged as the unifying field for technologies that require processing and correlating distributed data sources in real time. CER finds applications in diverse domains, which has resulted in a large number of proposals for expressing and processing complex events. Existing CER languages lack a clear semantics, however, which makes them hard to understand and generalize. Moreover, there are no general techniques for evaluating CER query languages with clear performance guarantees. In this article, we embark on the task of giving a rigorous and efficient framework to CER. We propose a formal language for specifying complex events, called complex event logic (CEL), that contains the main features used in the literature and has a denotational and compositional semantics. We also formalize the so-called selection strategies, which had only been presented as by-design extensions to existing frameworks. We give insight into the language design trade-offs regarding the strict sequencing operators of CEL and selection strategies. With a well-defined semantics at hand, we discuss how to efficiently process complex events by evaluating CEL formulas with unary filters. We start by introducing a formal computational model for CER, called complex event automata (CEA), and study how to compile CEL formulas with unary filters into CEA. Furthermore, we provide efficient algorithms for evaluating CEA over event streams using constant time per event followed by output-linear delay enumeration of the results.

关键词： complex event recognition complex event processing streaming evaluation constant delay enumeration

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complex event recognition meets Hierarchical Conjunctive Queries

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Proceedings of the ACM on Management of Data 2024年第5期2卷 1-26页

作者： Dante Pinto Cristian Riveros Pontificia Universidad Católica de Chile & UHasselt Santiago Chile Pontificia Universidad Católica de Chile & IMFD Santiago Chile

Hierarchical conjunctive queries (HCQ) are a subclass of conjunctive queries (CQ) with robust algorithmic properties. Among others, Berkholz, Keppeler, and Schweikardt have shown that HCQ is the subclass of CQ (without projection) that admits dynamic query evaluation with constant update time and constant delay enumeration. On a different but related setting stands complex event recognition (CER), a prominent technology for evaluating sequence patterns over streams. Since one can interpret a data stream as an unbounded sequence of inserts in dynamic query evaluation, it is natural to ask to which extent CER can take advantage of HCQ to find a robust class of queries that can be evaluated *** this paper, we search to combine HCQ with sequence patterns to find a class of CER queries that can get the best of both worlds. To reach this goal, we propose a class of complex event automata model called Parallelized complex event Automata (PCEA) for evaluating CER queries with correlation (i.e., joins) over streams. This model allows us to express sequence patterns and compare values among tuples, but it also allows us to express conjunctions by incorporating a novel form of non-determinism that we call parallelization. We show that for every HCQ (under bag semantics), we can construct an equivalent PCEA. Further, we show that HCQ is the biggest class of full CQ that this automata model can define. Then, PCEA stands as a sweet spot that precisely expresses HCQ (i.e., among full CQ) and extends them with sequence patterns. Finally, we show that PCEA also inherits the good algorithmic properties of HCQ by presenting a streaming evaluation algorithm under sliding windows with logarithmic update time and output-linear delay for the class of PCEA with equality predicates.

关键词： complex event recognition conjunctive queries query evaluation streams

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complex event recognition From Discrete Sensor Data With a Discrete event System Framework

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IFAC-PapersOnLine 2023年第2期56卷 11330-11336页

作者： Yu Liu Shaolong Shu Feng Lin School of Electronics and Information Engineering Tongji University Shanghai China Department of Electrical and Computer Engineering Wayne State University Detroit MI 48202 USA

Recognizing complex events revealed by sensor readings is an increasingly crucial task that serves as one of the foundations for system monitoring and decision-making. In this paper, we investigate the recognition problem for a class of complex events that can be represented by a sequence of discrete sensor outputs. We view the outputs of discrete sensors as sensor events. We use an automaton to describe all possible sequences of sensor events that can be generated in a given system. The complex events to be recognized is a set of sequences of sensor events and can be represented by the marked language of another automaton. For a given sensor event sequence, we introduce the notion of “partition” to stand for a possible complex event sequence. By constructing an augmented automaton that includes all possible partitions, we find a necessary and sufficient condition for the recognition problem to be solvable. We then find an algorithm to check the condition. Finally, an online complex event recognition procedure is proposed to determine the occurred complex events when the complex event recognition problem is solvable.

关键词： Discrete event systems complex event recognition Discrete sensor data Automaton Pattern recognition

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