Gerasimos Marketos

TL;DR: This paper introduces a framework consisting of a set of distance operators based on primitive as well as derived parameters of trajectories (speed and direction) to support trajectory clustering and classification mining tasks, which definitely imply a way to quantify the distance between two trajectories.

TL;DR: This work investigates how the traditional data cube model is adapted to trajectory warehouses in order to transform raw location data into valuable information.

TL;DR: This paper proposes a framework that provides several trajectory similarity measures, based on primitive as well as on derived parameters of trajectories (speed, acceleration, and direction), which quantify the distance between two trajectories and can be exploited for trajectory data mining, including clustering and classification.

TL;DR: This work demonstrates a framework that transforms the traditional data cube model into a trajectory warehouse, T-WAREHOUSE, a system that incorporates all the required steps for Visual Trajectory Data Warehousing, from trajectory reconstruction and ETL processing to Visual OLAP analysis on mobility data.

TL;DR: A novel set of trajectory distance operators based on primitive as well as derived parameters of trajectories (speed and direction) is defined, aimed at providing a powerful toolkit for analysts who require producing distance matrices with different semantics as input to mining tasks.