Towards Traditional Simulation Models of Context Using Process Mining

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Towards Traditional Simulation Models of Context Using Process Mining. / Pileggi, Paolo; Rivero-Rodriguez, Alejandro; Nykänen, Ossi.

Computational Intelligence, Communication Systems and Networks (CICSyN), 2015 7th International Conference on. IEEE, 2015. s. 70-75.

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Harvard

Pileggi, P, Rivero-Rodriguez, A & Nykänen, O 2015, Towards Traditional Simulation Models of Context Using Process Mining. julkaisussa Computational Intelligence, Communication Systems and Networks (CICSyN), 2015 7th International Conference on. IEEE, Sivut 70-75, INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE, COMMUNICATION SYSTEMS AND NETWORKS (CICSYN), 1/01/00. https://doi.org/10.1109/CICSyN.2015.23

APA

Pileggi, P., Rivero-Rodriguez, A., & Nykänen, O. (2015). Towards Traditional Simulation Models of Context Using Process Mining. teoksessa Computational Intelligence, Communication Systems and Networks (CICSyN), 2015 7th International Conference on (Sivut 70-75). IEEE. https://doi.org/10.1109/CICSyN.2015.23

Vancouver

Pileggi P, Rivero-Rodriguez A, Nykänen O. Towards Traditional Simulation Models of Context Using Process Mining. julkaisussa Computational Intelligence, Communication Systems and Networks (CICSyN), 2015 7th International Conference on. IEEE. 2015. s. 70-75 https://doi.org/10.1109/CICSyN.2015.23

Author

Pileggi, Paolo ; Rivero-Rodriguez, Alejandro ; Nykänen, Ossi. / Towards Traditional Simulation Models of Context Using Process Mining. Computational Intelligence, Communication Systems and Networks (CICSyN), 2015 7th International Conference on. IEEE, 2015. Sivut 70-75

Bibtex - Lataa

@inproceedings{80a744e90da44fef89952cf4fba54627,

title = "Towards Traditional Simulation Models of Context Using Process Mining",

abstract = "Context (sensor) systems are hard to model: they require constant updating and insightful approaches, especially considering the increasing data volume, variety, and generation rate of contemporary networking paradigms, like the Internet of Things. In this paper, we argue that intelligent process models can be mined to look at the actual system activity from alternative context perspectives, i.e., perspectives observable from the sensor attributes themselves. We explain how the close relationship between the models derived using Process Mining, and Event-Driven Simulation can be exploited to help not only better understand what is happening in such systems but also provide alternative models for the intelligent solutions they support, such as context inference. We demonstrate this using a real-world example and discuss the feasibility of extending these alternative process models to be viewed as simulation. We envision automated steps that would result in traditional simulation models of context using Process Mining.",

author = "Paolo Pileggi and Alejandro Rivero-Rodriguez and Ossi Nyk{\"a}nen",

year = "2015",

doi = "10.1109/CICSyN.2015.23",

language = "English",

isbn = "9781467370165",

pages = "70--75",

booktitle = "Computational Intelligence, Communication Systems and Networks (CICSyN), 2015 7th International Conference on",

publisher = "IEEE",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - Towards Traditional Simulation Models of Context Using Process Mining

AU - Pileggi, Paolo

AU - Rivero-Rodriguez, Alejandro

AU - Nykänen, Ossi

PY - 2015

Y1 - 2015

N2 - Context (sensor) systems are hard to model: they require constant updating and insightful approaches, especially considering the increasing data volume, variety, and generation rate of contemporary networking paradigms, like the Internet of Things. In this paper, we argue that intelligent process models can be mined to look at the actual system activity from alternative context perspectives, i.e., perspectives observable from the sensor attributes themselves. We explain how the close relationship between the models derived using Process Mining, and Event-Driven Simulation can be exploited to help not only better understand what is happening in such systems but also provide alternative models for the intelligent solutions they support, such as context inference. We demonstrate this using a real-world example and discuss the feasibility of extending these alternative process models to be viewed as simulation. We envision automated steps that would result in traditional simulation models of context using Process Mining.

AB - Context (sensor) systems are hard to model: they require constant updating and insightful approaches, especially considering the increasing data volume, variety, and generation rate of contemporary networking paradigms, like the Internet of Things. In this paper, we argue that intelligent process models can be mined to look at the actual system activity from alternative context perspectives, i.e., perspectives observable from the sensor attributes themselves. We explain how the close relationship between the models derived using Process Mining, and Event-Driven Simulation can be exploited to help not only better understand what is happening in such systems but also provide alternative models for the intelligent solutions they support, such as context inference. We demonstrate this using a real-world example and discuss the feasibility of extending these alternative process models to be viewed as simulation. We envision automated steps that would result in traditional simulation models of context using Process Mining.

U2 - 10.1109/CICSyN.2015.23

DO - 10.1109/CICSyN.2015.23

M3 - Conference contribution

SN - 9781467370165

SP - 70

EP - 75

BT - Computational Intelligence, Communication Systems and Networks (CICSyN), 2015 7th International Conference on

PB - IEEE

ER -

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