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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIX pp 130–168Cite as

eVM: An Event Virtual Machine Framework

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Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 11310))

Abstract

Information and communication technology (ICT) is impacting our daily lives more than ever before. Many existing applications guide users in their daily activities (e.g., navigation through traffic, health monitoring, managing home comfort, socializing with others). Although these applications are different in terms of purpose and application domain, they all detect events and propose actions and decision making aid to users. However, there is no usage of a common backbone for event detection that can be instantiated, re-used, and reconfigured in different use cases. In this paper, we propose eVM, a generic event Virtual Machine able to detect events in different contexts while allowing domain experts to model and define the targeted events prior to detection. eVM simultaneously considers the various features of the defined events (e.g., temporal, geographical), and uses the latter to detect different feature-centric events (e.g., time-centric, location-centric). eVM is based on different components (an event query language, a query compiler, an event detection core, etc.), but mainly the event detection modules are detailed here. We show that eVM is re-usable in different contexts and that the performance of our prototype is quasi-linear in most cases. Our experimental results showed that the detection accuracy is improved when, besides spatio-temporal information, other features are considered.

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Notes

  1. 1.

    http://www.apple.com/ios/photos.

  2. 2.

    https://developer.xamarin.com.

  3. 3.

    https://www.acleddata.com.

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Acknowledgements

We thank Dr. Gilbert Tekli and Dr. Yudith Cardinale for their valuable feedback and input. We would also like to thank Anthony Nassar for his remarkable contribution in developing the mobile application used for the experimentation of this work.

Author information

Authors and Affiliations

  1. Univ Pau & Pays Adour/E2S-UPPA, LIUPPA, EA3000, Mont-de-Marsan, France

    Elio Mansour & Philippe Arnould

  2. Univ Pau & Pays Adour/E2S-UPPA, LIUPPA, EA3000, Anglet, France

    Richard Chbeir

Authors
  1. Elio Mansour
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  2. Richard Chbeir
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  3. Philippe Arnould
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Corresponding author

Correspondence to Elio Mansour .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Roland Wagner

  3. IUT, University Lyon 1, Villeurbanne Cedex, France

    Djamal Benslimane

  4. University of Milan, Crema, Italy

    Ernesto Damiani

  5. University of Michigan-Dearborn, Dearborn, MI, USA

    William I. Grosky

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Mansour, E., Chbeir, R., Arnould, P. (2018). eVM: An Event Virtual Machine Framework. In: Hameurlain, A., Wagner, R., Benslimane, D., Damiani, E., Grosky, W. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIX. Lecture Notes in Computer Science(), vol 11310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58415-6_5

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  • DOI: https://doi.org/10.1007/978-3-662-58415-6_5

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