Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/50519

TitleAdapting the user path through trajectory data mining
Author(s)Ramos, João Ricardo Martins
Analide, Cesar
Neves, José
Novais, Paulo
KeywordsTrajectory Data Mining
Frequent Patterns
Sequence Mining
Clustering
Issue date2017
PublisherSpringer
JournalAdvances in Intelligent Systems and Computing
CitationRamos J., César A., Neves J., Novais P., Adapting the User Path Through Trajectory Data Mining, Ambient Intelligence- Software and Applications – 7th International Symposium on Ambient Intelligence (ISAmI 2017), Springer - Advances in Intelligent Systems and Computing, Vol 615, ISSN 2194-5357, ISBN 9783319401133, pp 210-202, 2017. https://doi.org/10.1007/978-3-319-61118-1_24
Abstract(s)The pervasiveness of location based services such the GPS on mobile devices enabled the gathering of massive spatial-temporal data. These databases enabled the mining of new data in order to calculate frequent patterns and predict the movement of the objects. In the development of our system for guiding the user with cognitive disabilities (CogHelper) we are applying a trajectory data mining to adapt and adjust the path to user preferences. Indeed, the guiding process may be more useful and it increases the quality of life of the user through this new functionality (conciliated with the speculative computation module). Thus, instead of the user having to adapt to the application we are developing a system that adapts the path to the user. Rather than being guided through the shortest path he may be oriented by a longer but preferred path. The main contribution of this paper is the specification of the trajectory data mining which is incorporated in CogHelper system.
TypeConference paper
URIhttp://hdl.handle.net/1822/50519
ISBN978-3-319-61117-4
DOI10.1007/978-3-319-61118-1_24
ISSN2194-5357
e-ISSN978-3-319-61118-1
Publisher versionhttps://link.springer.com/chapter/10.1007%2F978-3-319-61118-1_24
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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