Supporting activity modelling from activity traces

Georgeon, Olivier L.; Mille, Alain; Bellet, Thierry; Mathern, Benoît; Ritter, Frank E.

doi:10.1111/j.1468-0394.2011.00584.x

Cited by 19 publications

(9 citation statements)

References 20 publications

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“…A Modelled Trace, noted "M-Trace", is a set of observed elements. We call observed element, noted "obsel" any structured information generated from the observation of an activity [11]. In our research context, the activity we observe is a training simulation on a NPP FSS of EDF.…”

Section: Principle Of Our Approachmentioning

confidence: 99%

Assistance to Trainers for the Observation and Analysis Activities of Operators Trainees on Nuclear Power Plant Full-Scope Simulator

Champalle

Sehaba

Cosmas

et al. 2012

2012 Fourth International Conference on Intelligent Networking and Collaborative Systems

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This article focuses on the professional training of operators of Nuclear Power Plants (NPP) on Full-Scope Simulators (FSS). In such context, observation and analysis of the individual and collective interaction of the trainees are critical and particularly difficult. The objective of our work is to propose models and tools to help trainers observe and analyze trainees' activities during preparation and debriefing. For that purpose, our approach consists in representing the actions of the operators and the simulation data in the form of modelled traces. These modelled traces are then transformed in order to extract higher information level. Trainers can visualized the different levels of trace to analyze the reasons, collective or individual, of successes or failure of trainees during the simulation. In order to validate our approach, we have developed the prototype D3KODE based on the trace model and transformation that we proposed. This prototype was then evaluate according to a protocol based on a comparative method in the context of several experiment conducted with a team of experts, trainers and trainees from EDF Group.

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Section: Principle Of Our Approachmentioning

confidence: 99%

Assistance to Trainers for the Observation and Analysis Activities of Operators Trainees on Nuclear Power Plant Full-Scope Simulator

Champalle

Sehaba

Cosmas

et al. 2012

2012 Fourth International Conference on Intelligent Networking and Collaborative Systems

Self Cite

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“…To model an activity based on its traces, Georgeon et al proposed a trace graph structure in two parts: a sequence and an ontology [Georgeon et al, 2012]. The sequence includes event instances with relation instances found between the event instances.…”

Section: -Trace-based Systemsmentioning

confidence: 99%

PETRA: Process Evolution using a TRAce-based system on a maintenance platform

Karray¹,

Chebel-Morello

Zerhouni

2014

Knowledge-Based Systems

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To meet increasing needs in the field of maintenance, we studied the dynamic aspect of process and services on a maintenance platform, a major challenge in process mining and knowledge engineering. Hence, we propose a dynamic experience feedback approach to exploit maintenance process behaviors in real execution of the maintenance platform. An active learning process exploiting event log is introduced by taking into account the dynamic aspect of knowledge using trace engineering. Our proposal makes explicit the underlying knowledge of platform users by means of a trace-based system called "PETRA". The goal of this system is to extract new knowledge rules about transitions and activities in maintenance processes from previous platform executions as well as its user (i.e. maintenance operators) interactions. While following a Knowledge Traces Discovery process and handling the maintenance ontology IMAMO, "PETRA" is composed of three main subsystems: tracking, learning and knowledge capitalization. The capitalized rules are shared in the platform knowledge base in order to be reused in future process executions. The feasibility of this method is proven through concrete use cases involving four maintenance processes and their simulation.

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“…Activity modelling in general has been considered in the work of Georgeon et al (2011). They constructed a symbolic abstract representation of an activity from an activity trace.…”

Section: Activity Modellingmentioning

confidence: 99%

Learning routines over long‐term sensor data using topic models

et al. 2013

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Recent advances on sensor network technology provide the infrastructure to create intelligent environments on physical places. One of the main issues of sensor networks is the large amount of data they generate. Therefore, it is necessary to have good data analysis techniques with the aim of learning and discovering what is happening on the monitored environment. The problem becomes even more challenging if this process is performed following an unsupervised way (without having any a priori information) and applied over a long‐term timeline with many sensors. In this work, topic models are employed to learn the latent structure and dynamics of sensor network data. Experimental results using two realistic datasets, having over 50 weeks of data, have shown the ability to find routines of activity over sensor network data in office environments.

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Supporting activity modelling from activity traces

Cited by 19 publications

References 20 publications

Assistance to Trainers for the Observation and Analysis Activities of Operators Trainees on Nuclear Power Plant Full-Scope Simulator

Assistance to Trainers for the Observation and Analysis Activities of Operators Trainees on Nuclear Power Plant Full-Scope Simulator

PETRA: Process Evolution using a TRAce-based system on a maintenance platform

Learning routines over long‐term sensor data using topic models

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