Improving pervasive application behavior using other users' information

  • Authors:

  • Mike Spence;Siobhán Clarke

  • Affiliations:

  • Distributed Systems Group School of Computer Science and Statistics, Trinity College Dublin;Distributed Systems Group School of Computer Science and Statistics, Trinity College Dublin

  • Venue:
  • ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
  • Year:
  • 2010

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Abstract

The behavior of a pervasive application is much improved with access to accurate, relevant information. While other users' devices are a promising source of current information for pervasive applications, the relevance of information describing other users is not always apparent. To date, CBR has been successfully used to select information of relevance from the previous experience of the application's user. This paper describes how CBR techniques can be used to select accurate, relevant information from other users as well. We address the challenges that arise due to the set of other users from which information is available being dynamic and potentially sparse, the potential pitfalls of completely ignoring the previous experience of the application's user while using context from other users, and the elicitation of essential feedback distracting the potentially mobile user. This paper presents an examination of the use of information from other users through simulations run on three existing pervasive datasets.