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Semantic Trajectory Data Mining: a User Driven Approach

Authors: Vania Bogorny and Luis Otavio Alvares

Published in: Dagstuhl Seminar Proceedings, Volume 8471, Geographic Privacy-Aware Knowledge Discovery and Delivery (2009)

Trajectories left behind cars, humans, birds or any other moving object are a new kind of data which can be very useful in decision making process in several application domains. These data, however, are normally available as sample points, and therefore have very little or no semantics. The analysis and knowledge extraction from trajectory sample points is very difficult from the user's point of view, and there is an emerging need for new data models, manipulation techniques, and tools to extract meaningful patterns from these data. In this paper we propose a new methodology for knowledge discovery from trajectories. We propose through a semantic trajectory data mining query language several functionalities to select, preprocess, and transform trajectory sample points into semantic trajectories at higher abstraction levels, in order to allow the user to extract meaningful, understandable, and useful patterns from trajectories. We claim that meaningful patterns can only be extracted from trajectories if the background geographical information is considered. Therefore we build the proposed methodology considering both moving object data and geographic information. The proposed language has been implemented in a toolkit in order to provide a first software prototype for trajectory knowledge discovery.

Cite as

Vania Bogorny and Luis Otavio Alvares. Semantic Trajectory Data Mining: a User Driven Approach. In Geographic Privacy-Aware Knowledge Discovery and Delivery. Dagstuhl Seminar Proceedings, Volume 8471, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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  author =	{Bogorny, Vania and Alvares, Luis Otavio},
  title =	{{Semantic Trajectory Data Mining: a User Driven Approach}},
  booktitle =	{Geographic Privacy-Aware Knowledge Discovery and Delivery},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8471},
  editor =	{Bart Kuijpers and Dino Pedreschi and Yucel Saygin and Stefano Spaccapietra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.08471.3},
  URN =		{urn:nbn:de:0030-drops-20096},
  doi =		{10.4230/DagSemProc.08471.3},
  annote =	{Keywords: Spatio-temporal data mining, trajectory data mining, trajectory sequential patterns, trajectory association rules, trajectory generalization, trajecto}
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