Ευρετηρίαση δεδομένων κίνησης με χρήση μοντέλων
Model - based indexing of mobility data
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Keywords
Μαρκοβιανά μοντέλα ; Markov ; Δεδομένα κίνησης ; Στοχαστικά μοντέλαAbstract
During the last decade, the domain of mobility data management and mining has emerged,
providing many effective methods for mining intuitive patterns that represent collective
behavior of trajectories of moving objects. An interesting research line is that instead of
operating on the raw data collected from various sensors, researchers make use of
semantically enriched data, which are either declared (by the users) or inferred (by some
annotation method). This way raw trajectory data is transformed into spatio-temporal-textual
sequences, where the extra textual information that is added to the dimensions of space and
time, represent the movement semantics. Such spatio-temporal-textual sequences form a more
realistic representation model of the complex every-day life (and as such the mobility) of
individuals. The last years, there is a mainstream using stochastic models in information
retrieval systems of sequential data. Specifically, a particular type of Markov models, named
Hidden Markov Models (HMMs) have been successfully applied in speech recognition, music
pattern recognition, consumer pattern recognition and many other domains of sequential data.
Aiming to achieve high accuracy in indexing mobility data we will apply a model-based
classification by representing each class of a mobility database via a HMM.
Postgraduate Studies Programme
Εφαρμοσμένη ΣτατιστικήDepartment
Σχολή Χρηματοοικονομικής και Στατιστικής. Τμήμα Στατιστικής και Ασφαλιστικής ΕπιστήμηςNumber of pages
47Language
GreekCollections
Except where otherwise noted, this item's license is described as
Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές
Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές