Intelligent System for Locating, Labeling, and Logging (ISL3) View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2009

AUTHORS

Yan Huang , Terry Griffin , Salomon Lompo

ABSTRACT

As mobile smart devices become ubiquitous in our society, users will be able to receive location based information on the fly. A model that is able to predict a user’s next destination will make location based services more effective by providing personalized information to the user. The implementation of such a location prediction model requires a set of correctly labeled destinations collected from users to tune the prediction model to an acceptable level of accuracy. A large collection of data will allow researchers to derive the parameters required to train predication models and also get the trends of user behaviors in general. ISL3 will allow researchers to do just this by easily allowing them to collect user activity data to create location prediction models. More... »

PAGES

167-173

References to SciGraph publications

  • 2004. Adaptive On-Device Location Recognition in PERVASIVE COMPUTING
  • 2004. Project Lachesis: Parsing and Modeling Location Histories in GEOGRAPHIC INFORMATION SCIENCE
  • 2005. Clustering and Prediction of Mobile User Routes from Cellular Data in KNOWLEDGE DISCOVERY IN DATABASES: PKDD 2005
  • 2003-10. Using GPS to learn significant locations and predict movement across multiple users in PERSONAL AND UBIQUITOUS COMPUTING
  • Book

    TITLE

    Opportunities and Challenges for Next-Generation Applied Intelligence

    ISBN

    978-3-540-92813-3
    978-3-540-92814-0

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-540-92814-0_26

    DOI

    http://dx.doi.org/10.1007/978-3-540-92814-0_26

    DIMENSIONS

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