Probabilistic Complex Event Recognition

Alevizos, Elias; Skarlatidis, Anastasios; Artikis, Alexander; Παλιούρας, Γεώργιος

doi:10.1145/3117809

Cited by 60 publications

(80 citation statements)

References 74 publications

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“…Listing 2 5 shows how individual's activities and their context information (in this example 'direction' of user, measured in degrees) are stored to recognize '*-Together group activities' (where '*' is a wild-card which could mean 'walking' , 'running' etc). Every individual (mobile device) has a unique ID which relates them to other entities such as context entity or group entity.…”

Section: Groupsense Android Client Endpointmentioning

confidence: 99%

GARSAaaS: group activity recognition and situation analysis as a service

Abkenar

Loke

Zaslavsky

et al. 2019

J Internet Serv Appl

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Human activity recognition using embedded mobile and embedded sensors is becoming increasingly important. Scaling up from individuals to groups, that is, group activity recognition, has attracted significant attention recently. This paper proposes a model and specification language for group activities called GroupSense-L, and a novel architecture called GARSAaaS (GARSA-as-a-Service) to provide services for mobile Group Activity Recognition and Situation Analysis (or GARSA) applications. We implemented and evaluated GARSAaaS which is an extension of a framework called GroupSense (Abkenar et al., 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), 2016) where sensor data, collected using smartphone sensors, smartwatch sensors and embedded sensors in things, are aggregated via a protocol for these different devices to share information, as required for GARSA. We illustrate our approach via a scenario for providing services for bush walking leaders and bush walkers in a bushwalking group activity. We demonstrate the feasibility of our model and expressiveness of our proposed model.

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Section: Groupsense Android Client Endpointmentioning

confidence: 99%

GARSAaaS: group activity recognition and situation analysis as a service

Abkenar

Loke

Zaslavsky

et al. 2019

J Internet Serv Appl

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“…Numerous CER systems have been proposed in the literature [6,7]. Recognition systems with a logic-based representation of complex event (CE) patterns, in particular, have been attracting attention since they exhibit a formal, declarative semantics [2]. We have been developing an efficient dialect of the Event Calculus, called 'Event Calculus for Run-Time reasoning' (RTEC) [4].…”

Section: Complex Event Recognitionmentioning

confidence: 99%

“…CER applications exhibit various types of uncertainty, ranging from incomplete and erroneous data streams to imperfect CE patterns [2]. We have been developing techniques for handling uncertainty in CER by extending the Event Calculus with probabilistic reasoning.…”

Section: Uncertainty Handlingmentioning

confidence: 99%

The Complex Event Recognition Group

Alevizos¹,

Artikis²,

Katzouris³

et al. 2018

SIGMOD Rec.

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The Complex Event Recognition (CER) group is a research team, affiliated with the National Centre of Scientific Research "Demokritos" in Greece. The CER group works towards advanced and efficient methods for the recognition of complex events in a multitude of large, heterogeneous and interdependent data streams. Its research covers multiple aspects of complex event recognition, from efficient detection of patterns on event streams to handling uncertainty and noise in streams, and machine learning techniques for inferring interesting patterns. Lately, it has expanded to methods for forecasting the occurrence of events. It was founded in 2009 and currently hosts 3 senior researchers, 5 PhD students and works regularly with under-graduate students.

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“…Methods for handling both uncertainty and complex relational structure have received much attention in machine learning. For instance, in composite event recognition (Cugola and Margara, 2012;Artikis et al, 2012;Alevizos et al, 2017), relations are defined over entities of actors and objects involved in an event. Such applications are typically characterised by uncertainty, and in many cases data of significant volume and velocity.…”

mentioning

confidence: 99%

Semi-supervised online structure learning for composite event recognition

2019

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Online structure learning approaches, such as those stemming from Statistical Relational Learning, enable the discovery of complex relations in noisy data streams. However, these methods assume the existence of fully-labelled training data, which is unrealistic for most real-world applications. We present a novel approach for completing the supervision of a semi-supervised structure learning task. We incorporate graph-cut minimisation, a technique that derives labels for unlabelled data, based on their distance to their labelled counterparts. In order to adapt graph-cut minimisation to first order logic, we employ a suitable structural distance for measuring the distance between sets of logical atoms. The labelling process is achieved online (single-pass) by means of a caching mechanism and the Hoeffding bound, a statistical tool to approximate globally-optimal decisions from locally-optimal ones. We evaluate our approach on the task of composite event recognition by using a benchmark dataset for human activity recognition, as well as a real dataset for maritime monitoring. The evaluation suggests that our approach can effectively complete the missing labels and eventually, improve the accuracy of the underlying structure learning system.

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Probabilistic Complex Event Recognition

Cited by 60 publications

References 74 publications

GARSAaaS: group activity recognition and situation analysis as a service

GARSAaaS: group activity recognition and situation analysis as a service

The Complex Event Recognition Group

Semi-supervised online structure learning for composite event recognition

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