ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Computer Science


Computer Science and Engineering

First Advisor

Ramez Elmasri

Second Advisor

Leonidas Fegaras


In this dissertation, we propose efficient frameworks to analyze spatio-temporal data. In the first part of the dissertation, we use a clustering based method to mine useful information from trajectory data. Existing trajectory clustering algorithms have focused on geometric properties and spatial features of trajectories. In contrast to existing algorithms, we propose a new framework to cluster sub-trajectories based on a combination of spatial and non-spatial features. In the second part of dissertation, we propose a unified framework to build recommendation systems by analyzing human movement data. We propose recommendation frameworks to recommend POI locations and travel routes that use a combination of spatial, temporal and content features. POI recommendation method aims to provide users with a list of recommendation of POI locations within a geo-spatial range that should match their temporal activities and categorical preferences. In travel route recommendation method, we propose to recommend time-aware and preference-aware travel routes consisting of a sequence POI locations with corresponding time information. This method helps users to plan the entire trip under a specific time constraint. The recommended travel routes tell users where to visit and when to visit. For all the problems, we provide extensive experiments with real world spatio-temporal data available in public domains. The performance evaluation validates the utility and the effectiveness of the proposed methods over baseline approaches.


Spatio-temporal, Recommendation, Spatial clustering


Computer Sciences | Physical Sciences and Mathematics


Degree granted by The University of Texas at Arlington