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Towards Traditional Simulation Models of Context Using Process Mining

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationComputational Intelligence, Communication Systems and Networks (CICSyN), 2015 7th International Conference on
PublisherIEEE
Pages70-75
Number of pages6
ISBN (Print)9781467370165
DOIs
Publication statusPublished - 2015
Publication typeA4 Article in a conference publication
EventInternational Conference on Computational Intelligence, Communication Systems and Networks -
Duration: 1 Jan 1900 → …

Conference

ConferenceInternational Conference on Computational Intelligence, Communication Systems and Networks
Abbreviated titleCICSyN
Period1/01/00 → …

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.

Publication forum classification

Field of science, Statistics Finland

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