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A Heterogeneous Fault-Resilient Architecture for Mining Anomalous Activity Patterns in Smart Homes

  • Ciprian PungilaEmail author
  • Bogdan Manate
  • Viorel Negru
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 369)

Abstract

We are presenting a massively parallel heterogeneous cloud-based architecture oriented towards anomalous activity detection in smart homes. The architecture has very high resilience to both hardware and software faults, it is capable of collecting activity from various data sources and performing anomaly detection in real-time. We corroborate the approach with an efficient checkpointing mechanism for data processing which allows the implementation of hybrid (CPU/GPU) fault-resilience and anomaly detection through pattern mining techniques, at the same time offering high throughput.

Keywords

Anomaly detection Pattern mining Smart home Fault resiliency Heterogeneous architecture Graphics processing unit 

Notes

Acknowledgments

This work was partially supported by the Romanian national grant PN-II-ID-PCE-2011-3-0260 (AMICAS).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.West University of TimisoaraTimisoaraRomania

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