Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/9897
Title: ST-DMQL: A Semantic Trajectory Data Mining Query Language
Authors: Bogorny, V.
KUIJPERS, Bart 
ALVARES, Luis Otavio 
Issue Date: 2009
Publisher: TAYLOR & FRANCIS LTD
Source: INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 23(10). p. 1245-1276
Abstract: Mobile devices are becoming very popular in recent years, and large amounts of trajectory data are generated by these devices. Trajectories left behind cars, humans, birds or other objects are a new kind of data which can be very useful in the 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.
Notes: [Bogorny, Vania; Alvares, Luis Otavio] Univ Fed Rio Grande do Sul, Inst Informat, Porto Alegre, RS, Brazil. [Kuijpers, Bart] Hasselt Univ, Dept WNI, Diepenbeek, Belgium.
Keywords: Moving objects; Trajectories; Semantics; Trajectory query language; Trajectory knowledge discovery; Trajectory data mining
Document URI: http://hdl.handle.net/1942/9897
ISSN: 1365-8816
e-ISSN: 1362-3087
DOI: 10.1080/13658810802231449
ISI #: 000270037500002
Category: A1
Type: Journal Contribution
Validations: ecoom 2010
Appears in Collections:Research publications

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