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Temporal Data Mining for Smart Homes

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Book cover Designing Smart Homes

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4008))

Abstract

Temporal data mining is a relatively new area of research in computer science. It can provide a large variety of different methods and techniques for handling and analyzing temporal data generated by smart-home environments. Temporal data mining in general fits into a two level architecture, where initially a transformation technique reduces data dimensionality in the first level and indexing techniques provide efficient access to the data in the second level. This infrastructure of temporal data mining provides the basis for high-level data mining operations such as clustering, classification, rule discovery and prediction. These operations can form the basis for developing different smart-home applications, capable of addressing a number of situations occurring within this environment. This paper outlines the main temporal data mining techniques available and provides examples of where they can be applied within a smart home environment.

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Authors and Affiliations

  1. Northern Ireland Knowledge Engineering Laboratory (NIKEL), Shore Road, Newtownabbey, Co. Antrim, BT37 0QB, Northern Ireland

    Mykola Galushka, Dave Patterson & Niall Rooney

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  1. Mykola Galushka
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  2. Dave Patterson
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  3. Niall Rooney
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Editors and Affiliations

  1. School of Computing and Mathematics, University of Ulster, UK

    Juan Carlos Augusto

  2. School of Computing and Mathematics and Computer Science Research Institute, University of Ulster, Northern Ireland

    Chris D. Nugent

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Galushka, M., Patterson, D., Rooney, N. (2006). Temporal Data Mining for Smart Homes. In: Augusto, J.C., Nugent, C.D. (eds) Designing Smart Homes. Lecture Notes in Computer Science(), vol 4008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11788485_6

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  • DOI: https://doi.org/10.1007/11788485_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35994-4

  • Online ISBN: 978-3-540-35995-1

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