International Internet of Things Summit

IoT360 2014: Internet of Things. IoT Infrastructures pp 192-197 | Cite as

Understanding the Impact of Data Sparsity and Duration for Location Prediction Applications

  • Alasdair Thomason
  • Matthew Leeke
  • Nathan Griffiths
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 151)

Abstract

As mobile devices capable of sensing location have become pervasive, the collection and transmission of location data has become commonplace, enabling the creation of models of behaviour that support location prediction. With such devices often heavily resource-constrained, the nature of data used in location prediction must be understood in order to optimise storage and processing requirements. This paper specifically explores data sparsity and collection duration. The results presented provide insight which suggest: (i) a relationship of diminishing returns in predictive accuracy when collecting user location data at increased rates over a fixed period, and (ii) the duration over which a fixed size sample of location data is collected has a greater impact on predicative accuracy than data sparsity.

Keywords

Collection Data Duration Location prediction Sparsity 

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Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015

Authors and Affiliations

  • Alasdair Thomason
    • 1
  • Matthew Leeke
    • 1
  • Nathan Griffiths
    • 1
  1. 1.University of WarwickCoventryUK

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