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An ODT-based abstraction for mining closed sequential temporal patterns in IoT-cloud smart homes

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Abstract

Due to the large amount of usage data collected from smart home appliances in an IoT-cloud environment, efficient mining techniques are of great need to capture the behavioral patterns. Existing mining algorithms are time-consuming and error prone as the amount of data is increasing rapidly. In this paper, we propose an abstraction approach to model temporal data based on an ordered decision tree (ODT) and spatiotemporal characteristics of usage data for IoT-cloud paradigm. The contribution of this research is to provide an efficient representation in terms of average length of patterns, while preserving the spatiotemporal characteristics of original data. We performed extensive experiments on synthetic data to report the performance and provide a comparison with state-of-the-art algorithms to prove the correctness of the proposed technique, even at a low-level of abstraction. The results indicate that the proposed methodology outperform existing techniques due to the inherited power of the ODT temporal structure.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through the research group project No. RGP-049.

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Authors and Affiliations

  1. Department of Computer Information Systems, Yarmouk University, Irbid, Jordan

    Mohammed G. H. Al Zamil & Samer M. J. Samarah

  2. Department of Management Information System, Princess Sumaya University for Technology, Amman, Jordan

    Majdi Rawashdeh

  3. Department of Software Engineering, King Saud University (KSU), Riyadh, Kingdom of Saudi Arabia

    M. Anwar Hossain

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  1. Mohammed G. H. Al Zamil
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  2. Samer M. J. Samarah
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  3. Majdi Rawashdeh
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  4. M. Anwar Hossain
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Correspondence to Samer M. J. Samarah.

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Al Zamil, M.G.H., Samarah, S.M.J., Rawashdeh, M. et al. An ODT-based abstraction for mining closed sequential temporal patterns in IoT-cloud smart homes. Cluster Comput 20, 1815–1829 (2017). https://doi.org/10.1007/s10586-017-0837-0

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  • DOI: https://doi.org/10.1007/s10586-017-0837-0

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