Document details

Knowledge discovery from trajectories

Author(s): Li, Song

Date: 2009

Persistent ID: http://hdl.handle.net/10362/2320

Origin: Repositório Institucional da UNL

Subject(s): Data mining; Elk; Knowledge discovery; Spatio-temporal patterns; Starkey project; Taxonomy; Theoretical framework; Trajectory


Description

As a newly proliferating study area, knowledge discovery from trajectories has attracted more and more researchers from different background. However, there is, until now, no theoretical framework for researchers gaining a systematic view of the researches going on. The complexity of spatial and temporal information along with their combination is producing numerous spatio-temporal patterns. In addition, it is very probable that a pattern may have different definition and mining methodology for researchers from different background, such as Geographic Information Science, Data Mining, Database, and Computational Geometry. How to systematically define these patterns, so that the whole community can make better use of previous research? This paper is trying to tackle with this challenge by three steps. First, the input trajectory data is classified; second, taxonomy of spatio-temporal patterns is developed from data mining point of view; lastly, the spatio-temporal patterns appeared on the previous publications are discussed and put into the theoretical framework. In this way, researchers can easily find needed methodology to mining specific pattern in this framework; also the algorithms needing to be developed can be identified for further research. Under the guidance of this framework, an application to a real data set from Starkey Project is performed. Two questions are answers by applying data mining algorithms. First is where the elks would like to stay in the whole range, and the second is whether there are corridors among these regions of interest.

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Document Type Master thesis
Language English
Advisor(s) Bação, Fernando José Ferreira Lucas; Sánchez, Laura Díaz
Contributor(s) Li, Song
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