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Title: Semi-Lazy Learning Approach to Dynamic Spatio-Temporal Data Analysis
Keywords: semi-lazy learning, spatio-temporal data analysis, dynamic prediction, trajectory, time series, itinerary
Issue Date: 14-Aug-2014
Citation: ZHOU JINGBO (2014-08-14). Semi-Lazy Learning Approach to Dynamic Spatio-Temporal Data Analysis. ScholarBank@NUS Repository.
Abstract: With a wide range of applications, spatio-temporal data analysis has been a timely and popular research topic in recent years. In this thesis, we investigate problems concerning dynamic spatio-temporal data analysis. Data analysis methods can be categorized into two classes: the eager learning approach and the lazy learning approach. However, none of the existing approaches are able to achieve eligible performance that is suitable for dynamic spatio-temporal data analysis. The main aim of this thesis is to propose a new approach to dynamic spatio-temporal data analysis. After carefully cogitating how the features of the eager learning and lazy learning approaches could influence analysis performance, we perceived, to our pleasure, that their strong points and weak points are just complementary. Hence, it would be highly imperative and persuasive to adopt their strong points to contrive a new approach. Consequently, we devised a novel semi-lazy learning approach which can take the dynamic factor into account in a similar fashion to the lazy learning approach and still keep good analysis functions like the eager learning approach. Based on the semi-lazy learning approach, we exploited three concrete dynamic spatio-temporal data analysis problems, which are trajectory prediction, time series prediction and itinerary recommendation respectively.
Appears in Collections:Ph.D Theses (Open)

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