A conceptual view on trajectories

By S. Spaccapietra, C. Parent, M. Damiani, J. Macêdo, F. Porto, C. Vangenot. Data & Knowledge Engineering, 2008

683 citations recorded by the source corpus.

Analysis of trajectory data is the key to a growing number of applications aiming at global understanding and management of complex phenomena that involve moving objects (e.g. worldwide courier distribution, city traffic management, bird migration monitoring). Current DBMS support for such data is limited to the ability to store and query raw movement (i.e. the spatio-temporal position of an object). This paper explores how conceptual modeling could provide applications with direct support of trajectories (i.e. movement data that is structured into countable semantic units) as a first class concept. A specific concern is to allow enriching trajectories with semantic annotations allowing users to attach semantic data to specific parts of the trajectory. Building on a preliminary requirement analysis and an application example, the paper proposes two modeling approaches, one based on a design pattern, the other based on dedicated data types, and illustrates their differences in terms of implementation in an extended-relational context.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c8b715fada36d2b8e7b7e326e32c63bef9ebeac1

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Citations in the corpus

The [Limited] Impact of Weather on Tourist Behavior in an Urban Destination (2015, background): “Research in this field grows out of the increasing availability of spatiotemporal data and the lack of techniques to analyze it (D’Urso and Massari 2013; Nanni and Pedreschi 2006; Spaccapietra et al. 2008).”
A model for enriching trajectories with semantic geographical information (2007, methodology): “In other words, we propose a model to automatically add semantic information to trajectories based on the notion of stops and moves introduced in [15].”
Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis (2015, methodology): “This is a widely used definition of mobility trace (see for example [38]).”
A clustering-based approach for discovering interesting places in trajectories (2008, background): “Several works for trajectory data analcept, trajectories are observed as a set of stops and moves, ysis have been developed speci.cally considering the road where stops are the most important parts of the trajectory. network as the background geographic information [6],[10], Stops and moves…”